Planning and Control of Multi-Robot-Object Systems under Temporal Logic Tasks and Uncertain Dynamics

TL;DR

This paper presents a novel algorithm for planning and control of multi-robot-object systems under complex temporal logic tasks, effectively handling uncertain dynamics and ensuring safe navigation and task execution in cluttered environments.

Contribution

It introduces an adaptive control-based transition system construction and a sampling-based planning method for multi-robot-object systems with temporal logic constraints under uncertainty.

Findings

Successfully verified through simulation results.

Ensures safe navigation and task satisfaction despite uncertainties.

Efficiently integrates high-level planning with low-level control.

Abstract

We develop an algorithm for the motion and task planning of a system comprised of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Simulation results verify the proposed framework.