48,993 research outputs found
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
Multi-Robot Patrol Algorithm with Distributed Coordination and Consciousness of the Base Station's Situation Awareness
Multi-robot patrolling is the potential application for robotic systems to
survey wide areas efficiently without human burdens and mistakes. However, such
systems have few examples of real-world applications due to their lack of human
predictability. This paper proposes an algorithm: Local Reactive (LR) for
multi-robot patrolling to satisfy both needs: (i)patrol efficiently and
(ii)provide humans with better situation awareness to enhance system
predictability. Each robot operating according to the proposed algorithm
selects its patrol target from the local areas around the robot's current
location by two requirements: (i)patrol location with greater need, (ii)report
its achievements to the base station. The algorithm is distributed and
coordinates the robots without centralized control by sharing their patrol
achievements and degree of need to report to the base station. The proposed
algorithm performed better than existing algorithms in both patrolling and the
base station's situation awareness.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
GPRL: Gaussian Processes-Based Relative Localization for Multi-Robot Systems
Relative localization is crucial for multi-robot systems to perform
cooperative tasks, especially in GPS-denied environments. Current techniques
for multi-robot relative localization rely on expensive or short-range sensors
such as cameras and LIDARs. As a result, these algorithms face challenges such
as high computational complexity, dependencies on well-structured environments,
etc. To overcome these limitations, we propose a new distributed approach to
perform relative localization using a Gaussian Processes map of the Radio
Signal Strength Indicator (RSSI) values from a single wireless Access Point
(AP) to which the robots are connected. Our approach, Gaussian Processes-based
Relative Localization (GPRL), combines two pillars. First, the robots locate
the AP w.r.t. their local reference frames using novel hierarchical inferencing
that significantly reduces computational complexity. Secondly, the robots
obtain relative positions of neighbor robots with an AP-oriented vector
transformation. The approach readily applies to resource-constrained devices
and relies only on the ubiquitously-available RSSI measurement. We extensively
validate the performance of the two pillars of the proposed GRPL in Robotarium
simulations. We also demonstrate the applicability of GPRL through a
multi-robot rendezvous task with a team of three real-world robots. The results
demonstrate that GPRL outperformed state-of-the-art approaches regarding
accuracy, computation, and real-time performance
Algorithms for Rapidly Dispersing Robot Swarms in Unknown Environments
We develop and analyze algorithms for dispersing a swarm of primitive robots
in an unknown environment, R. The primary objective is to minimize the
makespan, that is, the time to fill the entire region. An environment is
composed of pixels that form a connected subset of the integer grid.
There is at most one robot per pixel and robots move horizontally or
vertically at unit speed. Robots enter R by means of k>=1 door pixels
Robots are primitive finite automata, only having local communication, local
sensors, and a constant-sized memory.
We first give algorithms for the single-door case (i.e., k=1), analyzing the
algorithms both theoretically and experimentally. We prove that our algorithms
have optimal makespan 2A-1, where A is the area of R.
We next give an algorithm for the multi-door case (k>1), based on a
wall-following version of the leader-follower strategy. We prove that our
strategy is O(log(k+1))-competitive, and that this bound is tight for our
strategy and other related strategies.Comment: 17 pages, 4 figures, Latex, to appear in Workshop on Algorithmic
Foundations of Robotics, 200
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
- …