collision detection ik

add

README.md

@@ -1,5 +1,7 @@
 ### This repo is for inverse kinematics and verification
 
+In this branch, the qp-based inverse kinematics method is modified as a python class. The user can call it as in `main.py`
+
 Inverse Kinematics (IK) is numerically obtained through quadratic programming (QP).
 
 Verification is done with Mujoco simulation.
@@ -9,9 +11,9 @@ Key specifications:
 2. Success rate
 3. Minial joint variation.
 
-Cost:
+Next:\
-
+Comparison with Realman official IK method.
-<img src="img/cost.jpg" alt="Cost" width="400">
+Embedded with current demo.
 
 
 ### Comparison (05June2026):
@@ -24,9 +26,6 @@ lb = np.array([-150.0, -30.0, -170.0, -130, -175.0, -125.0,  -179.0])
 the success rates for **qp-based ik** and **realman Algo ik** are **63%** and **46%**.\
 At least one solver works out the ik, rate = **74%**.
 
-
-
-
 - With realman-75 physical joint limit,
 ```
 ub = np.array([179.0, 129.0, 179.0, 134, 179.0, 127.0, 359.0])
@@ -35,9 +34,15 @@ lb = -ub
 the success rates for **qp-based ik** and **realman Algo ik** are **76%** and **51%**.\
 At least one solver works out the ik, rate = **84%**.
 
+### update(1st July 2026)
 
+In each iteration, update optimization formula:
 
-Visualization:
+- new cost item for distance from middle of the joint range. 
+- set up different weight for different joints motion.
 
+<img src="img/optimization.png" alt="Cost" width="400">
 
-![demo](kine_ctrl/visual/mjc_ik_test1/mjc_ik.gif)
+<img src="img/cons.png" alt="Cost" width="400">
+
+<img src="img/osqp.png" alt="Cost" width="400">

kine_ctrl/fix_robotics_env.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+echo "Fixing robotics environment..."
+conda activate coppeliasim
+export PYTHONPATH="/home/zl/miniforge3/envs/coppeliasim/lib/python3.10/site-packages"
+pip install osqp==0.6.2.post8 --force-reinstall
+python -c "import osqp; print(f'OSQP version: {osqp.__version__}')"

kine_ctrl/main.py

@@ -12,18 +12,53 @@ import time
 from math import radians, degrees, pi, cos, sin
 import numpy as np
 
+#   pose expression of tool-tip in end-effector, x y z quatx quaty quatz quatw
+#   load: kg, mass_center_x in ee frame: m, y, z, then last threes are for filling
+tools_in_ee = {
+    'scissor': np.array([[0.0, 0.0, 0.19, 0.0, 0.0, 0.0, 1.0],[0.66, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0]],dtype=np.float64),
+    'omnipic': np.array([[0.0, 0.0, 0.16, 0.0, 0.0, 0.0, 1.0],[0.43, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0]],dtype=np.float64),
+    'minisci': np.array([[0.0, 0.0, 0.19, 0.0, 0.0, 0.0, 1.0],[0.46, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0]],dtype=np.float64),
+    'no_tool': np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],dtype=np.float64),
+}
+
+# joint limit
+# ub = np.array([150.0, 110.0, 170.0, 130, 175.0, 125.0, 179.0]) / 180 * pi
+# lb = np.array([-150.0, -30.0, -170.0, -130, -175.0, -125.0, -179.0]) / 180 * pi
+
+
+ub = np.array([179.0, 129.0, 179.0, 134, 179.0, 127.0, 359.0])/180*pi
+lb = -ub
+
+tool_name = "scissor"
+
 def main():
     """Demonstrate pure position control"""
 
     # Create controller
     robot_mjk = MuJoCoPositionController()
 
-    tool_name = "scissor"
+
     #  -----------      rm75 qp based kine          ------------
-    robot_kine_qp = kine_qp()
+    robot_kine_qp = kine_qp(urdf_path='/home/zl/Downloads/urdf_rm75/RM75-B.urdf', mesh_dir='/home/zl/Downloads/urdf_rm75')
+    robot_kine_qp.add_tool_frames(tools_in_ee)
+    robot_kine_qp.cfg_j_limit(min_j=lb, max_j=ub, rad_flag=True)
 
     #   ----------      rm75 official algorithm         -----------
     robot_kine_rm = kine_rm()
+    robot_kine_rm.add_tool_frames(tools_in_ee)
+    robot_kine_rm.cfg_j_limit(min_j=lb, max_j=ub, rad_flag=True)
+
+    ret_rm, q = robot_kine_rm.inverse_kinematics(target_position=[-0.6, -0.6 , 0. ], target_rpy=[1.2022060487764064, -1.0097962261845583, -0.6518417572686532],
+                                                 initial_guess=[0.1] * 7, tool="no_tool")
+
+    print(f'ret_rm = {ret_rm}, q = {q}')
+    pose = robot_kine_rm.forward_kinematics(joint_angles=q, tool="no_tool")
+    print(f'pose = {pose}')
+    print('-'*100)
+
+    time.sleep(5)
+
+
 
 
 
@@ -32,16 +67,6 @@ def main():
 
     if True:
 
-        # ub = np.array([150.0, 110.0, 170.0,  130, 175.0, 125.0,  179.0])
-        # lb = np.array([-150.0, -30.0, -170.0, -130, -175.0, -125.0,  -179.0])
-
-        ub = np.array([179.0, 129.0, 179.0, 134, 179.0, 127.0, 359.0])/180*pi
-        lb = -ub
-
-        robot_kine_qp.cfg_j_limit(min_j=lb, max_j=ub, rad_flag=True)
-
-        robot_kine_rm.cfg_j_limit(min_j=lb, max_j=ub, rad_flag=True)
-
         result = np.array([[0,0],[0,0]], dtype=np.int32)        # to collect ik result   qp_fk, qp_ik, rm_fk, rm_ik
 
         solve_sum = 0
@@ -70,32 +95,33 @@ def main():
             if ret_qp == 0:
                 fk_qp_p2 = robot_kine_qp.forward_kinematics(q, tool=tool_name)
                 d_p_ik = cal_pose_deviation(pose1=t_p, pose2=fk_qp_p2)
-                print(f'-- success, in the qp ik, fk_qp_p2 = {fk_qp_p2}, d_p_ik = {d_p_ik}')
+                print(f'---- success, in the qp ik, fk_qp_p2 = {fk_qp_p2}, d_p_ik = {d_p_ik}')
+                # robot_kine_qp.collision_detect(q,stop_at_first_collision=True, verbose=True)
                 if d_p_ik < 0.01:
                     result[0][1] += 1
 
-                robot_mjk.send_command(q)
+                # robot_mjk.send_command(q)
-                robot_mjk.wait_until_reached()
+                # robot_mjk.wait_until_reached()
-                robot_mjk.print_state()
+                # robot_mjk.print_state()
             else:
                 fk_qp_p2 = robot_kine_qp.forward_kinematics(q, tool=tool_name)
                 d_p_ik = cal_pose_deviation(pose1=t_p, pose2=fk_qp_p2)
-                print(f'-- fail, in the qp ik, fk_qp_p2 = {fk_qp_p2}, d_p_ik = {d_p_ik},q = {q}, ret_qp = {ret_qp}')
+                print(f'---- fail, in the qp ik, fk_qp_p2 = {fk_qp_p2}, d_p_ik = {d_p_ik},q = {q}, ret_qp = {ret_qp}')
 
             ret_rm, q = robot_kine_rm.inverse_kinematics(target_position=t_p[0:3], target_rpy=t_p[3:6], initial_guess=joint_rand_init, tool=tool_name)
             if ret_rm == 0:
                 fk_rm_p2 = robot_kine_rm.forward_kinematics(joint_angles=q, tool=tool_name)
