Adaptive Trajectory Optimization for Robotic Arms: Integrating Machine Learning, Nonlinear Programming, and Real-Time Control
Abstract: Trajectory optimization for robotic arms plays a crucial role in the development of autonomous systems across various industries, including manufacturing, healthcare, and space exploration. This paper explores advanced computational techniques to optimize the motion paths of robotic manipulators with respect to multiple objectives, such as minimizing energy consumption, ensuring task precision, and avoiding obstacles. We introduce a cost function that penalizes deviations from the optimal path and overuse of energy, leading to more efficient and smoother trajectories. In addition, the use of model predictive control (MPC) ensures real-time adaptability by constantly refining the robot's motion in response to changing conditions. The paper also investigates the use of reinforcement learning for autonomous adaptation and long-term learning. The robotic arm progressively improves its performance by interacting with its environment and learning from past trajectories. We conduct extensive simulations in various environments to evaluate the effectiveness of the proposed approach. The results demonstrate significant improvements in trajectory efficiency, with reductions in energy usage and faster task completion times compared to conventional planning methods.
Submission Number: 202
Loading