HEHA: Hierarchical Planning for Heterogeneous Multi-Robot Exploration of Unknown Environments
🤖 AI Summary
This work addresses the problem of collaborative autonomous exploration by heterogeneous robotic teams—comprising UAVs, wheeled, and legged robots—in unknown, complex environments. The core challenge lies in jointly satisfying terrain traversability constraints and dynamically allocating tasks and optimizing paths amid significant platform capability disparities. Methodologically, we propose a hierarchical path planning framework: (i) a global layer employs Partially Arbitrary Focus Search (PEAF) to efficiently compute bounded-suboptimal traversable paths; and (ii) a local layer implements a terrain-adaptive path generation mechanism to avoid redundant exploration. Our key contribution is the tight integration of traversability modeling, heterogeneity-aware task allocation, and hierarchical optimization. Experimental results demonstrate that our approach reduces total exploration time by up to 30% compared to baseline methods, while significantly improving collaborative efficiency and environmental coverage.
Application Category
📝 Abstract
This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and legged robots, which have different capabilities to traverse complex terrains. A key challenge there is to intelligently allocate the robots to the unknown areas to be explored and determine the visiting order of those spaces subject to traversablity constraints, which leads to a large scale constrained optimization problem that needs to be quickly and iteratively solved every time when new space are explored. To address the challenge, we propose HEHA (Hierarchical Exploration with Heterogeneous Agents) by leveraging a recent hierarchical method that decompose the exploration into global planning and local planning. The major contribution in HEHA is its global planning, where we propose a new routing algorithm PEAF (Partial Anytime Focal search) that can quickly find bounded sub-optimal solutions to minimize the maximum path length among the agents subject to traversability constraints. Additionally, the local planner in HEHA also considers heterogeneity to avoid repeated and duplicated exploration among the robots. The experimental results show that, our HEHA can reduce up to 30% of the exploration time than the baselines.
Problem
Research questions and friction points this paper is trying to address.
Planning paths for heterogeneous robots exploring unknown environments
Allocating robots to unknown areas while considering traversability constraints
Minimizing maximum path length among agents with different capabilities
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical planning for heterogeneous multi-robot exploration
PEAF algorithm minimizes maximum path length with constraints
Local planner prevents duplicated exploration among robots
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