Enhancing Heterogeneous Multi-Robot Teaming for Planetary Exploration

Future space missions will include multi-robot systems, with greater autonomy and a large degree of heterogeneity for a wider range of task capabilities and redundancy. It is imperative that both software (learning models, parallelizing capabilities, resource distribution, etc.) and hardware factors must be considered during decentralized task negotiation to lead to better performance of the team. By utilizing the formalism of contextual Markov decision processes, team composition can be incorporated into the learning process and used for more meaningful and reliable evaluation using measures such as total time, overall consumed energy, performance feedback from tasks, or damage incurred. Improved team performance will in turn enhance the overall results of the mission. Planetary exploration tasks often involve time, communication and energy constraints. Such missions are also prone to noisy sensor data (e.g., camera images distorted by dust), as well as wear and tear on hardware (e.g., wheels, manipulators). To ensure that such factors do not jeopardize the mission, they must be taken into account. Therefore, this paper describes a software framework for the reliable execution of tasks in constrained and dynamic environments. Our work leverages the advantages of heterogeneity for more resilient planetary missions by addressing two aspects—first, the integration of hardware parameters into the negotiation process, and second the analysis of how the integration of team performance metrics, particularly adaptability and mutual support, in task negotiation plays a role in the overall mission success.