Robust Optimal Task Planning to Maximize Battery Life

TL;DR

This paper introduces a control-oriented optimization platform for autonomous mobile robots that maximizes battery life through robust task planning, using bilinear optimization linearization and a novel algorithm to handle uncertainties efficiently.

Contribution

It presents a new optimization framework with a bilinear problem linearization and a robust planning algorithm for extending robot battery life.

Findings

Simulation shows reduced battery degradation

Efficient task planning under uncertainties

Maximized battery life while ensuring task completion

Abstract

This paper proposes a control-oriented optimization platform for autonomous mobile robots (AMRs), focusing on extending battery life while ensuring task completion. The requirement of fast AMR task planning while maintaining minimum battery state of charge, thus maximizing the battery life, renders a bilinear optimization problem. McCormick envelop technique is proposed to linearize the bilinear term. A novel planning algorithm with relaxed constraints is also developed to handle parameter uncertainties robustly with high efficiency ensured. Simulation results are provided to demonstrate the utility of the proposed methods in reducing battery degradation while satisfying task completion requirements.