One of the major research topics in unmanned aerial vehicle (UAV) collaborative control systems is the problem of multi-UAV target assignment and path planning (MUTAPP). It is a complicated optimization problem in which target assignment and path planning are solved separately. However, recalculation of the optimal results is too slow for real-time operations in dynamic environments because of the large number of calculations required. In this paper, we propose an artificial intelligence method named simultaneous target assignment and path planning (STAPP) based on a multi-agent deep deterministic policy gradient (MADDPG) algorithm, which is a type of multi-agent reinforcement learning algorithm. In STAPP, the MUTAPP problem is first constructed as a multi-agent system. Then, the MADDPG framework is used to train the system to solve target assignment and path planning simultaneously according to a corresponding reward structure. The proposed system can deal with dynamic environments effectively as its execution only requires the locations of the UAVs, targets, and threat areas. Real-time performance can be guaranteed as the neural network used in the system is simple. In addition, we develop a technique to improve the training effect and use experiments to demonstrate the effectiveness of our method.INDEX TERMS Multi-UAV, target assignment and path planning, multi-agent reinforcement learning, MADDPG, dynamic environments.

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Recently, the emerging bio-inspired event cameras have demonstrated potentials for a wide range of robotic applications in dynamic environments. In this paper, we propose a novel fast and asynchronous event-based corner detection method which is called FA-Harris. FA-Harris consists of several components, including an event filter, a Global Surface of Active Events (G-SAE) maintaining unit, a corner candidate selecting unit, and a corner candidate refining unit. The proposed G-SAE maintenance algorithm and corner candidate selection algorithm greatly enhance the real-time performance for corner detection, while the corner candidate refinement algorithm maintains the accuracy of performance by using an improved event-based Harris detector. Additionally, FA-Harris does not require artificially synthesized event-frames and can operate on asynchronous events directly. We implement the proposed method in C++ and evaluate it on public Event Camera Datasets. The results show that our method achieves approximately 8× speed-up when compared with previously reported event-based Harris detector, and with no compromise on the accuracy of performance.

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