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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.

In this work, we incorporate trust into the interaction dynamics for multi-agent systems in order to analyze the effects of trust on human-robot teams. We extend previous work by moving away from a 'self-centered' trust model and introduce a so-called 'team-oriented' trust model in which each agent increases its trust for its neighbors only if they are collectively contributing to the team's overall goal. The coupling of the trust dynamics and the agents' state dynamics is shown to give rise to an intricate relationship that has the potential to make the team's performance deteriorate under certain circumstances. We derive conditions under which the multi-agent system is guaranteed to achieve a collective objective and provide simulations to corroborate the theoretical findings.

This paper explores how haptic interfaces should be designed to enable effective human-swarm interactions. When a single operator is interacting with a team of mobile robots, there are certain properties of the team that may help the operator complete the task at hand if these properties were fed back via haptics. However, not all team-level properties may be particularly well-suited for haptic feedback. In this paper, characteristics that make a property of a multi-agent system appropriate for haptic feedback are defined. The focus here is on leader-follower networks, in which one robot, the so-called leader, is controlled via an operator with a haptic device, whereas the remaining robots, the so-called followers, are tasked with maintaining distances between one another. Multi-agent manipulability, a property which describes how effective the leader is at controlling the movement of the followers, is proposed as one such appropriate property for haptic feedback in a human-swarm interaction scenario. Manipulability feedback is implemented using a PHANTOM Omni haptic joystick and experiments in which a team of mobile robots is controlled via a human operator with access to this feedback show that this is viable in practice.

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.