NMPC-Augmented Visual Navigation and Safe Learning Control for Large-Scale Mobile Robots

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.

Vision-based Goal-Reaching Control for Mobile Robots Using a Hierarchical Learning Framework

Reinforcement learning (RL) is effective in many robotic applications, but it requires extensive exploration of the state-action space, during which behaviors can be unsafe. This significantly limits its applicability to large robots with complex actuators operating on unstable terrain. Hence, to design a safe goal-reaching control framework for large-scale robots, this paper decomposes the whole system into a set of tightly coupled functional modules. 1) A real-time visual pose estimation approach is employed to provide accurate robot states to 2) an RL motion planner for goal-reaching tasks that explicitly respects robot specifications. The RL module generates real-time smooth motion commands for the actuator system, independent of its underlying dynamic complexity. 3) In the actuation mechanism, a supervised deep learning model is trained to capture the complex dynamics of the robot and provide this model to 4) a model-based robust adaptive controller that guarantees the wheels track the RL motion commands even on slip-prone terrain. 5) Finally, to reduce human intervention, a mathematical safety supervisor monitors the robot, stops it on unsafe faults, and autonomously guides it back to a safe inspection area. The proposed framework guarantees uniform exponential stability of the actuation system and safety of the whole operation. Experiments on a 6,000 kg robot in different scenarios confirm the effectiveness of the proposed framework.

All-Electric Heavy-Duty Robotic Manipulator: Actuator Configuration Optimization and Sensorless Control

This paper presents a unified framework that integrates modeling, optimization, and sensorless control of an all-electric heavy-duty robotic manipulator (HDRM) driven by electromechanical linear actuators (EMLAs). An EMLA model is formulated to capture motor electromechanics and direction-dependent transmission efficiencies, while a mathematical model of the HDRM, incorporating both kinematics and dynamics, is established to generate joint-space motion profiles for prescribed TCP trajectories. A safety-ensured trajectory generator, tailored to this model, maps Cartesian goals to joint space while enforcing joint-limit and velocity margins. Based on the resulting force and velocity demands, a multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to select the optimal EMLA configuration. To accelerate this optimization, a deep neural network, trained with EMLA parameters, is embedded in the optimization process to predict steady-state actuator efficiency from trajectory profiles. For the chosen EMLA design, a physics-informed Kriging surrogate, anchored to the analytic model and refined with experimental data, learns residuals of EMLA outputs to support force and velocity sensorless control. The actuator model is further embedded in a hierarchical virtual decomposition control (VDC) framework that outputs voltage commands. Experimental validation on a one-degree-of-freedom EMLA testbed confirms accurate trajectory tracking and effective sensorless control under varying loads.

LiDAR-Inertial SLAM-Based Navigation and Safety-Oriented AI-Driven Control System for Skid-Steer Robots

Integrating artificial intelligence (AI) and stochastic technologies into the mobile robot navigation and control (MRNC) framework while adhering to rigorous safety standards presents significant challenges. To address these challenges, this paper proposes a comprehensively integrated MRNC framework for skid-steer wheeled mobile robots (SSWMRs), in which all components are actively engaged in real-time execution. The framework comprises: 1) a LiDAR-inertial simultaneous localization and mapping (SLAM) algorithm for estimating the current pose of the robot within the built map; 2) an effective path-following control system for generating desired linear and angular velocity commands based on the current pose and the desired pose; 3) inverse kinematics for transferring linear and angular velocity commands into left and right side velocity commands; and 4) a robust AI-driven (RAID) control system incorporating a radial basis function network (RBFN) with a new adaptive algorithm to enforce in-wheel actuation systems to track each side motion commands. To further meet safety requirements, the proposed RAID control within the MRNC framework of the SSWMR constrains AI-generated tracking performance within predefined overshoot and steady-state error limits, while ensuring robustness and system stability by compensating for modeling errors, unknown RBF weights, and external forces. Experimental results verify the proposed MRNC framework performance for a 4,836 kg SSWMR operating on soft terrain.

Anti-Slip AI-Driven Model-Free Control with Global Exponential Stability in Skid-Steering Robots

Undesired lateral and longitudinal wheel slippage can disrupt a mobile robot's heading angle, traction, and, eventually, desired motion. This issue makes the robotization and accurate modeling of heavy-duty machinery very challenging because the application primarily involves off-road terrains, which are susceptible to uneven motion and severe slippage. As a step toward robotization in skid-steering heavy-duty robot (SSHDR), this paper aims to design an innovative robust model-free control system developed by neural networks to strongly stabilize the robot dynamics in the presence of a broad range of potential wheel slippages. Before the control design, the dynamics of the SSHDR are first investigated by mathematically incorporating slippage effects, assuming that all functional modeling terms of the system are unknown to the control system. Then, a novel tracking control framework to guarantee global exponential stability of the SSHDR is designed as follows: 1) the unknown modeling of wheel dynamics is approximated using radial basis function neural networks (RBFNNs); and 2) a new adaptive law is proposed to compensate for slippage effects and tune the weights of the RBFNNs online during execution. Simulation and experimental results verify the proposed tracking control performance of a 4,836 kg SSHDR operating on slippery terrain.

Increasing the Task Flexibility of Heavy-Duty Manipulators Using Visual 6D Pose Estimation of Objects

Recent advances in visual 6D pose estimation of objects using deep neural networks have enabled novel ways of vision-based control for heavy-duty robotic applications. In this study, we present a pipeline for the precise tool positioning of heavy-duty, long-reach (HDLR) manipulators using advanced machine vision. A camera is utilized in the so-called eye-in-hand configuration to estimate directly the poses of a tool and a target object of interest (OOI). Based on the pose error between the tool and the target, along with motion-based calibration between the camera and the robot, precise tool positioning can be reliably achieved using conventional robotic modeling and control methods prevalent in the industry. The proposed methodology comprises orientation and position alignment based on the visually estimated OOI poses, whereas camera-to-robot calibration is conducted based on motion utilizing visual SLAM. The methods seek to avert the inaccuracies resulting from rigid-body--based kinematics of structurally flexible HDLR manipulators via image-based algorithms. To train deep neural networks for OOI pose estimation, only synthetic data are utilized. The methods are validated in a real-world setting using an HDLR manipulator with a 5 m reach. The experimental results demonstrate that an image-based average tool positioning error of less than 2 mm along the non-depth axes is achieved, which facilitates a new way to increase the task flexibility and automation level of non-rigid HDLR manipulators.