Keeping Mobile Robots Connected

Designing robust algorithms for mobile agents with reliable communication is difficult due to the distributed nature of computation, in mobile ad hoc networks (MANETs) the matter is exacerbated by the need to ensure connectivity. Existing distributed algorithms provide coordination but typically assume connectivity is ensured by other means. We present a connectivity service that encapsulates an arbitrary motion planner and can refine any plan to preserve connectivity (the graph of agents remains connected) and ensure progress (the agents advance towards their goal). The service is realized by a distributed algorithm that is modular in that it makes no assumptions of the motion-planning mechanism except the ability for an agent to query its position and intended goal position, local in that it uses 1-hop broadcast to communicate with nearby agents but doesn't need any network routing infrastructure, and \emph{oblivious} in that it does not depend on previous computations. We prove the progress of the algorithm in one round is at least Omega(min(d,r)), where d is the minimum distance between an agent and its target and r is the communication radius. We characterize the worst case configuration and show that when d >= r this bound is tight and the algorithm is optimal, since no algorithm can guarantee greater progress. Finally we show all agents get epsilon-close to their targets within O(D_0/r+n^2/epsilon) rounds where n is the number of agents and D_0 is the initial distance to the targets

Local distributed algorithms for multi-robot systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 165-173) and index.The field of swarm robotics focuses on controlling large populations of simple robots to accomplish tasks more effectively than what is possible using a single robot. This thesis develops distributed algorithms tailored for multi-robot systems with large populations. Specifically we focus on local distributed algorithms since their performance depends primarily on local parameters on the system and are guaranteed to scale with the number of robots in the system. The first part of this thesis considers and solves the problem of finding a trajectory for each robot which is guaranteed to preserve the connectivity of the communication graph, and when feasible it also guarantees the robots advanced towards a goal defined by an arbitrary motion planner. We also describe how to extend our proposed approach to preserve the k-connectivity of a communication graph. Finally, we show how our connectivity-preserving algorithm can be combined with standard averaging procedures to yield a provably correct flocking algorithm. The second part of this thesis considers and solves the problem of having each robot localize an arbitrary subset of robots in a multi-robot system relying only on sensors at each robot that measure the angle, relative to the orientation of each robot, towards neighboring robots in the communication graph. We propose a distributed localization algorithm that computes the relative orientations and relative positions, up to scale, of an arbitrary subset of robots. For the case when the robots move in between rounds we show how to use odometry information to allow each robot to compute the relative positions complete with scale, of an arbitrary subset of robots. Finally we describe how to use the our localization algorithm to design a variety of multi-robot tasks.by Alejandro Cornejo.Ph.D

Connectivity Service for Mobile Ad-Hoc Networks

Abstract—We present a distributed connectivity service that allows agents in a mobile ad-hoc network to move while preserving connectivity. This allows unmodified motion planning algorithms to control the trajectories of each robot independently, these trajectories are fed to the service which modifies them as little as possible to ensuring global connectivity. Since we require only short term targets the service can be used with online motion planning where the complete trajectory is not known a priori. For most motions the algorithm requires only local knowledge of the graph, and therefore scales up with the number of agents. I