                 d_p_ik = cal_pose_deviation(pose1=t_p, pose2=fk_rm_p2)
-                print(f'== sucess, in the rm ik, fk_rm_p2 = {fk_rm_p2}, d_p_ik = {d_p_ik} ,q = {q}, ret_qp = {ret_qp}')
+                print(f'==== sucess, in the rm ik, fk_rm_p2 = {fk_rm_p2}, d_p_ik = {d_p_ik} ,q = {q}, ret_qp = {ret_rm}')
                 if d_p_ik < 0.01:
                     result[1][1] += 1
             else:
-                print(f'== fail in the rm ik, ret = {ret_rm}, q = {q}')
+                print(f'==== fail in the rm ik, ret = {ret_rm}, q = {q}')
 
             if ret_qp == 0 or ret_rm == 0:
                 solve_sum += 1
 
-        print(f'result is {result}')
+        print(f'results with qp and rm for ik are {result}')
         print(f'solve_sum is {solve_sum}')
 
 

kine_ctrl/req.txt

@@ -0,0 +1,3 @@
+conda install -c conda-forge osqp scipy tqdm matplotlib pandas "numpy<1.24" pinocchio -y
+pip install urdfpy mujoco "networkx>=2.8.4"
+pip install Robotic_Arm

kine_ctrl/requirements.txt

@@ -0,0 +1,9 @@
+numpy
+pandas
+matplotlib
+tqdm
+scipy
+urdfpy
+pin
+osqp
+Robotic_Arm

kine_ctrl/rm75_kine_qp.py

@@ -8,6 +8,7 @@ import osqp
 from scipy import sparse
 from math import radians, degrees, pi, cos, sin
 import time
+import threading
 
 
 
@@ -19,79 +20,25 @@ class KinematicsSolver():
         unit: m, rad
         """
         print(f' ------------  the qp based kinematic initialising   -----------')
-        self.model, collision_model, visual_model = pin.buildModelsFromUrdf(urdf_path, mesh_dir)
+        self.model, self.collision_model, visual_model = pin.buildModelsFromUrdf(urdf_path, mesh_dir)
 
-        # -------------------------------------------------
+        self.geom_model = pin.buildGeomFromUrdf(self.model, urdf_path, pin.GeometryType.COLLISION, mesh_dir)
-        # ee
+        self.geom_model.addAllCollisionPairs()
-        # -------------------------------------------------
+        self.remove_adjacent_collision_pairs(verbose=True)
-        ee_offset = pin.SE3(np.eye(3), np.array([0, 0, 0.0]))
+        self.geom_data = pin.GeometryData(self.geom_model)
-        self.model.addFrame(
-            pin.Frame(
-                "ee",
-                self.model.getJointId("joint_7"),
-                self.model.getFrameId("link_7"),
-                ee_offset,
-                pin.FrameType.OP_FRAME
-            )
-        )
 
 
-        # -------------------------------------------------
-        # Scissor tool
-        # -------------------------------------------------
-        scissor_offset = pin.SE3(
-            np.eye(3),
-            np.array([0.0, 0.0, 0.144])
-        )
-        self.model.addFrame(
-            pin.Frame(
-                "scissor",
-                self.model.getJointId("joint_7"),
-                self.model.getFrameId("link_7"),
-                scissor_offset,
-                pin.FrameType.OP_FRAME
-            )
-        )
-
-        # -------------------------------------------------
-        # Camera tool
-        # -------------------------------------------------
-        camera_rotation = pin.rpy.rpyToMatrix(
-            radians(-90),
-            0,
-            radians(-90)
-        )
-        camera_offset = pin.SE3(
-            camera_rotation,
-            np.array([0.05, 0.02, 0.10])
-        )
-        self.model.addFrame(
-            pin.Frame(
-                "camera",
-                self.model.getJointId("joint_7"),
-                self.model.getFrameId("link_7"),
-                camera_offset,
-                pin.FrameType.OP_FRAME
-            )
-        )
-
-        # -------------------------------------------------
-        # Store tool frame IDs
-        # -------------------------------------------------
-
-        self.tool_frames = {
-            "scissor": self.model.getFrameId("scissor"),
-            "camera": self.model.getFrameId("camera"),
-            "ee":   self.model.getFrameId("ee")
-        }
-
-
-
-
-        self.data = self.model.createData()
-
         self.cfg_j_limit()
 
+        q_range = (
+                self.model.upperPositionLimit[:7] -
+                self.model.lowerPositionLimit[:7]
+        )
+
+        self.w_q_limit = np.diag(1.0 / (q_range ** 2))
+
+        self.q_mid = 0.5 * (self.model.lowerPositionLimit[:7] + self.model.upperPositionLimit[:7])
+
         # ----------  for reused qp_solver  ------------------
         self.nv = 7
 
@@ -117,6 +64,36 @@ class KinematicsSolver():
         )
 
         self.W = np.diag([1, 1, 1, 0.4, 0.4, 0.4])
+        # Smaller value => joint moves more actively
+        # Larger value  => joint moves less / more lazy
+        self.joint_motion_weight = np.diag([
+            1.0, 1.0, 1.0, 1.0,
+            0.3, 0.3, 0.2
+        ])
+
+    def add_frame(self,frame_name, position, rotationXYZ):
+        '''
+        :param frame_name:  str
+        :param position: [x, y, z] target position (meters)
+        :param rotationXYZ: [x, y, z] target rotation (rad)
+        '''
+        camera_rotation = pin.rpy.rpyToMatrix( rotationXYZ[0],  rotationXYZ[1],  rotationXYZ[2] )
+        camera_offset = pin.SE3(
+            camera_rotation,
+            np.array(position)
+        )
+        self.model.addFrame(  pin.Frame(  frame_name, self.model.getJointId("joint_7"), self.model.getFrameId("link_7"), camera_offset, pin.FrameType.OP_FRAME ) )
+
+    def add_tool_frames(self,dict_frames):
+        self.tool_frames ={}
+        for tool_name in dict_frames:
+            tool_attr = dict_frames[tool_name]
+            position = tool_attr[0][0:3]
+            rotationXYZ = self.quaternion_to_euler(tool_attr[0][3:7])
+            self.add_frame(tool_name, position, rotationXYZ)
+            self.tool_frames.update({tool_name: self.model.getFrameId(tool_name)})
+        self.data = self.model.createData()
+
 
     def cfg_j_limit(self, min_j=None, max_j=None, rad_flag = True):
         if min_j is None:
@@ -132,7 +109,7 @@ class KinematicsSolver():
                 self.model.lowerPositionLimit[i] = min_j[i] / 180 * pi
                 self.model.upperPositionLimit[i] = max_j[i] / 180 * pi
 
-    def forward_kinematics(self, joint_angles, tool="ee"):
+    def forward_kinematics(self, joint_angles, tool="omnipic"):
         """
         Compute forward kinematics.
