Methods for Stochastic Collection and Replenishment (SCAR) optimisation for persistent autonomy

TL;DR

This paper presents a novel stochastic optimization method for resource replenishment in autonomous systems, incorporating uncertainty to improve scheduling and reduce downtime in scenarios like mining and agriculture.

Contribution

It introduces a prediction framework with Gaussian approximations and a branch and bound optimization method for better schedule planning under uncertainty.

Findings

Outperforms existing methods in large scenarios

Reduces downtime of autonomous agents

Provides efficient computation times

Abstract

Consideration of resources such as fuel, battery charge, and storage space, is a crucial requirement for the successful persistent operation of autonomous systems. The Stochastic Collection and Replenishment (SCAR) scenario is motivated by mining and agricultural scenarios where a dedicated replenishment agent transports a resource between a centralised replenishment point to agents using the resource in the field. The agents in the field typically operate within fixed areas (for example, benches in mining applications, and fields or orchards in agricultural scenarios), and the motion of the replenishment agent may be restricted by a road network. Existing research has typically approached the problem of scheduling the actions of the dedicated replenishment agent from a short-term and deterministic angle. This paper introduces a method of incorporating uncertainty in the schedule optimisation through a novel prediction framework, and a branch and bound optimisation method which uses the prediction framework to minimise the downtime of the agents. The prediction framework makes use of several Gaussian approximations to quickly calculate the risk-weighted cost of a schedule. The anytime nature of the branch and bound method is exploited within an MPC-like framework to outperform existing optimisation methods while providing reasonable calculation times in large scenarios.