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In this paper, we propose a novel distributed consensus-based Kalman filtering (DCKF) with an information-weighted structure for estimation with random mobile targets in continuous-time (CT) systems. First, a novel information-flow structure for the measurement of moving targets is developed based on comprehensive information that includes sensing ranges, target mobility and local information-weighted neighbors. Then, novel necessary and sufficient conditions are given for the convergence of the proposed DCKF. Under these conditions, the estimates of all sensors for multiple targets converge to the consensus values. Finally, comparative simulation studies with the existing Kalman filters demonstrate the superior convergence performance of the new DCKF.

This paper discusses the problem of poweraware rendezvous, in which a collection of mobile agents must decide where and when to meet in order to do so in the least amount of time, given different available initial power levels. Moreover, the movements of the individual agents directly affect the power consumption, which means that the agents must meet in such a way that they do not run out of power. This problem is first approached as an optimal control problem and then implemented in a distributed manner on a team of simulated mobile robots.

This paper investigates how haptic interactions can be defined for enabling a single operator to control and interact with a team of mobile robots. Since there is no unique or canonical mapping from the swarm configuration to the forces experienced by the operator, a suitable mapping must be developed. To this end, multi-agent manipulability is proposed as a potentially useful mapping, whereby the forces experienced by the operator relate to how inputs, injected at precise locations in the team, translate to swarm-level motions. Small forces correspond to directions in which it is easy to move the swarm, while larger forces correspond to more costly directions. Initial experimental results support the viability of the proposed, haptic, human-swarm interaction mapping, through a user study where operators are tasked with driving a collection of robots through a series of way points.