Multi-Goal Motion Memory

TL;DR

This paper introduces a Multi-Goal Motion Memory approach that leverages previous planning experiences to significantly speed up multi-goal path planning in dynamic, obstacle-rich environments for autonomous robots.

Contribution

The paper presents a novel memory-based planning method that predicts trajectories and distances to improve efficiency in multi-goal planning tasks.

Findings

Planning speed increased by up to 90%

Solution quality comparable or better than state-of-the-art methods

Effective in obstacle-rich, changing environments

Abstract

Autonomous mobile robots (e.g., warehouse logistics robots) often need to traverse complex, obstacle-rich, and changing environments to reach multiple fixed goals (e.g., warehouse shelves). Traditional motion planners need to calculate the entire multi-goal path from scratch in response to changes in the environment, which result in a large consumption of computing resources. This process is not only time-consuming but also may not meet real-time requirements in application scenarios that require rapid response to environmental changes. In this paper, we provide a novel Multi-Goal Motion Memory technique that allows robots to use previous planning experiences to accelerate future multi-goal planning in changing environments. Specifically, our technique predicts collision-free and dynamically-feasible trajectories and distances between goal pairs to guide the sampling process to build a roadmap, to inform a Traveling Salesman Problem (TSP) solver to compute a tour, and to efficiently produce motion plans. Experiments conducted with a vehicle and a snake-like robot in obstacle-rich environments show that the proposed Motion Memory technique can substantially accelerate planning speed by up to 90\%. Furthermore, the solution quality is comparable to state-of-the-art algorithms and even better in some environments.