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Register a new userCooperative Robot Localization Using Event-triggered Estimation
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Added by
Nisar Ahmed
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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This paper describes a novel communication-spare cooperative localization algorithm for a team of mobile unmanned robotic vehicles. Exploiting an event-based estimation paradigm, robots only send measurements to neighbors when the expected innovation for state estimation is high. Since agents know the event-triggering condition for measurements to be sent, the lack of a measurement is thus also informative and fused into state estimates. The robots use a Covariance Intersection (CI) mechanism to occasionally synchronize their local estimates of the full network state. In addition, heuristic balancing dynamics on the robots CI-triggering thresholds ensure that, in large diameter networks, the local error covariances remains below desired bounds across the network. Simulations on both linear and nonlinear dynamics/measurement models show that the event-triggering approach achieves nearly optimal state estimation performance in a wide range of operating conditions, even when using only a fraction of the communication cost required by conventional full data sharing. The robustness of the proposed approach to lossy communications, as well as the relationship between network topology and CI-based synchronization requirements, are also examined.
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We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Different from existing time-triggered communication schemes, an event-triggered asynchronous scheme is investigated in order to reduce communication while preserving estimation convergence. A distributed estimation algorithm with a single step size is first proposed based on an event-triggered communication scheme with a time-dependent decaying threshold. With the event-triggered scheme, each sensor sends its estimate to neighbor sensors only when the difference between the current estimate and the last sent-out estimate is larger than the triggering threshold. We prove that the proposed algorithm has mean-square and almost-sure convergence respectively, under an integrated condition of sensor network topology and sensor measurement matrices. The condition is satisfied if the topology is a balanced digraph containing a spanning tree and the system is collectively observable. Moreover, we provide estimates for the convergence rates, which are related to the step size as well as the triggering threshold. Furthermore, as an essential metric of sensor communication intensity in the event-triggered distributed algorithms, the communication rate is proved to decay to zero with a certain speed almost surely as time goes to infinity. We show that given the step size, adjusting the decay speed of the triggering threshold can lead to a tradeoff between the convergence rate of the estimation error and the decay speed of the communication rate. Specifically, increasing the decay speed of the threshold would make the communication rate decay faster, but reduce the convergence rate of the estimation error. Numerical simulations are provided to illustrate the developed results.
We present a novel path-planning algorithm to reduce localization error for a network of robots cooperatively localizing via inter-robot range measurements. The quality of localization with range measurements depends on the configuration of the network, and poor configurations can cause substantial localization errors. To reduce the effect of network configuration on localization error for moving networks we consider various optimality measures of the Fisher information matrix (FIM), which have well-studied relationships with the localization error. In particular, we pose a trajectory planning problem with constraints on the FIM optimality measures. By constraining these optimality measures we can control the statistical properties of the localization error. To efficiently generate trajectories which satisfy these FIM constraints we present a prioritized planner which leverages graph-based planning and unique properties of the range-only FIM. We show results in simulated experiments that demonstrate the trajectories generated by our algorithm reduce worst-case localization error by up to 42% in comparison to existing planning approaches and can scalably plan distance-efficient trajectories in complicated environments for large numbers of robots.
General nonlinear continuous-time systems are considered for which the state is to be estimated via a packet-based communication network. We assume that the system has multiple sensor nodes, affected by measurement noise, which can transmit output data at discrete (non-equidistant) and asynchronous points in time. For this general system setup, we develop a state estimation framework, where the transmission instances of the individual sensor nodes can be generated in both time-triggered and event-triggered fashions. In the latter case, we guarantee the absence of Zeno behavior by construction. It is shown that, under the provided design conditions, an input-to-state stability property is obtained for the estimation error and that the state is thus reconstructed asymptotically in the absence of noise. A numerical case study shows the strengths of the developed framework.
In this paper, we develop a textcolor{black}{emph{distributed}} algorithm to localize a network of robots moving arbitrarily in a bounded region. In the case of such mobile networks, the main challenge is that the robots may not be able to find nearby robots to implement a distributed algorithm. We address this issue by providing an opportunistic algorithm that only implements a location update when there are nearby robots and does not update otherwise. We assume that each robot measures a noisy version of its motion and the distances to the nearby robots. To localize a network of mobile robots in~$mathbb{R}^m$, we provide a simple emph{linear} update, which is based on barycentric coordinates and is linear-convex. We abstract the corresponding localization algorithm as a Linear Time-Varying (LTV) system and show that it asymptotically converges to the true locations~of~the robots. We first focus on the noiseless case, where the distance and motion vectors are known (measured) perfectly, and provide sufficient conditions on the convergence of the algorithm. We then evaluate the performance of the algorithm in the presence of noise and provide modifications to counter the undesirable effects of noise. textcolor{black}{We further show that our algorithm precisely tracks a mobile network as long as there is at least one known beacon (a node whose location is perfectly known).
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