I already have a working pipeline that spots ripe blackberries with a YOLOv5 model (custom-trained, weights are ready) and feeds their pixel coordinates to my S0101 robotic arm. What I’m missing is the last, crucial step: turning those detections into a reliable 3-D trajectory so the gripper can move in, grasp, and pick without bumping the plant or losing berries. Everything runs on a custom-built control system driven from Python, so the task is to slot a path-planning layer into code that is largely finished. I can provide the camera–to–world calibration, arm kinematics, and the current scripts that publish joint targets; they just follow a straight-line approach that fails whenever branches get in the way. What I need from you • A Python module (or set of functions) that takes XYZ targets from my vision script and returns collision-free joint trajectories for the S0101 arm. • Clean integration with the existing classes and message format I’ll share, so I can call a single method, e.g., plan_and_execute(target_pose). • Short demonstration video or simulation capture showing the arm picking at least three berries in sequence, plus the source code and any config files. Acceptance criteria 1. Path executes end-to-end on my rig with no branch collisions at nominal picking speed. 2. Berry is grasped within ±5 mm of the visual target. 3. All code is commented and stays inside the current Python environment (no extra languages). I need help with detection of berries using yolov5, after detection the robotic arm should point at the berries and grasp it and move to to another location to drop it in the container and then move back to initial state