Stochastic Collection and Replenishment (SCAR) Optimisation for Persistent Autonomy

TL;DR

This paper optimizes the scheduling of a single replenishment agent in the SCAR scenario for persistent robot autonomy, demonstrating the effectiveness of a ratio objective function and the importance of accounting for uncertainty.

Contribution

It introduces an A*-based optimization approach for SCAR with a focus on objective functions and uncertainty, advancing persistent autonomy strategies.

Findings

Ratio objective function outperforms total weighted tardiness.

Shorter schedules benefit more from the ratio objective.

Accounting for uncertainty improves scheduling robustness.

Abstract

Robots have a finite supply of resources such as fuel, battery charge, and storage space. The aim of the Stochastic Collection and Replenishment (SCAR) scenario is to use dedicated agents to refuel, recharge, or otherwise replenish robots in the field to facilitate persistent autonomy. This paper explores the optimisation of the SCAR scenario with a single replenishment agent, using several different objective functions. The problem is framed as a combinatorial optimisation problem, and A* is used to find the optimal schedule. Through a computational study, a ratio objective function is shown to have superior performance compared with a total weighted tardiness objective function, with a greater performance advantage present when using shorter schedule lengths. The importance of incorporating uncertainty in the objective function used in the optimisation process is also highlighted, in particular for scenarios in which the replenishment agent is under- or fully-utilised.