 
@@ -201,8 +178,7 @@ class KinematicsSolver():
         """
         # Build target SE3 placement
         if target_quat is not None:
-            quat = pin.Quaternion(target_quat[3], target_quat[0],
+            quat = pin.Quaternion(target_quat[3], target_quat[0], target_quat[1], target_quat[2])
-                                  target_quat[1], target_quat[2])
             target_rotation = quat.matrix()
         elif target_rpy is not None:
             target_rotation = pin.rpy.rpyToMatrix(target_rpy[0],
@@ -298,28 +274,28 @@ class KinematicsSolver():
                 # =========================
                 # QP-based IK
                 # =========================
-                w_posture = 0.0001
+                w_ref = 0.0001
+                w_limit_mid = 0.00002
 
                 J_eff = pin.Jlog6(error_SE3) @ J #J #
 
                 H = J_eff.T @ self.W @ J_eff
 
 
-                # H = J.T @ self.W @ J
+                H += damping * damping * self.joint_motion_weight
-                H += damping * damping * np.eye(7)
+                H += w_ref * np.eye(7)
-                H += w_posture * np.eye(7)
+                H += w_limit_mid * self.w_q_limit
 
                 H_triu = sparse.triu(H).tocsc()
 
                 g = -J_eff.T @ self.W @ error_vec
-                g += w_posture * (q[:7] - q_ref[:7])
+                g += w_ref * (q[:7] - q_ref[:7])
-                # g = - J.T @ self.W @ error_vec
+                g += w_limit_mid * self.w_q_limit @ (q[:7] - self.q_mid)
 
                 # -------------------------
                 # Joint velocity constraints
                 # -------------------------
-
+                dq_limit =  np.array([ 0.05, 0.05, 0.05, 0.05, 0.08, 0.08, 0.10 ])  # rad per iteration
-                dq_limit = 0.05  # rad per iteration
 
                 lb = -dq_limit * np.ones(7)
                 ub = dq_limit * np.ones(7)
@@ -363,11 +339,128 @@ class KinematicsSolver():
                 iter_count += 1
 
         if best_solution is not None:
+            collision = self.collision_detect(q=best_solution, stop_at_first_collision=True)
+
+            if collision is False:
                 # return best_solution, True, best_error, iter_count
-            return 0, best_solution
+                return 0, best_solution.tolist()
+            else:
+                return -2, q[:7].copy().tolist()
         else:
             # return q[:7].copy(), False, error_norm, iter_count
-            return -1, q[:7].copy()
+            return -1, q[:7].copy().tolist()
+
+    def collision_detect(self, q ,stop_at_first_collision=True, verbose=False ):
+        q = np.asarray(q, dtype=np.float64).reshape(-1)
+
+        if q.shape[0] != self.model.nq:
+            raise ValueError(f"q size mismatch: expected {self.model.nq}, got {q.shape[0]}")
+
+        # Update robot kinematics
+        pin.forwardKinematics(self.model, self.data, q)
+        pin.updateGeometryPlacements(
+            self.model,
+            self.data,
+            self.geom_model,
+            self.geom_data,
+            q
+        )
+
+        # Now compute collisions on the updated geometry model
+        collision = pin.computeCollisions(
+            self.geom_model,
+            self.geom_data,
+            stop_at_first_collision
+        )
+
+        if verbose:
+            print(f"the collision is {collision}\n")
+
+            for k, cr in enumerate(self.geom_data.collisionResults):
+                if cr.isCollision():
+                    cp = self.geom_model.collisionPairs[k]
+                    geom1 = self.geom_model.geometryObjects[cp.first]
+                    geom2 = self.geom_model.geometryObjects[cp.second]
+
+                    print(
+                        f"collision pair {k}: "
+                        f"{geom1.name} <--> {geom2.name}"
+                    )
+
+        return bool(collision)
+
+    def remove_adjacent_collision_pairs(self, verbose=True):
+        """
+        Remove collision pairs between same/adjacent parent joints.
