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The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed event-triggered algorithmic solution to the multi-agent average consensus problem for networks whose communication topology is described by weight-balanced, strongly connected digraphs. The proposed event-triggered communication and control strategy does not rely on individual agents having continuous or periodic access to information about the state of their neighbors. In addition, it does not require the agents to have a priori knowledge of any global parameter to execute the algorithm. We show that, under the proposed law, events cannot be triggered an infinite number of times in any finite period (i.e., no Zeno behavior), and that the resulting network executions provably converge to the average of the initial agents’ states exponentially fast. We also provide weaker conditions on connectivity under which convergence is guaranteed when the communication topology is switching. Finally, we also propose and analyze a periodic implementation of our algorithm where the relevant triggering functions do not need to be evaluated continuously. Simulations illustrate our results and provide comparisons with other existing algorithms. [ABSTRACT FROM AUTHOR]