Optimal randomized path planning for redundant manipulators based on Memory-Goal-Biasing

Dong Han,Meng Chen,Jinbao Chen,Hong Nie

doi:10.1177/1729881418787049

Abstract

Planning path rapidly and optimally is one of the key technologies for industrial manipulators. A novel method based on Memory-Goal-Biasing–Rapidly-exploring Random Tree is proposed to solve high-dimensional manipulation planning more rapidly and optimally. The tree extension of Memory-Goal-Biasing–Rapidly-exploring Random Tree can be divided into random extension and goal extension. In the goal extension, the nodes extended to the goal are recorded in a memory, and then the node closest to the goal is selected in the search tree excepting the nodes in the memory for overcoming the local minimum. In order to check collisions efficiently, the manipulator is simplified into several key points, and the obstacle area is appropriately enlarged for safety. Taking the redundant manipulator of Baxter robot as an example, the proposed algorithm is verified through MoveIt! software. The results show that Memory-Goal-Biasing–Rapidly-exploring Random Tree only takes a few seconds for the path planning of the redundant manipulator in some complex environments, and within an acceptable time, its optimization performance is better than that of traditional optimal method in terms of the obtained path costs and the corresponding standard deviation.

Highlights

In industrial manufacturing and other domains, collisionfree path planning plays an important role in performing mobile robot navigation[1] and complex manipulator operation.[2]
It is a computational challenge to rapidly find an optimal feasible path for redundant manipulators. In response to this challenge, researchers have presented sampling-based algorithms that are capable of realizing high-dimensional path planning,[3,4,5,6,7,8] such as probabilistic roadmaps, Rapidly-exploring Random Tree (RRT), and
The performance of the MGB-RRT algorithm was compared against Bi-RRT* and Bi-RRT over different planning tasks

Summary

Introduction

In industrial manufacturing and other domains, collisionfree path planning plays an important role in performing mobile robot navigation[1] and complex manipulator operation.[2]. It is a computational challenge to rapidly find an optimal feasible path for redundant manipulators In response to this challenge, researchers have presented sampling-based algorithms that are capable of realizing high-dimensional path planning,[3,4,5,6,7,8] such as probabilistic roadmaps, Rapidly-exploring Random Tree (RRT), and. Some other variants adopt specific optimization methods to lower the path costs and even tend to the optimal path,[10,11,12] but they fall into the local minimum or consume much more time

Results

Discussion

Conclusion