Collision avoidance trajectory planning for a dual-robot system: using a modified APF method

Abstract

Dual-robot system has been widely applied to the field of handling and palletizing for its high efficiency and large workspace. It is one of the key problems of the trajectory planning to determine the collision avoidance method of the dual-robot system. In the present study, a collision avoidance trajectory planning method for the dual-robot system was proposed on the basis of a modified artificial potential field (APF) algorithm. The interference and collision criterion of the dual-robot system was given firstly, which was established based on the method of kinematic analysis in robotics. And then, in consideration of the problem of excessive virtual potential field force induced by using the traditional APF algorithm in the process of dual-robot trajectory planning, a modified APF algorithm was proposed. Finally, the modified APF algorithm was used for motion control of a dual-robot palletizing process, and the collision avoidance performance of the proposed collision avoidance algorithm was studied through a dual-robot palletizing simulation and experiment. The results have shown that with the proposed collision avoidance trajectory planning algorithm, the two robots in dual-robot system can maintain a safe distance at all times during palletizing process. Compared with the traditional APF and rapidly-exploring random tree (RRT) algorithm, the trajectory solution time of the modified APF algorithm is greatly reduced. And the modified APF algorithm’s convergence time is 14.2% shorter than that of the traditional APF algorithm.

Design of a Wheelchair-Mounted Robotic Arm for Feeding Assistance of Upper-Limb Impaired Patients

“…Additionally, future work will delve into control aspects, exploring impedance and admittance control for optimal robot-environment interaction, as proposed in works such as [29]. Attention will also be directed towards active learning of collision distance functionality, as suggested in [30] and [31].Furthermore, we aim to explore different end-effectors, such as grippers capable of manipulating not only food but also a wide range of objects, as proposed by Fu et al [32], Dong and Zhang [33], Carbone et al [34], and Yao et al [35]. Their collective work offers valuable insights into control strategies, motion planning, flexible grasping, grasp detection technology, and optimal design of robotic mechanisms, thereby opening multiple avenues for further enhancement of this research.…”

“…Additionally, future work will delve into control aspects, exploring impedance and admittance control for optimal robot-environment interaction, as proposed in works such as [29]. Attention will also be directed towards active learning of collision distance functionality, as suggested in [30,31]. Furthermore, we aim to explore different end-effectors, such as grippers capable of manipulating not only food but also a wide range of objects, as proposed by Fu et al [32], Dong and Zhang [33], Carbone et al [34], and Yao et al [35].…”