This paper proposes a generalized algorithm that enables mobile agents to meet in a bounded region based only on bearing information of other agents within their vicinity. Each agent repeatedly employs a stop-and-go strategy consisting of the following three actions: (1) Estimate the bearing of agents in its vicinity, (2) compute a target point based on the estimates, and (3) move to that target point. The motivation and case study example is a modular robot, the Distributed Flight Array, which we employ to validate the proposed algorithm.

This paper discusses Algorithm A2 in “Asynchronous Distributed Averaging on Communication Networks” (IEEE Trans. Netw., vol. 15, no. 3, pp. 512-520, Jun. 2007), which claims to solve the distributed averaging problem provided that the parameters to the algorithm meet certain constraints. Specifically, the states of each node in the network are claimed to converge to the average of the initial values associated with the nodes under these constraints. This paper points out a flaw in the proof of the algorithm and in addition provides a specific example of a network, satisfying the assumptions, for which the algorithm does not converge.

This paper discusses distributed average consensus in the context of a distributed embedded system with multiple agents connected through a communication network. Adversities such as switching of network topologies, agents joining or leaving the network, and communication link creation or failure may arise in these systems. To address these difficulties, we propose an asynchronous implementation of a distributed average consensus algorithm that has the following properties: (1) unbiased average, (2) homogeneous implementation, (3) robustness to network adversities, (4) dynamic consensus, and (5) well-defined tuning parameters. We demonstrate an application of the implementation on a specific distributed embedded system, the Distributed Flight Array, where we solve two average consensus problems to estimate altitude and tilt of the vehicle from multiple distance measurements.

This paper describes a generalized method for computing the altitude and attitude of a rigid body with respect to an inertial frame using a set of distance measurements obtained from a sensor network. In the case where all sensors are centrally measurable, a linear-optimal estimate is obtained. This method is used as a way for estimating altitude and attitude of the Distributed Flight Array, a modular multi-propeller flying vehicle where each module in the array obtains its own distance measurement and coordinates with its immediate neighbour(s) actions for flight. To account for communication bandwidth constraints, a scalable, distributed scheme is presented where each module shares local information. In the limit of sharing information, each module asymptotically computes the linear-optimal altitude and attitude estimate.