Resource-Optimal Planning For An Autonomous Planetary Vehicle

TL;DR

This paper demonstrates how the UPMurphi model checking tool can generate resource-optimal plans for autonomous planetary rovers by directly modeling their hybrid systems and safety constraints, improving planning accuracy.

Contribution

It introduces a method to perform resource-optimal offline planning for planetary rovers using model checking on hybrid models, addressing complexity and realism issues.

Findings

Generated accurate resource-optimal plans for rover engines.

Successfully modeled hybrid systems with safety constraints.

Enhanced planning precision over simplified models.

Abstract

Autonomous planetary vehicles, also known as rovers, are small autonomous vehicles equipped with a variety of sensors used to perform exploration and experiments on a planet's surface. Rovers work in a partially unknown environment, with narrow energy/time/movement constraints and, typically, small computational resources that limit the complexity of on-line planning and scheduling, thus they represent a great challenge in the field of autonomous vehicles. Indeed, formal models for such vehicles usually involve hybrid systems with nonlinear dynamics, which are difficult to handle by most of the current planning algorithms and tools. Therefore, when offline planning of the vehicle activities is required, for example for rovers that operate without a continuous Earth supervision, such planning is often performed on simplified models that are not completely realistic. In this paper we show how the UPMurphi model checking based planning tool can be used to generate resource-optimal plans to control the engine of an autonomous planetary vehicle, working directly on its hybrid model and taking into account several safety constraints, thus achieving very accurate results.