面向搜救任务的异构双足与空中机器人协同的地形感知模型预测控制
Terrain-Aware Model Predictive Control of Heterogeneous Bipedal and Aerial Robot Coordination for Search and Rescue Tasks
AI总结：
本研究提出了一种面向搜救任务的异构机器人协同规划框架，结合高斯过程学习的地形感知MPC为双足和空中机器人生成安全路径并估计目标位置，利用scLTL和基于共识的算法进行任务分配，仿真验证了其有效性。
摘要：
人形机器人在搜救任务中具有显著优势，这得益于它们能够穿越崎岖地形并执行运输任务。在本研究中，我们提出了一个面向搜救行动的任务与运动规划框架，使用由人形机器人和空中机器人组成的异构机器人团队。我们提出了一种地形感知模型预测控制器（MPC），它结合了使用高斯过程（GP）学习的地形高程梯度。这种地形感知MPC为双足机器人生成安全导航路径以穿越崎岖地形，同时最小化地形坡度，并指示四旋翼飞行器执行空中搜索与测绘任务。救援目标的位置通过目标信念GP进行估计，该估计在地图探索期间在线更新。通过使用语法共安全线性时序逻辑（scLTL）编码导航任务，设计了用于任务分配的高级规划器，并设计了基于共识的算法用于单个机器人的任务分配。我们在具有各种地形和随机救援目标放置的不确定环境中，于仿真中评估了我们规划框架的有效性。
英文摘要：
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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