Optimal Task and Motion Planning for Autonomous Systems Using Petri Nets

TL;DR

This paper presents an optimal task and motion planning method for autonomous systems using Petri nets, combining offline and online strategies to improve efficiency and scalability.

Contribution

It introduces a novel Petri net-based modeling approach and an extended basis reachability graph for efficient, optimal planning in autonomous systems.

Findings

Demonstrates improved computational efficiency.

Shows scalability through simulations.

Validates the approach with practical case studies.

Abstract

This study deals with the problem of task and motion planning of autonomous systems within the context of high-level tasks. Specifically, a task comprises logical requirements (conjunctions, disjunctions, and negations) on the trajectories and final states of agents in certain regions of interest. We propose an optimal planning approach that combines offline computation and online planning. First, a simplified Petri net system is proposed to model the autonomous system. Then, indicating places are designed to implement the logical requirements of the specifications. Building upon this, a compact representation of the state space called extended basis reachability graph is constructed and an efficient online planning algorithm is developed to obtain the optimal plan. It is shown that the most burdensome part of the planning procedure may be removed offline, thanks to the construction of the extended basis reachability graph. Finally, series of simulations are conducted to demonstrate the computational efficiency and scalability of our developed method.