Area coverage and target tracking are important applications of UAV swarms. However, attempting to perform both tasks simultaneously can be a challenge, particularly under resource constraints. In such scenarios, UAV ...
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Area coverage and target tracking are important applications of UAV swarms. However, attempting to perform both tasks simultaneously can be a challenge, particularly under resource constraints. In such scenarios, UAV swarms must collaborate to cover extensive areas while simultaneously tracking multiple targets. This paper proposes a deep reinforcement learning (DRL)-based, scalable UAV swarm control method for a simultaneous coverage and tracking (SCT) task, called the SCT-DRL algorithm. SCT-DRL simplifies the interaction between UAV swarms into a series of pairwise interactions and aggregates the information of perceived targets in advance, based on which forms the control framework with a variable number of neighboring UAVs and targets. Another highlight of SCT-DRL is using the trajectories of the traditional one-step optimization method to initialize the value network, which encourages the UAVs to select the actions leading to the state with less rest time to task completion to avoid extensive random exploration at the beginning of training. SCT-DRL can be seen as a special improvement of the traditional one-step optimization method, shaped by the samples derived from the latter, and gradually overcomes the inherent myopic issue with the far-sighted value estimation through RL training. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments.
This paper presents two autonomous methods for cooperatively controlling a number of distributed mobile platforms in order to accurately and efficiently achieve a desired common mission objective. The first method pre...
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ISBN:
(纸本)9781479977727
This paper presents two autonomous methods for cooperatively controlling a number of distributed mobile platforms in order to accurately and efficiently achieve a desired common mission objective. The first method presented is based on an information-theoretic approach utilizing a decentralized data fusion (DDF) core with information measures. This technique achieves coordinated control of distributed mobile platforms by maximizing joint information gains relative to various information metrics of interest. The second method presented is based on a distributed locational optimization approach employing Voronoi tessellations for optimal placement of resources relative to a given area of interest. This technique, referred to as simultaneous coverage and tracking (SCAT), provides a framework which allows the full coupling of environment coverage, target tracking and task assignment. In this work, these state-of-the-art decentralized control methodologies are uniquely combined in order to couple their individual strengths and provide a complementary capability. A heterogeneous application example consisting of ground-based Intelligence, Surveillance and Reconnaissance (ISR) with air-based Suppression of Enemy Air Defenses (SEAD) establishes a proof of concept while illustrating the complementary strengths of the two presented autonomous architectures.
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