Applying swarm intelligence to actual swarm robotic systems is a challenging task especially with adequately consideration of corresponding practical constraints. Under the restrictions of the fieldof-view limited relative positioning, local sensing and communication, kinematic limitations as well as anti-collision issues, this paper presents a constrained particle swarm optimization (PSO) based collaborative searching method for robotic swarms. Besides, the proposed method follows the concept of evolution speed and a modified aggregation degree to determine the adaptive weights in the robotic PSO model. The modified aggregation degree is associated with the number of members in one's field-of-view. Unlike the traditional position update method, the proposed method updates the forward speed and angular velocity of the robot using the non-holonomic model to realize the motion control of each robot. The simulation results show that the proposed method has the potential for the practical implementation of collaborative searching tasks for robotic swarms in different types of environments.
This paper focuses on the problem of the exponential synchronizationlike criteria for state-dependent impulsive dynamical networks (SIDNs). Two types of sufficient conditions, which are applied to ensure every solution intersecting each impulsive surface exactly once, are derived. For each type of collision conditions, combining with comparison principle and inequality techniques, some sufficient conditions are obtained to ensure local exponential synchronizationlike for SIDN. Moreover, a quiet different impulsive strategy concerning the trigger rules of impulsive instants is proposed. Finally, an example is given to demonstrate the effectiveness of our results.
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