A large-scale mobile robot (LSMR) is a high-order multibody system that often operates on loose, unconsolidated terrain, which reduces traction. This paper presents a comprehensive navigation and control framework for an LSMR that ensures stability and safety-defined performance, delivering robust operation on slip-prone terrain by jointly leveraging high-performance techniques. The proposed architecture comprises four main modules: (1) a visual pose-estimation module that fuses onboard sensors and stereo cameras to provide an accurate, low-latency robot pose, (2) a high-level nonlinear model predictive control that updates the wheel motion commands to correct robot drift from the robot reference pose on slip-prone terrain, (3) a low-level deep neural network control policy that approximates the complex behavior of the wheel-driven actuation mechanism in LSMRs, augmented with robust adaptive control to handle out-of-distribution disturbances, ensuring that the wheels accurately track the updated commands issued by high-level control module, and (4) a logarithmic safety module to monitor the entire robot stack and guarantees safe operation. The proposed low-level control framework guarantees uniform exponential stability of the actuation subsystem, while the safety module ensures the whole system-level safety during operation. Comparative experiments on a 6,000 kg LSMR actuated by two complex electro-hydrostatic drives, while synchronizing modules operating at different frequencies.

翻译：大规模移动机器人是一种高阶多体系统，常在松散、非固结的地形上运行，这会降低其牵引力。本文提出了一种用于大规模移动机器人的综合导航与控制框架，该框架通过协同利用高性能技术，确保稳定性与安全定义性能，在易打滑地形上实现鲁棒操作。所提出的架构包含四个主要模块：(1) 视觉位姿估计模块，融合机载传感器与立体相机以提供精确、低延迟的机器人位姿；(2) 高层非线性模型预测控制，更新轮式运动指令以校正机器人在易打滑地形上相对于参考位姿的漂移；(3) 底层深度神经网络控制策略，近似大规模移动机器人中轮式驱动执行机构的复杂行为，并辅以鲁棒自适应控制以处理分布外扰动，确保车轮精确跟踪高层控制模块发出的更新指令；(4) 对数安全模块，用于监控整个机器人系统栈并保障安全运行。所提出的底层控制框架保证了执行机构子系统的一致指数稳定性，而安全模块则确保整个系统在运行过程中的系统级安全性。在由两个复杂电液静压驱动装置驱动的6000公斤大规模移动机器人上进行了对比实验，同时同步了不同频率运行的模块。