+
+        This avoids false positives such as:
+            base_link_0 <--> link_1_0
+        """
+
+        pairs_to_remove = []
+
+        for pair_id, pair in enumerate(self.geom_model.collisionPairs):
+            geom1 = self.geom_model.geometryObjects[pair.first]
+            geom2 = self.geom_model.geometryObjects[pair.second]
+
+            j1 = geom1.parentJoint
+            j2 = geom2.parentJoint
+
+            # Same body or directly connected bodies
+            if j1 == j2 or abs(j1 - j2) <= 1:
+                pairs_to_remove.append(pair_id)
+
+                if verbose:
+                    print(
+                        "Removing adjacent pair:",
+                        pair_id,
+                        geom1.name,
+                        "<-->",
+                        geom2.name,
+                        "parentJoint:",
+                        j1,
+                        j2,
+                    )
+
+        for pair_id in reversed(pairs_to_remove):
+            self.geom_model.removeCollisionPair(
+                self.geom_model.collisionPairs[pair_id]
+            )
+
+        # Important: recreate geometry data after modifying pairs
+        self.geom_data = pin.GeometryData(self.geom_model)
+
+        if verbose:
+            print("Remaining collision pairs:", len(self.geom_model.collisionPairs))
+
+    def quaternion_to_euler(self, q):
+        """
+        Convert quaternion to Euler angles (roll, pitch, yaw)
+
+        Args:
+            qx, qy, qz, qw: quaternion components
+
+        Returns:
+            tuple: (roll, pitch, yaw) in radians
+        """
+        # Roll (x-axis rotation)
+        sinr_cosp = 2.0 * (q[3] * q[0] + q[1] * q[2])
+        cosr_cosp = 1.0 - 2.0 * (q[0] * q[0] + q[1] * q[1])
+        roll = np.arctan2(sinr_cosp, cosr_cosp)
+
+        # Pitch (y-axis rotation)
+        sinp = 2.0 * (q[3] * q[1] - q[2] * q[0])
+        if abs(sinp) >= 1:
+            pitch = np.copysign(np.pi / 2, sinp)  # Use 90 degrees if out of range
+        else:
+            pitch = np.arcsin(sinp)
+
+        # Yaw (z-axis rotation)
+        siny_cosp = 2.0 * (q[3] * q[2] + q[0] * q[1])
+        cosy_cosp = 1.0 - 2.0 * (q[1] * q[1] + q[2] * q[2])
+        yaw = np.arctan2(siny_cosp, cosy_cosp)
+
+        return [roll, pitch, yaw]
 
     # def invese_kinematics_velocity(self, target_position, target_rpy=None,
     #                        target_quat=None, initial_guess=None, tool="ee"):

kine_ctrl/rm75_kine_rm.py

@@ -7,23 +7,18 @@ class rm75_kine_api():
     def __init__(self):
         #   ----------      rm75 official algorithm         -----------
         print(f'-------  the realman official kinematic initialising -------')
-        arm_model = rm_robot_arm_model_e.RM_MODEL_RM_75_E  # RM_65 Robotic arm
+        arm_model = rm_robot_arm_model_e.RM_MODEL_RM_75_E  # RM_75 Robotic arm
         force_type = rm_force_type_e.RM_MODEL_RM_B_E  # Standard version
         # Initialize the robotic arm model and sensor type in the algorithm
         self.robot_kine_rm = Algo(arm_model, force_type)
 
         self.cfg_j_limit()
 
-        self.tool_frames = {
-            'ee': rm_frame_t(frame_name="ee", pose=(0.0, 0.0, 0.0, 0.0, 0, 0.0), payload=1, x=0, y=0, z=0),
-            'scissor': rm_frame_t(frame_name="scissor", pose=(0.0, 0.0, 0.144, 0.0, 0, 0.0), payload=1, x=0, y=0, z=72),
-            'camera': rm_frame_t(frame_name="camera", pose=(0.05, 0.02, 0.10, -1.57, 0, -1.57), payload=1, x=0, y=0, z=72)
-        }
         self.work_frames = {
-            'work': rm_frame_t(frame_name="ee", pose=(0.0, 0.0, 0.0, 0.0, 0, 0.0), payload=1, x=0, y=0, z=0),
+            'work': rm_frame_t(frame_name="work", pose=(0.0, 0.0, 0.0, 0.0, 0, 0.0), payload=1, x=0, y=0, z=0),
         }
 
-        self.tool_name = "ee"
+        self.tool_name = "no_tool"
         self.work_name = "work"
 
     def cfg_j_limit(self, min_j=None, max_j=None, rad_flag = True):
@@ -32,6 +27,8 @@ class rm75_kine_api():
         if min_j is None:
             min_j = np.array([ -3.14159, -2.2689, -3.14159, -2.3562, -3.14159, -2.234, -3.14159  ])
 
+        max_j = np.array(max_j)
+        min_j = np.array(min_j)
         if rad_flag:
             self.robot_kine_rm.rm_algo_set_joint_max_limit((max_j * 180 / math.pi).tolist())
             self.robot_kine_rm.rm_algo_set_joint_min_limit((min_j * 180 / math.pi).tolist())
@@ -51,7 +48,46 @@ class rm75_kine_api():
     def get_tool_frame(self):
         return self.robot_kine_rm.rm_algo_get_curr_toolframe()
 
-    def forward_kinematics(self, joint_angles, flag  = 1  , tool="ee", work="work"):
+    def quaternion_to_euler(self, q):
+        """
+        Convert quaternion to Euler angles (roll, pitch, yaw)
+
+        Args:
+            qx, qy, qz, qw: quaternion components
+
+        Returns:
+            tuple: (roll, pitch, yaw) in radians
+        """
+        # Roll (x-axis rotation)
+        sinr_cosp = 2.0 * (q[3] * q[0] + q[1] * q[2])
+        cosr_cosp = 1.0 - 2.0 * (q[0] * q[0] + q[1] * q[1])
+        roll = np.arctan2(sinr_cosp, cosr_cosp)
+
+        # Pitch (y-axis rotation)
+        sinp = 2.0 * (q[3] * q[1] - q[2] * q[0])
+        if abs(sinp) >= 1:
+            pitch = np.copysign(np.pi / 2, sinp)  # Use 90 degrees if out of range
+        else:
+            pitch = np.arcsin(sinp)
+
+        # Yaw (z-axis rotation)
+        siny_cosp = 2.0 * (q[3] * q[2] + q[0] * q[1])
+        cosy_cosp = 1.0 - 2.0 * (q[1] * q[1] + q[2] * q[2])
+        yaw = np.arctan2(siny_cosp, cosy_cosp)
+
+        return [roll, pitch, yaw]
+
+    def add_tool_frames(self, dict_frames):
+        self.tool_frames = {}
+        for tool_name in dict_frames:
+            tool_attr = dict_frames[tool_name]
+            position = tool_attr[0][0:3]
+            rotationXYZ = self.quaternion_to_euler(tool_attr[0][3:7])
+            f = rm_frame_t(frame_name=tool_name, pose=(position[0], position[1], position[2], rotationXYZ[0], rotationXYZ[1], rotationXYZ[2]), payload=1, x=0, y=0, z=0)
+
+            self.tool_frames.update({tool_name:f})
+
+    def forward_kinematics(self, joint_angles, flag  = 1  , tool="omnipic", work="work"):
         '''
         :param joint_angles:   list of joint values, in rad
         :param flag:       0: return list [x,y,z,w,x,y,z]. 1: return list [x,y,z,rx,ry,rz]
@@ -66,7 +102,7 @@ class rm75_kine_api():
 
         return self.robot_kine_rm.rm_algo_forward_kinematics(joint=[q_s*180/math.pi for q_s in joint_angles]  , flag=flag)
 
-    def inverse_kinematics(self, target_position, target_rpy=None, initial_guess=None, tool="ee", work="work"):
+    def inverse_kinematics(self, target_position, target_rpy=None, initial_guess=None, tool="omnipic", work="work"):
         '''
         :param target_position:     list of position values, m
         :param target_rpy:          list of rpy values, rad
@@ -84,14 +120,37 @@ class rm75_kine_api():
             self.work_name = work
             self.cfg_work_frame(work)
 
-        target = target_position + target_rpy
+        target = list(target_position) + list(target_rpy)
 
         if initial_guess is not None:
             q_ref = [ 180/math.pi * ig for ig in  initial_guess ]
         else:
             q_ref = [0.0, 110.0, 20.0, 40.0, 30.0, 180.0, 20.0]
         ret, phi = self.robot_kine_rm.rm_algo_calculate_arm_angle_from_config_rm75(q_ref)
+        # print(f'the arm angle is ret = {ret}, and phi = {phi}')
         params = rm_inverse_kinematics_params_t(q_ref,
                                                 target, 1)
         ret, q_out = self.robot_kine_rm.rm_algo_inverse_kinematics_rm75_for_arm_angle(params, phi)
+        pose_fk = self.robot_kine_rm.rm_algo_forward_kinematics(joint=q_out, flag=1)
+        pose_dis = cal_pose_deviation(pose_fk, target)
+
+        # print(f'target pose is {target}, fk pose is {pose_fk}, dis of poses is {pose_dis}')
+        #
+        # print(f'\nin the rm75_kine_rm, l133, inverse_kinematics, q_ref = {q_ref}, target = {target}  phi = {phi}, q_out = {q_out}, ret = {ret}\n\n')
+        # print(f'the tool frame is {self.robot_kine_rm.rm_algo_get_curr_toolframe()}')
+        if int(ret) < 0:
             return ret, [ q/180*math.pi for q in q_out]
+        elif pose_dis < 0.01:
+            return ret, [ q/180*math.pi for q in q_out]
+        else:
+            return -10, [ q/180*math.pi for q in q_out]
+
+def cal_pose_deviation(pose1, pose2):
+    d_fk_p1 = np.array(pose1) - np.array(pose2)
+    for j in [3, 4, 5]:
+        while d_fk_p1[j] > math.pi:
+            d_fk_p1[j] -= 2 * math.pi
+        while d_fk_p1[j] < -math.pi:
+            d_fk_p1[j] += 2 * math.pi
+    d_fk = np.linalg.norm(d_fk_p1)
+    return d_fk

kine_ctrl/workspace_comfortable/Contour_plot.py

@@ -0,0 +1,80 @@
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+# --------------------------------------------------
+# 1. Load the data
+# --------------------------------------------------
+csv_path = Path("workspace_nocollisiondetection.csv")
+
+# The file has no column names, so header=None is important.
