Traversing Mars: Cooperative Informative Path Planning to Efficiently Navigate Unknown Scenes (2406.05313v2)

Abstract: The ability to traverse an unknown environment is crucial for autonomous robot operations. However, due to the limited sensing capabilities and system constraints, approaching this problem with a single robot agent can be slow, costly, and unsafe. For example, in planetary exploration missions, the wear on the wheels of a rover from abrasive terrain should be minimized at all costs as reparations are infeasible. On the other hand, utilizing a scouting robot such as a micro aerial vehicle (MAV) has the potential to reduce wear and time costs and increasing safety of a follower robot. This work proposes a novel cooperative IPP framework that allows a scout (e.g., an MAV) to efficiently explore the minimum-cost-path for a follower (e.g., a rover) to reach the goal. We derive theoretic guarantees for our algorithm, and prove that the algorithm always terminates, always finds the optimal path if it exists, and terminates early when the found path is shown to be optimal or infeasible. We show in thorough experimental evaluation that the guarantees hold in practice, and that our algorithm is 22.5% quicker to find the optimal path and 15% quicker to terminate compared to existing methods.

• The paper proposes a novel cooperative informative path planning framework where a MAV scout guides a rover follower to find the most cost-effective traversable path in unknown environments using strategic information gain.
• Experimental results show the framework improves optimal path finding speed by 22.5% and decreases termination time by 15%, achieving optimal paths with up to 77% scene coverage.
• This framework enables extended mission durations and maximises scientific yields for rover operations on extraterrestrial terrains by safeguarding robotic assets against environmental hazards.

Analysis of Cooperative Informative Path Planning in Unknown Environments

This paper presents a novel framework for Informative Path Planning (IPP) in the context of traversing unknown environments, specifically tailored for a cooperative robotic system comprising a Micro Aerial Vehicle (MAV) scout and a land-based follower such as a rover. The primary objective of this framework is to establish the most cost-effective traversable path for the rover, utilizing the agility of the MAV to scout the environment effectively. This need arises prominently in planetary exploration missions, where rover navigation is impeded by the rough terrain, thus necessitating intelligent path planning to minimize wear and increase operational safety.

Key Contributions

The paper proposes a cooperative IPP approach that tackles the known challenges of simultaneous localization and mapping (SLAM) within uncharted territories. At its core, the framework employs a strategic information gain criterion, focusing on the scout covering only those regions directly impacting the follower's trajectory, thereby optimizing the path-planning process through efficiency in both time and spatial coverage.

The methodology is underpinned by an innovative utility function, which factors in follower path costs and leverages this model to guide the MAV’s exploratory path. The formal contribution also includes deriving stability theorems for the developed algorithm: proving termination, theoretical guarantees for finding optimal paths where feasible, and providing early termination criteria when paths are deemed infeasible or globally optimal.

Numerical Results

A series of experiments underscored the framework’s efficacy, demonstrating a 22.5% improvement in the speed of finding optimal paths and a 15% decrease in termination time compared to existing approaches. Such performance metrics highlight the framework's aptitude for practical deployment in dynamic exploration tasks, where computational efficiency is paramount.

The experimental evaluations utilized high-fidelity planetary environments, representative of Mars’ terrain, to validate the approach. A significant attribute of the proposed method is its ability to expedite the exploration process significantly by decreasing the area required for exploration from a completion perspective, achieving optimal path determination with up to 77% of scene coverage.

Theoretical and Practical Implications

Theoretically, this research offers a comprehensive solution for IPP concerning heterogeneous robotic teams in environments with uncertain scenarios. In practice, its implications are widespread, particularly in enabling extended mission durations for rover operations on extraterrestrial terrains, subsequently maximizing scientific yields while concurrently safeguarding robotic assets against environmental hazards.

Further, the strategy outlined within opens avenues for optimizing multi-agent system collaborations, hinting at future developments that could include probabilistic models to accommodate sensing errors and state estimation inaccuracies, thereby enhancing robustness in real-world applications.

Future Directions and Developments

Improvements in this domain could envision expanding the framework to incorporate uncertain sensing conditions and evolving environmental maps, potentially embedding reinforcement learning models to further refine path prediction and selection based on dynamic environmental variables.

Overall, this paper delineates a substantial forward step for cooperative robotic systems operating under conditions of incomplete information, setting the stage for further research on autonomous navigation strategies that exploit the synergy between aerial and ground robotic platforms. The practical utility of such IPP frameworks could redefine exploration missions, not only within planetary contexts but within any domain requiring meticulous environmental navigation and mapping solutions.