Task Space Planning with Complementarity Constraint-based Obstacle Avoidance

TL;DR

This paper introduces a task space local motion planner that uses a novel complementarity constraint-based model to incorporate collision avoidance directly into robot path planning, demonstrated through simulations and physical experiments.

Contribution

The paper presents a new kinematic state evolution model encoding collision avoidance as a Linear Complementarity Problem, enabling direct path computation in task space.

Findings

Effective collision avoidance integrated into task space planning.

Successful implementation on a physical robot demonstrating scalability.

Simulation and experimental results confirm approach efficacy.

Abstract

In this paper, we present a task space-based local motion planner that incorporates collision avoidance and constraints on end-effector motion during the execution of a task. Our key technical contribution is the development of a novel kinematic state evolution model of the robot where the collision avoidance is encoded as a complementarity constraint. We show that the kinematic state evolution with collision avoidance can be represented as a Linear Complementarity Problem (LCP). Using the LCP model along with Screw Linear Interpolation (ScLERP) in SE(3), we show that it may be possible to compute a path between two given task space poses by directly moving from the start to the goal pose, even if there are potential collisions with obstacles. The scalability of the planner is demonstrated with experiments using a physical robot. We present simulation and experimental results with both collision avoidance and task constraints to show the efficacy of our approach.