+df = pd.read_csv(
+    csv_path,
+    header=None,
+    names=["x", "y", "z", "ik_rate"],
+)
+
+print(df.head())
+print("z planes:", np.sort(df["z"].unique()))
+
+
+# --------------------------------------------------
+# 2. Create an output directory
+# --------------------------------------------------
+output_dir = Path("contour_plots")
+output_dir.mkdir(exist_ok=True)
+
+
+# --------------------------------------------------
+# 3. Use the same colour scale for every z-plane
+# --------------------------------------------------
+value_min = df["ik_rate"].min()
+value_max = df["ik_rate"].max()
+
+# More levels give a smoother-looking contour plot.
+levels = np.linspace(value_min, value_max, 51)
+
+
+# --------------------------------------------------
+# 4. Draw one contour plot for each z-plane
+# --------------------------------------------------
+for z_value, plane in df.groupby("z", sort=True):
+
+    # Rows become y-coordinates, columns become x-coordinates.
+    grid = plane.pivot(index="y", columns="x", values="ik_rate")
+
+    x = grid.columns.to_numpy()
+    y = grid.index.to_numpy()
+    ik_grid = grid.to_numpy()
+
+    X, Y = np.meshgrid(x, y)
+
+    fig, ax = plt.subplots(figsize=(7, 6))
+
+    contour = ax.contourf(
+        X,
+        Y,
+        ik_grid,
+        levels=levels,
+        cmap="viridis",
+        extend="both",
+    )
+
+    colorbar = fig.colorbar(contour, ax=ax)
+    colorbar.set_label("IK rate")
+
+    ax.set_title(f"IK rate at z = {z_value:.2f}")
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+    ax.set_aspect("equal")
+
+    fig.tight_layout()
+
+    output_path = output_dir / f"ik_contour_z_{z_value:.2f}.png"
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+print(f"Plots saved to: {output_dir.resolve()}")

kine_ctrl/workspace_comfortable/workspace_cal.py

@@ -0,0 +1,750 @@
+"""
+RM75-B comfortable workspace evaluator.
+
+You provide:
+    - URDF file path '/home/zl/Downloads/urdf_rm75/RM75-B.urdf'
+    - your own IK solver inside solve_ik_user()
+
+This script computes:
+    - IK success rate
+    - joint-limit comfort
+    - manipulability
+    - singularity / condition number score
+    - final comfort score
+
+Recommended install:
+    pip install numpy scipy urdfpy pandas matplotlib tqdm
+
+Optional:
+    pip install plotly
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from tqdm import tqdm
+from scipy.spatial.transform import Rotation as R
+from urdfpy import URDF
+
+import sys
+from pathlib import Path
+
+# 1. Get the absolute path of the directory containing this current script
+current_dir = Path(__file__).resolve().parent
+
+# 2. Get the parent (upper) directory
+parent_dir = current_dir.parent
+
+# 3. Add the parent directory to the system path
+sys.path.insert(0, str(parent_dir))
+
+from rm75_kine_qp import KinematicsSolver as kine_qp
+from rm75_kine_rm import rm75_kine_api as kine_rm
+from rm75_mjc import MuJoCoPositionController
+from Robotic_Arm.rm_robot_interface import *
+
+import time
+from math import radians, degrees, pi, cos, sin
+
+# Cartesian workspace grid, in meters.
+# Adjust according to your robot placement and task.
+X_RANGE = (-0.6, 0.6)
+Y_RANGE = (-0.6, 0.6)
+Z_RANGE = (0.0, 0.8)
+
+GRID_RESOLUTION = 0.05  # 5 cm. Use 0.02 for finer but slower.
