196,904 research outputs found
Bio-inspired vision-based leader-follower formation flying in the presence of delays
Flocking starlings at dusk are known for the mesmerizing and intricate shapes they generate, as well as how fluid these shapes change. They seem to do this effortlessly. Real-life vision-based flocking has not been achieved in micro-UAVs (micro Unmanned Aerial Vehicles) to date. Towards this goal, we make three contributions in this paper: (i) we used a computational approach to develop a bio-inspired architecture for vision-based Leader-Follower formation flying on two micro-UAVs. We believe that the minimal computational cost of the resulting algorithm makes it suitable for object detection and tracking during high-speed flocking; (ii) we show that provided delays in the control loop of a micro-UAV are below a critical value, Kalman filter-based estimation algorithms are not required to achieve Leader-Follower formation flying; (iii) unlike previous approaches, we do not use external observers, such as GPS signals or synchronized communication with flock members. These three contributions could be useful in achieving vision-based flocking in GPS-denied environments on computationally-limited agents
Dynamic coordinated control laws in multiple agent models
We present an active control scheme of a kinetic model of swarming. It has
been shown previously that the global control scheme for the model, presented
in \cite{JK04}, gives rise to spontaneous collective organization of agents
into a unified coherent swarm, via a long-range attractive and short-range
repulsive potential. We extend these results by presenting control laws whereby
a single swarm is broken into independently functioning subswarm clusters. The
transition between one coordinated swarm and multiple clustered subswarms is
managed simply with a homotopy parameter. Additionally, we present as an
alternate formulation, a local control law for the same model, which implements
dynamic barrier avoidance behavior, and in which swarm coherence emerges
spontaneously.Comment: 20 pages, 6 figure
A Distributed Epigenetic Shape Formation and Regeneration Algorithm for a Swarm of Robots
Living cells exhibit both growth and regeneration of body tissues. Epigenetic
Tracking (ET), models this growth and regenerative qualities of living cells
and has been used to generate complex 2D and 3D shapes. In this paper, we
present an ET based algorithm that aids a swarm of identically-programmed
robots to form arbitrary shapes and regenerate them when cut. The algorithm
works in a distributed manner using only local interactions and computations
without any central control and aids the robots to form the shape in a
triangular lattice structure. In case of damage or splitting of the shape, it
helps each set of the remaining robots to regenerate and position themselves to
build scaled down versions of the original shape. The paper presents the shapes
formed and regenerated by the algorithm using the Kilombo simulator.Comment: 8 pages, 9 figures, GECCO-18 conferenc
Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks
A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned
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
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