Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures

Abstract

Multi-agent autonomous exploration is essential for applications such as environmental monitoring, search and rescue, and industrial-scale surveillance. However, effective coordination under communication constraints remains a significant challenge. Frontier exploration algorithms analyze the boundary between the known and unknown regions to determine the next-best view that maximizes exploratory gain. This article proposes an enhancement to existing frontier-based exploration algorithms by introducing a probabilistic approach to frontier prioritization. By leveraging Dirichlet process Gaussian mixture model (DP-GMM) and a probabilistic formulation of information gain, the method improves the quality of frontier prioritization. The proposed enhancement, integrated into two state-of-the-art multi-agent exploration algorithms, consistently improves performance across environments of varying clutter, communication constraints, and team sizes. Simulations showcase an average gain of $10\%$ and $14\%$ for the two algorithms across all combinations. Successful deployment in real-world experiments with a dual-drone system further corroborates these findings.

Figures (6)

• Figure A1: (a) The white and black spaces represent the known and unknown, respectively. The continuous green boundary represents the frontier, and the green dots represent the discretized frontiers. (b) We can see that because of hard clustering, frontier 1 will be considered separate from frontier 2 and frontier 3, which will lead to separate allocation after frontier processing. Similarly, despite frontier 4 being closer to drone C than drone B, the K-Means clustered it separately from frontier 5. (c) The responsibility of green, blue, and red Gaussians in a given frontier is represented by the color saturation of the green, blue, and red respectively.
• Figure B1: The proposed frontier prioritization module depicted as viewpoint processor processes the incoming frontiers and shares the output with a frontier-based exploration planner.
• Figure B2: An instance from a real-world experiment with $2$ UAVs in an Octomap. The UAV paths are shown in red and green. (a) The merged viewpoints, represented as white blobs, from Sec \ref{['subsec:Viewpoint_Generation']} (b) Soft cluster assignments from Sec \ref{['subsec:Viewpoint_Clustering']}, with color-coded probabilities visualized via smooth transitions between red and green to reflect each viewpoint's responsibility. (c) The prioritized viewpoint inferred from Sec \ref{['subsec:Viewpoint_Priortization']} is shown in red and green cubes.
• Figure C1: (a) and (b) showcase the trajectories completed by $4$ UAVs in a $50m \times 50m$ mixed density forest simulation using FAME and FAME+FP respectively. The blue blobs showcase the various points at which UAV trajectories intersect. (c) Showcases the computational time required to attain $P(k_c,I|\xi)$ for varying frontier counts.
• Figure C2: Exploration time analysis across a $0.2$ Trees$/m^2$ forest environment with varying communication range and UAV team sizes.