RoboTSP - A Fast Solution to the Robotic Task Sequencing Problem

TL;DR

This paper introduces a fast, near-optimal algorithm for the Robotic Task Sequencing Problem that leverages task and configuration space distinctions, significantly reducing computation time compared to existing methods.

Contribution

The paper presents a novel, efficient algorithm for RTSP that exploits the task and configuration space distinction, along with an open-source implementation for practical use.

Findings

Achieves near-optimal task sequences with much less computation time.

Outperforms existing approaches in speed while maintaining solution quality.

Provides an open-source, benchmarked software tool for RTSP.

Abstract

In many industrial robotics applications, such as spot-welding, spray-painting or drilling, the robot is required to visit successively multiple targets. The robot travel time among the targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the order of visit of the targets, and by the robot configurations used to reach each target. Therefore, it is crucial to optimize these two elements, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a fast, near-optimal, algorithm to solve RTSP. The key to our approach is to exploit the classical distinction between task space and configuration space, which, surprisingly, has been so far overlooked in the RTSP literature. Second, we provide an open-source implementation of the above algorithm, which has been carefully benchmarked to yield an efficient, ready-to-use, software solution. We discuss the relationship between RTSP and other Traveling Salesman Problem (TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and show experimentally that our method finds motion sequences of the same quality but using several orders of magnitude less computation time than existing approaches.