+
+num_orientations =  120 
+
+# Comfort thresholds
+MIN_JOINT_MARGIN = 0.05       # 15% away from joint limits
+MAX_CONDITION_NUMBER = 150.0
+MIN_MANIPULABILITY_RATIO = 0.10
+
+# Scoring weights
+WEIGHT_IK_SUCCESS = 0.70
+WEIGHT_JOINT_LIMIT = 0.10
+WEIGHT_MANIPULABILITY = 0.1
+WEIGHT_SINGULARITY = 0.1
+
+
+
+
+#   pose expression of tool-tip in end-effector, x y z quatx quaty quatz quatw
+#   load: kg, mass_center_x in ee frame: m, y, z, then last threes are for filling
+tools_in_ee = {
+    'scissor': np.array([[0.0, 0.0, 0.19, 0.0, 0.0, 0.0, 1.0],[0.66, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0]],dtype=np.float64),
+    'omnipic': np.array([[0.0, 0.0, 0.16, 0.0, 0.0, 0.0, 1.0],[0.43, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0]],dtype=np.float64),
+    'minisci': np.array([[0.0, 0.0, 0.19, 0.0, 0.0, 0.0, 1.0],[0.46, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0]],dtype=np.float64),
+    'no_tool': np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],dtype=np.float64),
+}
+
+# joint limit
+# ub = np.array([150.0, 110.0, 170.0, 130, 175.0, 125.0, 179.0]) / 180 * pi
+# lb = np.array([-150.0, -30.0, -170.0, -130, -175.0, -125.0, -179.0]) / 180 * pi
+#
+#
+ub = np.array([179.0, 129.0, 179.0, 134, 179.0, 127.0, 359.0])/180*pi
+lb = -ub
+
+tool_name = "no_tool"
+
+URDF_PATH = str(parent_dir) + '/urdf_rm75/RM75-B.urdf'
+MESH_DIR = str(Path(URDF_PATH).parent)
+
+#  -----------      rm75 qp based kine          ------------
+robot_kine_qp = kine_qp(urdf_path=URDF_PATH, mesh_dir=MESH_DIR)
+robot_kine_qp.add_tool_frames(tools_in_ee)
+robot_kine_qp.cfg_j_limit(min_j=lb, max_j=ub, rad_flag=True)
+
+#   ----------      rm75 official algorithm         -----------
+robot_kine_rm = kine_rm()
+robot_kine_rm.add_tool_frames(tools_in_ee)
+robot_kine_rm.cfg_j_limit(min_j=lb, max_j=ub, rad_flag=True)
+
+
+
+# ============================================================
+# 1. USER SETTINGS
+# ============================================================
+
+
+
+BASE_LINK = "base_link"
+TCP_LINK = "link_7"
+
+JOINT_NAMES = [
+    "joint_1",
+    "joint_2",
+    "joint_3",
+    "joint_4",
+    "joint_5",
+    "joint_6",
+    "joint_7",
+]
+
+
+
+# Numerical Jacobian settings
+JACOBIAN_EPS = 1e-5
+
+
+
+# ============================================================
+# 2. TASK ORIENTATION SAMPLING
+# ============================================================
+
+def make_task_orientations(num_orientations=num_orientations, seed=1):
+    """
+    Random orientation sampling using RM's Euler convention:
+
+        R = Rz @ Ry @ Rx
+
+    Note:
+        This samples Euler angles randomly.
+        It is useful, but not perfectly uniform over SO(3).
+    """
+
+    rng = np.random.default_rng(seed)
+
+    orientations = []
+
+    for _ in range(num_orientations):
+        rx = rng.uniform(-np.pi, np.pi)
+        ry = rng.uniform(-np.pi / 2.0, np.pi / 2.0)
+        rz = rng.uniform(-np.pi, np.pi)
+
+        orientations.append([rx, ry, rz])
+
+    return orientations
+
+#
+# def euler_angles_to_rotation_matrix(rx, ry, rz):
+#     """
+#     Official RM convention:
+#         R = Rz @ Ry @ Rx
+#     This matches scipy:
+#         Rotation.from_euler("xyz", [rx, ry, rz]).as_matrix()
+#     """
+#     Rx = np.array([
+#         [1, 0, 0],
+#         [0, np.cos(rx), -np.sin(rx)],
+#         [0, np.sin(rx),  np.cos(rx)]
+#     ])
+#
+#     Ry = np.array([
+#         [ np.cos(ry), 0, np.sin(ry)],
+#         [0,           1, 0],
+#         [-np.sin(ry), 0, np.cos(ry)]
+#     ])
+#
+#     Rz = np.array([
+#         [np.cos(rz), -np.sin(rz), 0],
+#         [np.sin(rz),  np.cos(rz), 0],
+#         [0,           0,          1]
+#     ])
+#
+#     return Rz @ Ry @ Rx
+
+
+# ============================================================
+# 3. YOUR IK FUNCTION GOES HERE
+# ============================================================
+
+def solve_ik_user(target_position, target_rotation):
+    """
+    Replace this function with your own IK solver.
+
+    Parameters
+    ----------
+    target_position : np.ndarray, shape (3,)
+        Desired TCP position in base_link frame.
+
+    target_rotation : np.ndarray, shape (3, 3)
+        Desired TCP rotation matrix in base_link frame.
+
+    Returns
+    -------
+    None
+        If IK fails.
+
+    or
+
+    np.ndarray, shape (7,)
+        One IK solution.
+
+    or
+
+    list[np.ndarray]
+        Multiple IK solutions.
+
+    Important:
+        Joint order must be:
+        [joint_1, joint_2, joint_3, joint_4, joint_5, joint_6, joint_7]
+    """
+
+    # ========================================================
+    # INSERT YOUR IK CODE HERE
+    # ========================================================
+    initial_guess = [0.1] * 7
+
+    ret_qp, q = robot_kine_qp.inverse_kinematics(target_position=target_position, target_rpy=target_rotation, initial_guess=initial_guess, tool=tool_name, max_iter=250)
+    # print(f'---- with qp ik, ret_qp: {ret_qp}, q = {q}')
+    if ret_qp == 0:
+        return q
+
+
+    ret_rm, q = robot_kine_rm.inverse_kinematics(target_position=target_position, target_rpy=target_rotation, initial_guess=initial_guess, tool=tool_name)
+    # print(f'==== with rm ik, ret_rm: {ret_rm}, q = {q}')
+    if ret_rm == 0:
+        pose_rm = robot_kine_rm.forward_kinematics(joint_angles=q, tool=tool_name)
+        # print(f'target position = {target_position}\ntarget_rpy = {target_rotation} \npose_rm = {pose_rm}')
+        return q
+
+
+    return None
+
+
+# ============================================================
+# 4. URDF / FK UTILITIES
+# ============================================================
+
+def load_robot_and_limits(urdf_path):
+    robot = URDF.load(urdf_path)
+
+    joints = []
+    lower = []
+    upper = []
+
+    joint_map = {j.name: j for j in robot.joints}
+
+    for name in JOINT_NAMES:
+        joint = joint_map[name]
+        joints.append(joint)
+
+        if joint.limit is None:
+            raise ValueError(f"Joint {name} has no limit in URDF.")
+
+        lower.append(joint.limit.lower)
+        upper.append(joint.limit.upper)
+
+    lower = np.asarray(lower, dtype=float)
+    upper = np.asarray(upper, dtype=float)
+
+    return robot, lower, upper
+
+
+# def q_to_cfg(q):
+#     """
+#     Convert joint vector to urdfpy FK config dictionary.
+#     """
+#     return {name: float(q[i]) for i, name in enumerate(JOINT_NAMES)}
+
+
+# def fk_transform(robot, q):
+#     """
+#     Forward kinematics from base_link to TCP_LINK.
+#
+#     Returns
+#     -------
+#     T : np.ndarray, shape (4, 4)
+#     """
+#     cfg = q_to_cfg(q)
+#     fk = robot.link_fk(cfg=cfg)
+#     tcp_link = robot.link_map[TCP_LINK]
+#     return fk[tcp_link]
+#
+#
+# def fk_position(robot, q):
+#     T = fk_transform(robot, q)
+#     return T[:3, 3]
+
+
+# ============================================================
+# 5. COMFORT METRICS
+# ============================================================
+
+def is_within_joint_limits(q, lower, upper, tol=1e-8):
+    q = np.asarray(q)
+    return np.all(q >= lower - tol) and np.all(q <= upper + tol)
+
+
+def joint_limit_score(q, lower, upper):
+    """
+    Score in [0, 1].
