Reinforcement Learning-Based Path Planning Approach for Unmanned Arial Vehicles

Path planning plays an important role in autonomous flight control design for Unmanned Arial Vehicles (UAVs). In this paper, a path planning method based on the concepts of Reinforcement Learning (RL) is proposed. A Q-learning algorithm with a dynamic reward function is proposed and successfully implemented to ensure flyable paths with efficient collision avoidance. The dynamic reward function is introduced allowing the UAV to use the real-time distance between its current position and the destination as instructional data during training. Such an improved training mechanism leads to minimize the path length, reduce the learning cost, avoid the inefficient exploration of the search space, and ameliorate the convergence of the Q-planning algorithm. Many performance indicators are considered to evaluate the proposed Q-learning-based UAV's planner. Demonstrative results are implemented to illustrate the efficiency and practicability of the proposed artificial intelligence-based path planning approach.