275 research outputs found
A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking
This paper investigates non-myopic path planning of mobile sensors for
multi-target tracking. Such problem has posed a high computational complexity
issue and/or the necessity of high-level decision making. Existing works tackle
these issues by heuristically assigning targets to each sensing agent and
solving the split problem for each agent. However, such heuristic methods
reduce the target estimation performance in the absence of considering the
changes of target state estimation along time. In this work, we detour the
task-assignment problem by reformulating the general non-myopic planning
problem to a distributed optimization problem with respect to targets. By
combining alternating direction method of multipliers (ADMM) and local
trajectory optimization method, we solve the problem and induce consensus
(i.e., high-level decisions) automatically among the targets. In addition, we
propose a modified receding-horizon control (RHC) scheme and edge-cutting
method for efficient real-time operation. The proposed algorithm is validated
through simulations in various scenarios.Comment: Copyright 2019 IEEE. Personal use of this material is permitted.
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ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains
Today, the most widespread, widely applicable technology for gathering data
relies on experienced scientists armed with handheld radio telemetry equipment
to locate low-power radio transmitters attached to wildlife from the ground.
Although aerial robots can transform labor-intensive conservation tasks, the
realization of autonomous systems for tackling task complexities under
real-world conditions remains a challenge. We developed ConservationBots-small
aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial
robot achieves robust localization performance and fast task completion times
-- significant for energy-limited aerial systems while avoiding close
encounters with potential, counter-productive disturbances to wildlife. Our
approach overcomes the technical and practical problems posed by combining a
lightweight sensor with new concepts: i) planning to determine both trajectory
and measurement actions guided by an information-theoretic objective, which
allows the robot to strategically select near-instantaneous range-only
measurements to achieve faster localization, and time-consuming sensor rotation
actions to acquire bearing measurements and achieve robust tracking
performance; ii) a bearing detector more robust to noise and iii) a tracking
algorithm formulation robust to missed and false detections experienced in
real-world conditions. We conducted extensive studies: simulations built upon
complex signal propagation over high-resolution elevation data on diverse
geographical terrains; field testing; studies with wombats (Lasiorhinus
latifrons; nocturnal, vulnerable species dwelling in underground warrens) and
tracking comparisons with a highly experienced biologist to validate the
effectiveness of our aerial robot and demonstrate the significant advantages
over the manual method.Comment: 33 pages, 21 figure
Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application
In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks
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