8,546 research outputs found

    A distributed optimization framework for localization and formation control: applications to vision-based measurements

    Full text link
    Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures

    Bearing-only formation control with auxiliary distance measurements, leaders, and collision avoidance

    Full text link
    We address the controller synthesis problem for distributed formation control. Our solution requires only relative bearing measurements (as opposed to full translations), and is based on the exact gradient of a Lyapunov function with only global minimizers (independently from the formation topology). These properties allow a simple proof of global asymptotic convergence, and extensions for including distance measurements, leaders and collision avoidance. We validate our approach through simulations and comparison with other stateof-the-art algorithms.ARL grant W911NF-08-2-0004, ARO grant W911NF-13-1-0350, ONR grants N00014-07-1-0829, N00014-14-1-0510, N00014-15-1-2115, NSF grant IIS-1426840, CNS-1521617 and United Technologies

    Bearing-based formation control with second-order agent dynamics

    Full text link
    We consider the distributed formation control problem for a network of agents using visual measurements. We propose solutions that are based on bearing (and optionally distance) measurements, and agents with double integrator dynamics. We assume that a subset of the agents can track, in addition to their neighbors, a set of static features in the environment. These features are not considered to be part of the formation, but they are used to asymptotically control the velocity of the agents. We analyze the convergence properties of the proposed protocols analytically and through simulations.Published versionSupporting documentatio

    Controlling rigid formations of mobile agents under inconsistent measurements

    Get PDF
    Despite the great success of using gradient-based controllers to stabilize rigid formations of autonomous agents in the past years, surprising yet intriguing undesirable collective motions have been reported recently when inconsistent measurements are used in the agents' local controllers. To make the existing gradient control robust against such measurement inconsistency, we exploit local estimators following the well known internal model principle for robust output regulation control. The new estimator-based gradient control is still distributed in nature and can be constructed systematically even when the number of agents in a rigid formation grows. We prove rigorously that the proposed control is able to guarantee exponential convergence and then demonstrate through robotic experiments and computer simulations that the reported inconsistency-induced orbits of collective movements are effectively eliminated.Comment: 10 page

    Cooperative Control for Localization of Mobile Sensor Networks

    Get PDF
    In this paper, we consider the problem of cooperatively control a formation of networked mobile robots/vehicles to optimize the relative and absolute localization performance in 1D and 2D space. A framework for active perception is presented utilizing a graphical representation of sensory information obtained from the robot network. Performance measures are proposed that capture the estimate quality of team localization. We show that these measures directly depend on the sensing graph and shape of the formation. This dependence motivates implementation of a gradient based control scheme to adapt the formation geometry in order to optimize team localization performance. This approach is illustrated through application to a cooperative target localization problem involving a small robot team. Simulation results are presented using experimentally validated noise models
    • …
    corecore