Time-Efficient Mars Exploration of Simultaneous Coverage and Charging with Multiple Drones

TL;DR

This paper introduces TIME-SC2, a comprehensive framework utilizing deep reinforcement learning and optimized scheduling to enhance the time-efficient exploration and charging of multiple drones and a rover on Mars.

Contribution

The paper presents a novel integrated approach combining multi-drone coverage control with a near-optimal charging scheduling algorithm for Mars exploration.

Findings

Demonstrates significant improvements in exploration time efficiency.

Shows high autonomy reduces non-exploring time.

Validates framework's effectiveness through extensive simulations.

Abstract

This paper presents a time-efficient scheme for Mars exploration by the cooperation of multiple drones and a rover. To maximize effective coverage of the Mars surface in the long run, a comprehensive framework has been developed with joint consideration for limited energy, sensor model, communication range and safety radius, which we call TIME-SC2 (TIme-efficient Mars Exploration of Simultaneous Coverage and Charging). First, we propose a multi-drone coverage control algorithm by leveraging emerging deep reinforcement learning and design a novel information map to represent dynamic system states. Second, we propose a near-optimal charging scheduling algorithm to navigate each drone to an individual charging slot, and we have proven that there always exists feasible solutions. The attractiveness of this framework not only resides on its ability to maximize exploration efficiency, but also on its high autonomy that has greatly reduced the non-exploring time. Extensive simulations have been conducted to demonstrate the remarkable performance of TIME-SC2 in terms of time-efficiency, adaptivity and flexibility.