Optimization of cooperative environmental data acquisition by UAV swarm based on reinforcement learning algorithm and biomechanical inspiration
Abstract
Inspired by biomechanics, to enhance the collaborative efficiency of UAV swarms in complex—environment data collection, an innovative optimization scheme is proposed. This scheme draws parallels from the principles of biomechanics, such as the coordinated movement of biological organisms and their ability to adapt to various environmental stresses. Just as living organisms adjust their postures and movements in response to external forces to maintain balance and perform tasks efficiently, the proposed UAV swarm system aims to achieve better adaptability and efficiency through the key techniques of path planning, task allocation, and load balancing, which are inspired by the biomechanical mechanisms of coordination and adaptation. Load balancing in the UAV swarm is inspired by the way biological systems distribute mechanical stress. In the human body, different muscles and bones work together to evenly distribute the load during movement. Similarly, UAVs in the swarm need to balance the data—collection load to prevent over - stressing any single UAV. The results show that in the experiment where the number of nodes is increased from 50 to 200, the data acquisition coverage is improved from 93.4% to 98.1%, the task completion time is shortened from 112 to 73 s, and the energy consumption is controlled within the range of 180 to 430 Joules. The reinforcement learning algorithm demonstrated advantages over traditional methods in several performance metrics, including reducing the average transmission delay to 18.6 ms and efficiently distributing the task load, reducing the percentage of highly loaded nodes to 5.6%. These results validate the important role of the reinforcement—learning algorithm, which is inspired by biomechanics, in UAV—swarm cooperative environmental data collection. By mimicking the efficient and adaptable mechanisms in biological systems, the proposed optimization scheme for UAV swarms can better meet the challenges of complex - environment data collection.
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