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.