652 research outputs found

    Formation control of nonholonomic mobile robots using implicit polynomials and elliptic Fourier descriptors

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    This paper presents a novel method for the formation control of a group of nonholonomic mobile robots using implicit and parametric descriptions of the desired formation shape. The formation control strategy employs implicit polynomial (IP) representations to generate potential fields for achieving the desired formation and the elliptical Fourier descriptors (EFD) to maintain the formation once achieved. Coordination of the robots is modeled by linear springs between each robot and its two nearest neighbors. Advantages of this new method are increased flexibility in the formation shape, scalability to different swarm sizes and easy implementation. The shape formation control is first developed for point particle robots and then extended to nonholonomic mobile robots. Several simulations with robot groups of different sizes are presented to validate our proposed approach

    Multi-robot hunting in dynamic environments

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    This paper is concerned with multi-robot hunting in dynamic environments. A BCSLA approach is proposed to allow mobile robots to capture an intelligent evader. During the process of hunting, four states including dispersion-random-search, surrounding, catch and prediction are employed. In order to ensure each robot appropriate movement in each state, a series of strategies are developed in this paper. The dispersion-search strategy enables the robots to find the evader effectively. The leader-adjusting strategy aims to improve the hunting robots&rsquo; response to environmental changes and the outflank strategy is proposed for the hunting robots to force the evader to enter a besieging circle. The catch strategy is designed for shrinking the besieging circle to catch the evader. The predict strategy allows the robots to predict the evader&rsquo;s position when they lose the tracking information about the evader. A novel collision-free motion strategy is also presented in this paper, which is called the direction-optimization strategy. To test the effect of cooperative hunting, the target to be captured owns a safety-motion strategy, which helps it to escape being captured. The computer simulations support the rationality of the approach.<br /

    Coordinated motion of UGVs and a UAV

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    Coordination of autonomous mobile robots has received significant attention during the last two decades. Coordinated motion of heterogenous robot groups are more appealing due to the fact that unique advantages of different robots might be combined to increase the overall efficiency of the system. In this paper, a heterogeneous robot group composed of multiple Unmanned Ground Vehicles (UGVs) and an Unmanned Aerial Vehicle (UAV) collaborate in order to accomplish a predefined goal. UGVs follow a virtual leader which is defined as the projection of UAV’s position onto the horizontal plane. The UAV broadcasts its position at certain frequency. The position of the virtual leader and distances from the two closest neighbors are used to create linear and angular velocity references for each UGV. Several coordinated tasks have been presented and the results are verified by simulations where certain amount of communication delay between the vehicles is also considered. Results are quite promising

    Adaptive Control in Swarm Robotic Systems

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    Inspired by the collective behavior observed in natural insects, swarm robotics is a new approach in designing control algorithms for a large group of robots performing a certain task. In such robotic systems, an individual robot with only limited capabilities in terms of sensing, computation, and communication can adapt its own behavior so that a desired collective behavior emerges from the local interactions among robots and between robots and the environment. Swarm robotics has been the focus of increased attention recently because of the beneficial features demonstrated in such systems, such as higher group efficiency, robustness against the failures of individual robots, flexibility to adapt to changes in the environment, and scalability over a wide range of group sizes. In this article we present an adaptive algorithm to regulate the behavior of an individual robot performing collective foraging tasks. Through the interactions between robots, a desired division of labor can be achieved at the group level. Robot groups also demonstrate the ability to improve energy efficiency and its potential robustness in different environments

    A Pursuit-Rendezvous Approach for Robotic Tracking

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    Information flow and cooperative control of vehicle formations

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    We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability

    Airship formation control

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    This paper addresses the problem underlying the control and coordination of multiple autonomous airships that must travel maintaining a desired geometric formation and simultaneously avoid collisions with moving or stationary obstacles. The control architecture is based on the attractor dynamics approach to behaviour generation. The airship physical model is presented and the mathematical background for the control architecture is explained. Simulations (with perturbations) with formations of two and three autonomous airships are presented in order to validate the architecture.(undefined
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