Lyapunov-Based $\mathcal{L}_2$-Stable PI-Like Control of a Four-Wheel Independently Driven and Steered Robot

In this letter, Lyapunov-based synthesis of a PI-like controller is proposed for $\mathcal{L}_2$-stable motion control of an independently driven and steered four-wheel mobile robot. An explicit, structurally verified model is used to enable systematic controller design with stability and performance guarantees suitable for real-time operation. A Lyapunov function is constructed to yield explicit bounds and $\mathcal{L}_2$ stability results, supporting feedback synthesis that reduces configuration dependent effects. The resulting control law maintains a PI-like form suitable for standard embedded implementation while preserving rigorous stability properties. Effectiveness and robustness are demonstrated experimentally on a real four-wheel mobile robot platform.

Odometry Calibration and Pose Estimation of a 4WIS4WID Mobile Wall Climbing Robot

This paper presents the design of a pose estimator for a four wheel independent steer four wheel independent drive (4WIS4WID) wall climbing mobile robot, based on the fusion of multimodal measurements, including wheel odometry, visual odometry, and an inertial measurement unit (IMU) data using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The pose estimator is a critical component of wall climbing mobile robots, as their operational environment involves carrying precise measurement equipment and maintenance tools in construction, requiring information about pose on the building at the time of measurement. Due to the complex geometry and material properties of building facades, the use of traditional localization sensors such as laser, ultrasonic, or radar is often infeasible for wall-climbing robots. Moreover, GPS-based localization is generally unreliable in these environments because of signal degradation caused by reinforced concrete and electromagnetic interference. Consequently, robot odometry remains the primary source of velocity and position information, despite being susceptible to drift caused by both systematic and non-systematic errors. The calibrations of the robot's systematic parameters were conducted using nonlinear optimization and Levenberg-Marquardt methods as Newton-Gauss and gradient-based model fitting methods, while Genetic algorithm and Particle swarm were used as stochastic-based methods for kinematic parameter calibration. Performance and results of the calibration methods and pose estimators were validated in detail with experiments on the experimental mobile wall climbing robot.