In this paper, the boundary coverage of known environments is investigated using multiple microrobots (MMR) involved in a distributed inspection case study. A strong need for an operator is able to control and receive...
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In this paper, the boundary coverage of known environments is investigated using multiple microrobots (MMR) involved in a distributed inspection case study. A strong need for an operator is able to control and receive feedback from microrobots. However, communication range is limited because of the size effect of microrobot, and obstacles may also prevent microrobots from communicating. To enable MMR to accomplish coverage task while maintaining the network connectivity with a base station, we propose a market-based boundary coverage algorithm. This algorithm can dynamically allocate the boundary coverage task to a microrobot, so as to adapt to the change of communication network topology. A motion control model based on virtual spring-damper system is established to prevent communication network splitting by monitoring infrared link quantity information among microrobot nodes. Simulations and experimental results, obtained using our MMR tested in a distributed inspection case study, demonstrate that the proposed solution fulfills the objective of maintaining network connectivity at all times while completing the allocated boundary coverage task.
Unmanned aerial vehicle (UAV) swarms are becoming increasingly attractive as highly integrated miniature sensors and processors deliver extraordinary performance. The employment of UAV swarms on complex real-life task...
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Unmanned aerial vehicle (UAV) swarms are becoming increasingly attractive as highly integrated miniature sensors and processors deliver extraordinary performance. The employment of UAV swarms on complex real-life tasks has motivated exploration on allocation problems involving multiple UAVs, complex constraints, and multiple tasks with coupling relationships. Such problems have been summarized domain independently as multirobot task allocation problems with temporal and ordering constraints (MRTA/TOC). The majority of MRTA/TOC works have hitherto focused on deterministic settings, while their stochastic counterparts are sparsely explored. In this article, allocation problems incorporating classification uncertainty of targets and soft ordering constraints of tasks are considered. To address such problems, a novel market-based allocation algorithm, the probability-tuned market-based allocation (PTMA), is proposed. PTMA consists of iterations between two phases: 1) the first phase updates local perception of global situational awareness and 2) the second phase is a market-based allocation scheme with embedded artificial randomness to locally generate allocation results. Under reasonable assumptions on classification uncertainty, the proposed PTMA algorithm is proven to guarantee a superior performance compared with conventional auctions, verifying the feasibility of using randomness to counter randomness theoretically. Three groups of numerical experiments have been conducted to assess the performance of the proposed PTMA. The first group of experiments compares PTMA with the consensus-based auction algorithm (CBAA), the chance-constrained CBAA, and the PTMA-I (specialization of PTMA with the Identity matrix), on simulated task scenarios with varying degrees of classification uncertainty. The second group evaluates the stability of PTMA. The third group tests the scalability of PTMA on expanded task scenarios. Simulation results demonstrate a satisfactory performance
Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to real...
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Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to realize swarm scheduling. It has no central node and UAVs can freely join or leave it, thus making it more robust and flexible. However, the two most representative methods, the Consensus-based Bundle algorithm (CBBA) and the Performance Impact (PI) algorithm, pursue the minimum cost impact of tasks, which have optimization limitations and are easily cause task conflicts. In this paper, a new concept called "task consideration" is proposed to quantify the impact of tasks on scheduling and the regression of the task itself, balancing the exploration of the UAV for the minimum-impact task and the regression of neighboring tasks to improve the optimization and convergence of scheduling. In addition, the conflict resolution rules are modified to fit the proposed method, and the exploration of tasks is increased by a new removal method to further improve the optimization. Finally, through extensive Monte Carlo experiments, compared with CBBA and PI, the proposed method is shown to perform better in terms of task allocation and total travel time, and with the increase in the number of average UAV tasks, the number of iterations is less and the convergence is faster.
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