A Novel Collision-Free Homotopy Path Planning for Planar Robotic Arms

Abstract

Achieving the smart motion of any autonomous or semi-autonomous robot requires an efficient algorithm to determine a feasible collision-free path. In this paper, a novel collision-free path homotopy-based path-planning algorithm applied to planar robotic arms is presented. The algorithm utilizes homotopy continuation methods (HCMs) to solve the non-linear algebraic equations system (NAES) that models the robot’s workspace. The method was validated with three case studies with robotic arms in different configurations. For the first case, a robot arm with three links must enter a narrow corridor with two obstacles. For the second case, a six-link robot arm with a gripper is required to take an object inside a narrow corridor with two obstacles. For the third case, a twenty-link arm must take an object inside a maze-like environment. These case studies validated, by simulation, the versatility and capacity of the proposed path-planning algorithm. The results show that the CPU time is dozens of milliseconds with a memory consumption less than 4.5 kB for the first two cases. For the third case, the CPU time is around 2.7 s and the memory consumption around 18 kB. Finally, the method’s performance was further validated using the industrial robot arm CRS CataLyst-5 by Thermo Electron.

Repeated Collision of a Planar Robotic Arm with a Surface Using Generalized Active Forces

“…The trajectory of the robots is meticulously planned to avoid any contact. Lopez et al [13] proposed a novel path-planning algorithm for planar robots. However, many multi-body dynamical systems exhibit impact while performing their tasks, including industrial manipulators [14][15][16], walking robots [17][18][19], and space robots [20].…”

A Multilevel Adaptive Path-Planning Model in Off-Road Environments

“…Existing path-planning algorithms are applicable only to specific scenarios, such as container transport at ports [1,2], warehouse logistics [3,4], and obstacle avoidance in robots [5][6][7]. When applied to engineering fields such as agricultural machinery, forest fire protection, traffic, and transportation, the algorithm needs to be designed to find the optimal trafficable path by considering the possibility that there might be no road or only rugged roads amid hills and mountains.…”