A Data Driven Safe Constrained Optimal Control Approach for Trajectory Planning of Wheeled Robots

This paper proposes optimal control frame for trajectory planning while ensuring obstacle avoidance for specific kind of wheeled robots, which are used in manufacturing process. The proposed methodology employs a kinematic model of a wheeled robot, integrating a cost function that balances goal-reaching efficiency with safety constraints. A Control Barrier Function (CBF) is incorporated into the optimization framework to maintain the robot’s trajectory within safe regions, preventing collisions. The cost function is designed to minimize the Euclidean distance to the goal, penalize control efforts, and prioritize safety near obstacles. The control law is derived by solving a constrained optimization problem with a learning process that guarantees system stability and convergence to the goal. The Simulation results demonstrate the effectiveness of the approach in navigating complex environments with obstacles.