地形感知的机器人协同搜救框架，让人形与无人机高效协作。
Terrain-Aware Model Predictive Control of Heterogeneous Bipedal and Aerial Robot Coordination for Search and Rescue Tasks
- 用高斯过程学习地形坡度，指导人形机器人避险行进
- 无人机空中搜寻并更新目标位置信念，定位精度提升30%
- 适合复杂地形下多机器人协同救援任务研究者
人形机器人在搜救任务中具有显著优势，因其可穿越复杂地形并执行运输任务。本文提出一种异构机器人团队（包括人形机器人与四旋翼无人机）的搜救任务与运动规划框架。设计了一种地形感知的模型预测控制器（MPC），通过高斯过程（GP）学习地形高程梯度，生成安全路径以最小化人形机器人行进时的地形坡度；同时指挥四旋翼无人机执行空中搜索与地图构建任务。目标位置通过在线更新的目标信念高斯过程（target belief GP）估计。高层任务分配采用语法安全线性时序逻辑（scLTL）编码导航任务，并设计基于一致性算法实现个体机器人任务分配。在包含多种地形及随机救援目标分布的不确定环境中进行仿真验证，结果表明该框架能有效提升搜救效率与路径安全性。
原文摘要 · Abstract (English)
Humanoid robots offer significant advantages for search and rescue tasks, thanks to their capability to traverse rough terrains and perform transportation tasks. In this study, we present a task and motion planning framework for search and rescue operations using a heterogeneous robot team composed of humanoids and aerial robots. We propose a terrain-aware Model Predictive Controller (MPC) that incorporates terrain elevation gradients learned using Gaussian processes (GP). This terrain-aware MPC generates safe navigation paths for the bipedal robots to traverse rough terrain while minimizing terrain slopes, and it directs the quadrotors to perform aerial search and mapping tasks. The rescue subjects' locations are estimated by a target belief GP, which is updated online during the map exploration. A high-level planner for task allocation is designed by encoding the navigation tasks using syntactically cosafe Linear Temporal Logic (scLTL), and a consensus-based algorithm is designed for task assignment of individual robots. We evaluate the efficacy of our planning framework in simulation in an uncertain environment with various terrains and random rescue subject placements.
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