23,226 research outputs found
Simultaneous velocity and position estimation via distance-only measurements with application to multi-agent system control
This paper proposes a strategy to estimate the velocity and position of
neighbor agents using distance measurements only. Since with agents executing
arbitrary motions, instantaneous distance-only measurements cannot provide
enough information for our objectives, we postulate that agents engage in a
combination of circular motion and linear motion. The proposed estimator can be
used to develop control algorithms where only distance measurements are
available to each agent. As an example, we show how this estimation method can
be used to control the formation shape and velocity of the agents in a multi
agent system. Simulation results are provided to illustrate the performance of
the proposed algorithm.This work was
supported by National ICT Australia, which is funded by the Australian
Research Council through the ICT Centre of Excellence program and
is also supported by the Australian Research Council under Grant
DP110100538
Least-square based recursive optimization for distance-based source localization
In this paper we study the problem of driving an agent to an unknown source
whose location is estimated in real-time by a recursive optimization algorithm.
The optimization criterion is subject to a least-square cost function
constructed from the distance measurements to the target combined with the
agent's self-odometry. In this work, two important issues concerning real world
application are directly addressed, which is a discrete-time recursive
algorithm for concurrent control and estimation, and consideration for input
saturation. It is proven that with proper choices of the system's parameters,
stability of all system states, including on-board estimator variables and the
agent-target relative position can be achieved. The convergence of the agent's
position to the target is also investigated via numerical simulation
A distributed optimization framework for localization and formation control: applications to vision-based measurements
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
A Decentralized Control Framework for Energy-Optimal Goal Assignment and Trajectory Generation
This paper proposes a decentralized approach for solving the problem of
moving a swarm of agents into a desired formation. We propose a decentralized
assignment algorithm which prescribes goals to each agent using only local
information. The assignment results are then used to generate energy-optimal
trajectories for each agent which have guaranteed collision avoidance through
safety constraints. We present the conditions for optimality and discuss the
robustness of the solution. The efficacy of the proposed approach is validated
through a numerical case study to characterize the framework's performance on a
set of dynamic goals.Comment: 6 pages, 3 figures, to appear at the 2019 Conference on Decision and
Control, Nice, F
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