Mobile Recharger Path Planning and Recharge Scheduling in a Multi-Robot Environment

TL;DR

This paper introduces an SMT-based method for optimizing recharger paths and scheduling in multi-robot systems, minimizing worker robot wait times and ensuring energy-efficient recharger trajectories.

Contribution

It presents a novel SMT encoding approach for joint path planning and recharge scheduling in multi-robot environments, balancing efficiency and scalability.

Findings

The SMT-based approach reduces worker wait times effectively.

Compared to a non-scalable optimal method, it offers a scalable near-optimal solution.

The method outperforms greedy algorithms in efficiency and scheduling.

Abstract

In many multi-robot applications, mobile worker robots are often engaged in performing some tasks repetitively by following pre-computed trajectories. As these robots are battery-powered, they need to get recharged at regular intervals. We envision that in the future, a few mobile recharger robots will be employed to supply charge to the energy-deficient worker robots recurrently, to keep the overall efficiency of the system optimized.In this setup, we need to find the time instants and locations for the meeting of the worker robots and recharger robots optimally. We present a Satisfiability Modulo Theory (SMT)-based approach that captures the activities of the robots in the form of constraints in a sufficiently long finite-length time window (hypercycle) whose repetitions provide their perpetual behavior. Our SMT encoding ensures that for a chosen length of the hypercycle, the total waiting time of the worker robots due to charge constraints is minimized under certain condition, and close to optimal when the condition does not hold. Moreover, the recharger robots follow the most energy-efficient trajectories. We show the efficacy of our approach by comparing it with another variant of the SMT-based method which is not scalable but provides an optimal solution globally, and with a greedy algorithm.