Path planning with general end-effector constraints

  • Authors:

  • Zhenwang Yao;Kamal Gupta

  • Affiliations:

  • Robotic Algorithms and Motion Planning Laboratory, School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada;Robotic Algorithms and Motion Planning Laboratory, School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada

  • Venue:
  • Robotics and Autonomous Systems
  • Year:
  • 2007

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Abstract

In this paper, we address the path planning problem with general end-effector constraints (PPGEC) for robot manipulators. Two approaches are proposed. The first approach is the Adapted-RGD method, which is adapted from an existing randomized gradient descent (RGD) method for closed-chain robots. The second approach is radically different. We call it ATACE, Alternate Task-space And Configuration-space Exploration. Unlike the first approach which searches purely in C-space, ATACE works in both task space and C-space. It explores the task space for end-effector paths satisfying given constraints, and utilizes trajectory tracking technique(s) as a local planner(s) to track these paths in the configuration space. We have implemented both approaches and compared their relative performances in different scenarios. ATACE outperforms Adapted-RGD in the majority (but not all) of the scenarios. We outline intuitive explanations for the relative performances of these two approaches.