多机器人异构探索未知环境，智能分配与路径规划提速三成。
HEHA: Hierarchical Planning for Heterogeneous Multi-Robot Exploration of Unknown Environments
- 分层规划：全局用新算法快速找近优解，局部考虑机器人差异避免重复探索。
- 实验表明探索时间最多减少30%，在复杂地形中表现更优。
- 适合需高效协同探索的无人机、轮式、足式机器人系统应用。
本文研究使用无人机、轮式和足式等异构机器人自主探索未知环境时的路径规划问题。核心挑战在于如何智能分配不同能力的机器人至待探索区域，并确定其访问顺序，同时满足可通行性约束，形成需快速迭代求解的大规模约束优化问题。为此，我们提出HEHA（分层异构探索）方法，利用分层规划思想将任务分解为全局与局部规划。其关键贡献在于全局规划中提出的新型路由算法PEAF（部分即时焦点搜索），可在满足可通行约束下快速找到最大路径长度最小化的有界次优解。此外，局部规划也考虑异构性，避免机器人间重复探索。实验结果表明，相较于基线方法，HEHA可最多减少30%的探索时间。
原文摘要 · Abstract (English)
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.
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