Path planning for volumetric flask grasping based on visual guidance and multi-constraint optimization

In the automated operation of the chemical laboratory, using a robotic arm to pick up volumetric flasks is a core step of the operation process. By implementing reasonable path planning, the grasping operation of the robotic arm can be made efficient and precise. In this scenario, the traditional Rapidly-exploring Random Tree Star (RRT*) algorithm suffers from low sampling efficiency and numerous sharp path turns. To address these problems, this paper proposes the Vision-guided Multi-constraint RRT* (VM-RRT*) algorithm, which integrates visual guidance sampling and multi-constraint paths. Firstly, the algorithm determines the spatial coordinates of the volumetric flask through target detection, reducing ineffective exploration and accelerating path convergence. Subsequently, it uses cubic B-splines to fit the path and improves the density of data points through spline interpolation methods. Combined with low-pass filtering, it further reduces noise and eliminates sudden changes in the end-effector speed and acceleration of the robotic arm, realizing multi-constraint trajectory optimization. Experimental simulation results show that the average planning time of the VM-RRT* algorithm is 3.27 seconds, which is approximately 20% shorter than that of the traditional RRT* algorithm (4.07 seconds), effectively improving the experimental efficiency. At the same time, the end motion parameters of the robotic arm are effectively controlled, providing support for laboratory automation.