In recent years, the increasing popularity of multi-vehicle missions has been accompanied by a growing interest in the development of control strategies to ensure safety in these scenarios. In this work, we propose a control framework for coordination and collision avoidance in cooperative multi-vehicle missions based on a speed adjustment approach. The overall problem is decoupled in a coordination problem, in order to ensure coordination and inter-vehicle safety among the agents, and a collision-avoidance problem to guarantee the avoidance of non-cooperative moving obstacles. We model the network over which the cooperative vehicles communicate using tools from graph theory, and take communication losses and time delays into account. Finally, through a rigorous Lyapunov analysis, we provide performance bounds and demonstrate the efficacy of the algorithms with numerical and experimental results.

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This paper proposes a method for localization of avalanche victims by multiple UAVs. The method consists of three main parts. First, assuming that the UAVs and the victim are equipped with ARTVA receivers and a transmitter, respectively, we introduce a mathematical model of the receiver, which is used to estimate the position of the victim. Second, we derive a closed-form expression indicating the performance of this estimator. In particular, we show that the victim's observability index is captured by the persistency of excitation of a function of the UAVs trajectories. Third, we design and implement a motion planning algorithm that uses the estimation and the estimator's performance function for the (near) real-time generation of trajectories that guarantee feasible, safe, and time-efficient localization of avalanche victims.

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