4 accepted papers
环境中的多机器人运动规划对安全高效协调构成挑战，但缺乏公平统一的测试平台来评估多样化算法。我们提出了DMRP-Bench，一个综合框架，旨在弥补这一空白。它采用分层架构，整合了全球和本地规划者，能够全面分析其在宏观系统层面结果和细粒度机器人间交互中的组合。高保真室内场景（如图书馆、商场、办公室）模拟多样的空间布局和行人动态，均在 NVIDIA Isaac Sim 环境中构建。对十六种规划器组合的广泛实验不仅揭示了轨迹效率与安全性之间的关键权衡，还促进了对机器人间协调的更深入评估。通过将路径执行的忠实度与交互结果关联，这些实验能够定量诊断局部行
Efficient data transmission and reasonable task allocation are important to improve multi-robot exploration efficiency. However, most communication data types typically contain redundant information and thus require massive communication volume. Moreover, exploration-oriented task allocation is far
Ground robot autonomous exploration for uneven terrains is still challenging since the rugged terrain structures not only degrade the exploration performance but also threaten the navigation safety of the robot. In this letter, a novel exploration planner is proposed for safe and fast exploration in
Local planning is one of the key technologies for mobile robots to achieve full autonomy and has been widely investigated. To evaluate mobile robot local planning approaches in a unified and comprehensive way, a mobile robot local planning benchmark called MRPB 1.0 is newly proposed in this paper. T…