+    1 means every joint is at center of its range.
+    0 means at least one joint is at its limit.
+    """
+
+    q = np.asarray(q)
+    mid = 0.5 * (lower + upper)
+    half_range = 0.5 * (upper - lower)
+
+    per_joint_score = 1.0 - np.abs(q - mid) / half_range
+    per_joint_score = np.clip(per_joint_score, 0.0, 1.0)
+
+    # Conservative: one bad joint makes the whole pose less comfortable.
+    return float(np.min(per_joint_score))
+
+
+def joint_margin(q, lower, upper):
+    """
+    Minimum normalized distance to joint limits.
+
+    0.15 means the closest joint is 15% away from its limit.
+    """
+    q = np.asarray(q)
+    margin_lower = (q - lower) / (upper - lower)
+    margin_upper = (upper - q) / (upper - lower)
+    margin = np.minimum(margin_lower, margin_upper)
+    return float(np.min(margin))
+
+
+def q_to_cfg(q):
+    """
+    Convert joint vector to urdfpy FK config dictionary.
+    """
+    return {name: float(q[i]) for i, name in enumerate(JOINT_NAMES)}
+
+
+def fk_transform(robot, q):
+    """
+    Forward kinematics from base_link to TCP_LINK.
+
+    Returns
+    -------
+    T : np.ndarray, shape (4, 4)
+    """
+    cfg = q_to_cfg(q)
+    fk = robot.link_fk(cfg=cfg)
+    tcp_link = robot.link_map[TCP_LINK]
+    return fk[tcp_link]
+
+
+def numerical_geometric_jacobian(robot, q, eps=1e-5):
+    """
+    Numerical 6D geometric-like Jacobian, shape (6, 7).
+
+    Top 3 rows:
+        linear velocity approximation
+
+    Bottom 3 rows:
+        angular velocity approximation as rotation-vector difference
+
+    This is useful for manipulability and singularity checks.
+    """
+    q = np.asarray(q, dtype=float)
+    n = len(q)
+
+    J = np.zeros((6, n))
+
+    T0 = fk_transform(robot, q)
+    p0 = T0[:3, 3]
+    R0 = T0[:3, :3]
+
+    for i in range(n):
+        q_plus = q.copy()
+        q_minus = q.copy()
+
+        q_plus[i] += eps
+        q_minus[i] -= eps
+
+        T_plus = fk_transform(robot, q_plus)
+        T_minus = fk_transform(robot, q_minus)
+
+        p_plus = T_plus[:3, 3]
+        p_minus = T_minus[:3, 3]
+
+        R_plus = T_plus[:3, :3]
+        R_minus = T_minus[:3, :3]
+
+        # Linear part
+        J[:3, i] = (p_plus - p_minus) / (2.0 * eps)
+
+        # Angular part
+        # Relative rotation from minus to plus.
+        dR = R_plus @ R_minus.T
+        rotvec = R.from_matrix(dR).as_rotvec()
+        J[3:, i] = rotvec / (2.0 * eps)
+
+    return J
+
+
+def manipulability_score_from_jacobian(J):
+    """
+    Yoshikawa-style manipulability.
+
+    For a 6x7 Jacobian:
+        w = sqrt(det(J J.T))
+
+    To improve numerical robustness, compute from singular values.
+    """
+    singular_values = np.linalg.svd(J, compute_uv=False)
+
+    # Product of singular values.
+    # For a 6x7 Jacobian, there are 6 singular values.
+    w = float(np.prod(singular_values))
+
+    return w
+
+
+def condition_number_from_jacobian(J, min_sigma=1e-9):
+    singular_values = np.linalg.svd(J, compute_uv=False)
+    sigma_max = np.max(singular_values)
+    sigma_min = np.min(singular_values)
+
+    if sigma_min < min_sigma:
+        return np.inf
+
+    return float(sigma_max / sigma_min)
+
+
+def singularity_score(condition_number):
+    """
+    Score in [0, 1].
+    Higher is better.
+
+    condition_number = 1 is ideal.
+    Very large means near singularity.
+    """
+    if not np.isfinite(condition_number):
+        return 0.0
+
+    return float(1.0 / condition_number)
+
+
+# ============================================================
+# 6. IK RESULT HANDLING
+# ============================================================
+
+def normalize_ik_solutions(ik_result):
+    """
+    Your IK returns:
+        - None if failed
+        - one list/array of 7 joint values if successful
+    """
+    if ik_result is None:
+        return []
+
+    q = np.asarray(ik_result, dtype=float).reshape(-1)
+
+    if q.shape[0] != 7:
+        return []
+
+    return [q]
+
+
+def evaluate_single_solution(robot, q, lower, upper):
+    """
+    Evaluate one IK solution.
+
+    Returns a dictionary with metrics.
+    """
+
+    if q.shape[0] != 7:
+        return None
+
+    if not is_within_joint_limits(q, lower, upper):
+        return None
+
+    jl_score = joint_limit_score(q, lower, upper)
+    jl_margin = joint_margin(q, lower, upper)
+
+    J = numerical_geometric_jacobian(robot, q, eps=JACOBIAN_EPS)
+
+    manip = manipulability_score_from_jacobian(J)
+    cond = condition_number_from_jacobian(J)
+    sing_score = singularity_score(cond)
+
+    valid_by_thresholds = (
+        jl_margin >= MIN_JOINT_MARGIN
+        and cond <= MAX_CONDITION_NUMBER
+    )
+
+    return {
+        "q": q,
+        "joint_limit_score": jl_score,
+        "joint_margin": jl_margin,
+        "manipulability": manip,
+        "condition_number": cond,
+        "singularity_score": sing_score,
+        "valid_by_thresholds": valid_by_thresholds,
+    }
+
+
+# ============================================================
+# 7. MAIN WORKSPACE EVALUATION
+# ============================================================
+
+def make_grid():
+    xs = np.arange(X_RANGE[0], X_RANGE[1] + 1e-9, GRID_RESOLUTION)
+    ys = np.arange(Y_RANGE[0], Y_RANGE[1] + 1e-9, GRID_RESOLUTION)
+    zs = np.arange(Z_RANGE[0], Z_RANGE[1] + 1e-9, GRID_RESOLUTION)
+
+    points = []
+
+    for x in xs:
+        for y in ys:
+            for z in zs:
+                points.append(np.array([x, y, z], dtype=float))
+
+    return points
+
+
+def evaluate_workspace():
+    robot, lower, upper = load_robot_and_limits(URDF_PATH)
+    orientations = make_task_orientations()
+    grid_points = make_grid()
+
+    rows = []
+
+    # First pass stores raw manipulability.
+    # Later we normalize manipulability by max observed value.
