Joint Design of Communication, Wireless Energy Transfer, and Control for Swarm Autonomous Underwater Vehicles

Abstract : A swarm of Autonomous Underwater Vehicles (AUVs) can provide richer spatial-temporal information than the traditional single-robot system, which can be used for underwater mapping, exploration, target tracking, among others. However, the limitation of AUVs’ battery cannot support persistent services, which restricts AUVs’ operating range and mission duration. In this paper, a mobile underwater charging solution is developed that can continuously recharge a swarm of AUVs by using a wireless mobile charger. Magnetic induction-based communication and wireless energy transfer are employed. This paper first shows that by using tri-axis coils, reliable wireless communication and wireless energy transfer without coil orientation losses can be obtained. After that, wireless communication, motion control, and wireless energy transfer are jointly designed for a swarm of mission-driven AUVs. In particular, optimal continuous motion controllers are developed for AUVs to avoid intra-swarm collisions and networking protocols are designed to optimally allocate resources for wireless communication and wireless energy transfer. The proposed approach can guide AUVs to their destinations, while maintaining wireless network integrity and maximizing wireless energy transfer efficiency, upon which the constraints on AUVs’ batteries can be eliminated and cheaper and smaller AUVs can be employed for various underwater applications.
 EXISTING SYSTEM :
 ? The development of smart mobile sensor network that provides node localisation as a service for an existent acoustic network was discussed in . ? All closed-form solutions have relatively low computational complexity and they do not accommodate the situation when a solution does not exist. ? In contrast, substantial efforts are needed when integrating new localisation methods in an existent EKF-based navigation for two reasons: Gaussian error modelling; and, the entire filter re-implementation to expand the covariance matrix and the state vector.
 DISADVANTAGE :
 ? In this paper, the problem of joint communication and control is studied for a swarm of three cellular-connected UAVs positioned in a triangle formation. ? However, prior works, such as and, ignore the impact of wireless system on the stability of the UAV and solely focus on the communication system design. ? For the swarm of UAVs, the delayed information received from the wireless links can negatively impact the control system’s ability to coordinate the movements. ? We then study the impact of interference on the reliability performance of the wireless network and finally obtain the design guideline of formulating a stable triangle formation for a swarm of three UAV.
 PROPOSED SYSTEM :
 • A behaviour-based cooperative algorithm for a mobile sensor node was proposed in , where each sensor node is mounted on an AUV. • Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. • A centralised Extended Kalman Filter (EKF) algorithm was proposed in , where the algorithm has access to all sensor data, including range measurements, to reduce the AUV’s location estimate uncertainty. • A decentralised approach was proposed in, where the Extended Information Filter (EIF) is utilised in order to enhance the performance of the algorithm that was proposed in.
 ADVANTAGE :
 ? To characterize the performance of the wireless system, we introduce a notion of reliability for the wireless system. ? The reliability performance of the wireless network with different densities of interfering UAVs when the spacing target increases. ? Also, in, groups of UAVs have been used to create a reconfigurable antenna array in the sky so as to provide wireless service to ground users. ? This threshold can, in turn, be used to identify the reliability requirement for the wireless communication system. In particular, we use stochastic geometry to mathematically characterize the reliability of the wireless network. ? We have used stochastic geometry to derive the mathematical expression for the reliability of the wireless system, defined as the probability of meeting the control system’s delay requirements.

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