The recent advancements of technology in robotics and wireless communication have enabled the low-cost and large-scale deployment of mobile sensor nodes for target tracking which is a critical application scenario of wireless sensor networks. Due to the constraints of limited sensing range, it is of great importance to design node coordination mechanism for reliable tracking so that at least the target can always be detected with a high probability while the total network energy cost can be reduced for longer network lifetime. In this paper, we deal with this problem considering both the unreliable wireless channel and the network energy constraint. We transfer the original problem into a dynamic coverage problem, and decompose it into two subproblems. By exploiting the online estimate of target location, we first decide the locations where the mobile nodes should move into so that the reliable tracking can be guaranteed. Then, we assign different nodes to each location in order that the total energy cost in terms of moving distance can be minimized. Extensive simulations under various system settings are employed to evaluate the effectiveness of our solution.
Abstract-As technology advancements in robotics and wireless communication, tracking mobile targets using mobile sensors has aroused widespread concern in recent years. In this paper, we propose a novel coordinative moving strategy for autonomous mobile sensor networks to guarantee the target can be detected in each observed step while minimizing the amount of moving sensors. The proposed scheme consists of obtaining the current position of the target, which is then used to predict the next time-step location of the target. Once the uncertainty region of the target's position is defined, the proposed method allows the mobile sensors to cover it in an optimal way. Therefore, we can assign each mobile sensor to an optimal location to cover the uncertainty region while minimizing the total traveled distance of sensors. Extensive simulations are given to evaluated performance and demonstrate the efficiency of the proposed strategy.
International audienceAutonomous formation flying (flocking) of UAVs requires coordination of efforts and solutions in multiple research fields (robotics, wireless sensor networks to name a few). The Airborne Embedded auTonomOUs Robust Network of Objects and Sensors (AETOURNOS) project is designed as a federative, fueling platform and global evaluation tool. It has spawned another collaborative project bridging the gap between Computer Science and Automation: 6PO (Cyber-Physical Systems and Cooperative Control of a Fleet of UAVs). We want to investigate new flocking strategies, mobility-aware communication protocols (and reciprocally: movements influenced by network QoS), and to ease the cooperation between experts, we need co-simulation solutions, and "universal" control interfaces. This article presents our first contribution to this grand scheme: a stand-alone simulation of an autonomous flocking strategy taking into account both the dynamics of the UAVs and their communications, which has allowed us to examine the relation between network unreliability and control computation. To better study the influence of the communication network, we present an architecture coupling a dedicated network simulator with the UAVs simulator, as well as with a real drone piloting interface. The lessons learned help us build a research and action plan for the years to come
The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems (MASs) under communication faults. All the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the agents. Linear matrix inequalities (LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization error. Based on the closed-loop system, an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy consumption. The proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles (UAVs) under communication faults. A comparison between a state-of-theart technique and the proposed technique has been provided, demonstrating the performance improvement brought by the proposed approach.
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