+    all_valid_solution_metrics = []
+
+    print(f"Loaded robot from: {URDF_PATH}")
+    print(f"Grid points: {len(grid_points)}")
+    print(f"Orientations per point: {len(orientations)}")
+    print("Evaluating IK reachability and raw metrics...")
+
+    for point in tqdm(grid_points):
+        point_solution_metrics = []
+
+        attempted = 0
+        ik_success_count = 0
+
+
+        for rpy in orientations:
+            attempted += 1
+
+
+            ik_result = solve_ik_user(point, rpy)
+
+            # print(f'\n point is {point}, rpy is {rpy}, and ik result q: {ik_result}')
+            candidate_solutions = normalize_ik_solutions(ik_result)
+
+            if len(candidate_solutions) == 0:
+                continue
+
+            evaluated_solutions = []
+
+            for q in candidate_solutions:
+                # pose = robot_kine_qp.forward_kinematics(joint_angles=q, tool=tool_name)
+                # print(f'the fk of q is {pose}\n')
+                metrics = evaluate_single_solution(robot, q, lower, upper)
+                # print(f'matrics: {metrics}, q = {q}, lower = {lower}, upper = {upper}')
+                if metrics is not None:
+                    evaluated_solutions.append(metrics)
+
+            if len(evaluated_solutions) == 0:
+                continue
+
+            ik_success_count += 1
+
+            # Use the best solution for this pose.
+            # At this stage, manipulability is not normalized,
+            # so use joint score + singularity score as temporary ranking.
+            best = max(
+                evaluated_solutions,
+                key=lambda m: 0.6 * m["joint_limit_score"] + 0.4 * m["singularity_score"]
+            )
+
+            point_solution_metrics.append(best)
+            all_valid_solution_metrics.append(best)
+        print(f'this position+all orientations, the point_solution_metrics = {point_solution_metrics}')
+        ik_success_rate = ik_success_count / attempted if attempted > 0 else 0.0
+
+        if len(point_solution_metrics) == 0:
+            rows.append({
+                "x": point[0],
+                "y": point[1],
+                "z": point[2],
+                "ik_success_rate": 0.0,
+                "joint_limit_score": 0.0,
+                "joint_margin": 0.0,
+                "manipulability": 0.0,
+                "manipulability_score": 0.0,
+                "condition_number": np.inf,
+                "singularity_score": 0.0,
+                "comfort_score": 0.0,
+                "comfortable": False,
+                "reachable": False,
+            })
+        else:
+            # Average over task orientations.
+            rows.append({
+                "x": point[0],
+                "y": point[1],
+                "z": point[2],
+                "ik_success_rate": ik_success_rate,
+                "joint_limit_score": np.mean([m["joint_limit_score"] for m in point_solution_metrics]),
+                "joint_margin": np.mean([m["joint_margin"] for m in point_solution_metrics]),
+                "manipulability": np.mean([m["manipulability"] for m in point_solution_metrics]),
+                "manipulability_score": 0.0,  # filled later
+                "condition_number": np.mean([m["condition_number"] for m in point_solution_metrics]),
+                "singularity_score": np.mean([m["singularity_score"] for m in point_solution_metrics]),
+                "comfort_score": 0.0,  # filled later
+                "comfortable": False,
+                "reachable": True,
+            })
+
+    df = pd.DataFrame(rows)
+
+    # Normalize manipulability by maximum observed value.
+    max_manip = df["manipulability"].replace([np.inf, -np.inf], np.nan).max()
+
+    if max_manip is None or not np.isfinite(max_manip) or max_manip <= 0:
+        max_manip = 1.0
+
+    df["manipulability_score"] = df["manipulability"] / max_manip
+    df["manipulability_score"] = df["manipulability_score"].clip(0.0, 1.0)
+
+    # Final comfort score.
+    df["comfort_score"] = (
+        WEIGHT_IK_SUCCESS * df["ik_success_rate"]
+        + WEIGHT_JOINT_LIMIT * df["joint_limit_score"]
+        + WEIGHT_MANIPULABILITY * df["manipulability_score"]
+        + WEIGHT_SINGULARITY * df["singularity_score"]
+    )
+
+    # Comfortable binary classification.
+    df["comfortable"] = (
+        (df["reachable"] == True)
+        & (df["ik_success_rate"] >= 0.80)
+        & (df["joint_margin"] >= MIN_JOINT_MARGIN)
+        & (df["condition_number"] <= MAX_CONDITION_NUMBER)
+        & (df["manipulability_score"] >= MIN_MANIPULABILITY_RATIO)
+    )
+
+    return df
+
+
+# ============================================================
+# 8. PLOTTING
+# ============================================================
+
+def plot_workspace(df):
+    """
+    3D scatter plot:
+        gray/low = low comfort
+        brighter = higher comfort
+    """
+
+    reachable = df[df["reachable"] == True]
+
+    if len(reachable) == 0:
+        print("No reachable points found. Check your IK function.")
+        return
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection="3d")
+
+    sc = ax.scatter(
+        reachable["x"],
+        reachable["y"],
+        reachable["z"],
+        c=reachable["comfort_score"],
+        s=12,
+        alpha=0.8,
+    )
+
+    ax.set_title("RM75-B Comfortable Workspace")
+    ax.set_xlabel("X [m]")
+    ax.set_ylabel("Y [m]")
+    ax.set_zlabel("Z [m]")
+
+    fig.colorbar(sc, ax=ax, label="Comfort score")
+    plt.show()
+
+
+def plot_comfortable_only(df):
+    comfortable = df[df["comfortable"] == True]
+
+    if len(comfortable) == 0:
+        print("No comfortable points found under current thresholds.")
+        return
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection="3d")
+
+    ax.scatter(
+        comfortable["x"],
+        comfortable["y"],
+        comfortable["z"],
+        c=comfortable["comfort_score"],
+        s=16,
+        alpha=0.9,
+    )
+
+    ax.set_title("RM75-B Comfortable Region Only")
+    ax.set_xlabel("X [m]")
+    ax.set_ylabel("Y [m]")
+    ax.set_zlabel("Z [m]")
+
+    plt.show()
+
+
+# ============================================================
+# 9. ENTRY POINT
+# ============================================================
+
+if __name__ == "__main__":
+    df = evaluate_workspace()
+
+    output_csv = "rm75b_comfort_workspace.csv"
+    df.to_csv(output_csv, index=False)
+
+    print(f"\nSaved result to: {output_csv}")
+
+    print("\nSummary:")
+    print(f"Total grid points:       {len(df)}")
+    print(f"Reachable points:        {df['reachable'].sum()}")
+    print(f"Comfortable points:      {df['comfortable'].sum()}")
+
+    if df["reachable"].sum() > 0:
+        print(f"Max comfort score:       {df['comfort_score'].max():.3f}")
+        print(f"Mean comfort score:      {df[df['reachable']]['comfort_score'].mean():.3f}")
+
+    plot_workspace(df)
+    plot_comfortable_only(df)