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This article presents a complete kinematic and dynamic modeling and trajectory tracking control of an autonomous tracked vehicle. First, based on the vehicle’s kinematics, the reference linear and angular velocities are evaluated. The kinematic controller is proposed as an integrated backstepping controller. Then, based on vehicle dynamics and slipping characteristics, an integral sliding mode control is utilized to get the desired vehicle drive torques and converge its trajectory to the desired one. Whereas the controller gains are optimally calculated. Furthermore, based on the Lyapunov stability, the proof of stability for proposed controllers is presented. Finally, simulation results are conducted to validate the effectiveness of the proposed control algorithm compared with a hybrid backstepping-modified proportional–integral–derivative dynamic controller and a nonlinear feedback acceleration controller.

This paper presents an online path planning approach for an autonomous tracked vehicle in a cluttered environment based on teaching–learning-based optimization (TLBO), considering the path smoothness, and the potential collision with the surrounding obstacles. In order to plan an efficient path that allows the vehicle to be autonomously navigated in cluttered environments, the path planning problem is solved as a multi-objective optimization problem. First, the vehicle perception is fully achieved by means of inertial measurement unit (IMU), wheels odometry, and light detection and ranging (LiDAR). In order to compensate the sensors drift to achieve more reliable data and improve the localization estimation and corrections, data fusion between the outputs of wheels odometry, LiDAR, and IMU is made through extended Kalman filter (EKF). Then, TLBO is proposed and applied to determine the optimum online path, where the objectives are to find the shortest path to reach the target destination, and to maximize the path smoothness, while avoiding the surrounding obstacles, and taking into account the vehicle dynamic and algebraic constraints. To check the performance of the proposed TLBO algorithm, it is compared in simulation to genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA–PSO algorithm. Finally, real-time experiments based on robot operating system (ROS) implementation are conducted to validate the effectiveness of the proposed path planning algorithm.