Abstract
Unmanned aerial vehicle (UAV) autonomous tracking and landing is playing an increasingly important role in military and civil applications. In particular, machine learning has been successfully introduced to robotics-related tasks. A novel UAV autonomous tracking and landing approach based on a deep reinforcement learning strategy is presented in this paper, with the aim of dealing with the UAV motion control problem in an unpredictable and harsh environment. Instead of building a prior model and inferring the landing actions based on heuristic rules, a model-free method based on a partially observable Markov decision process (POMDP) is proposed. In the POMDP model, the UAV automatically learns the landing maneuver by an end-to-end neural network, which combines the Deep Deterministic Policy Gradients (DDPG) algorithm and heuristic rules. A Modular Open Robots Simulation Engine (MORSE)-based reinforcement learning framework is designed and validated with a continuous UAV tracking and landing task on a randomly moving platform in high sensor noise and intermittent measurements. The simulation results show that when the moving platform is moving in different trajectories, the average landing success rate of the proposed algorithm is about 10% higher than that of the Proportional-Integral-Derivative (PID) method. As an indirect result, a state-of-the-art deep reinforcement learning-based UAV control method is validated, where the UAV can learn the optimal strategy of a continuously autonomous landing and perform properly in a simulation environment.
Highlights
In recent years, with the rapid development of unmanned aerial vehicle (UAV) technology, the UAV has been widely used in military and civilian fields, such as search, rescue, exploration, and surveillance [1,2]
To evaluate the behavior of the control system approach, experiments were performed on the Modular Open Robots Simulation Engine (MORSE) simulation platform, which can perform accurate dynamic simulations based on the state-of-the art Bullet library
The UAV speed was controlled by the algorithm proposed in this paper, and the orientation of the ground vehicle was able to be changed at any time from the command line
Summary
With the rapid development of unmanned aerial vehicle (UAV) technology, the UAV has been widely used in military and civilian fields, such as search, rescue, exploration, and surveillance [1,2]. Autonomous tracking and landing is a key point in UAV application [3,4,5,6]. Due to the absence of precise sensors and the constraint of the sensors’ specific physical motion, autonomous tracking and landing works poorly with high sensor noise [6,8] and intermittent measurements [9,10]. Given the importance and complexity of the UAV tracking and autonomous landing, increasingly more scholars from different fields have shown interest in specific solutions such as perception and relative pose estimation [11,12] or trajectory optimization and control [13,14].
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