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26
Multi-robot active target tracking with combinations of relative observations
, 2010
"... Abstract — In this paper, we study the problem of optimal trajectory generation for a team of mobile robots tracking a moving target using distance and bearing measurements. Contrary to previous approaches, we explicitly consider limits on the robots ’ speed and impose constraints on the minimum dis ..."
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Cited by 27 (3 self)
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Abstract — In this paper, we study the problem of optimal trajectory generation for a team of mobile robots tracking a moving target using distance and bearing measurements. Contrary to previous approaches, we explicitly consider limits on the robots ’ speed and impose constraints on the minimum distance at which the robots are allowed to approach the target. We first address the case of a single sensor and show that although this problem is non-convex with non-convex constraints, in general, its optimal solution can be determined analytically. Moreover, we extend this approach to the case of multiple sensors and propose an iterative algorithm, Gauss-Seidel-relaxation (GSR), for determining the set of feasible locations that each sensor should move to in order to minimize the uncertainty about the position of the target. Extensive simulation results are presented demonstrating that the performance of the GSR algorithm, whose computational complexity is linear in the number of sensors, is indistinguishable of that of a grid-based exhaustive search, with cost exponential in the number of sensors, and significantly better than that of a random, towards the target, motion strategy. I.
Inter-robot Transformations in 3D
"... In this paper, we provide a study of motion-induced 3D extrinsic calibration based on robot-to-robot sensor measurements. In particular, we introduce algebraic methods to compute the relative translation and rotation between two robots using known robot motion and robot-to-robot (i) distance and bea ..."
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Cited by 15 (6 self)
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In this paper, we provide a study of motion-induced 3D extrinsic calibration based on robot-to-robot sensor measurements. In particular, we introduce algebraic methods to compute the relative translation and rotation between two robots using known robot motion and robot-to-robot (i) distance and bearing, (ii) bearing-only, and (iii) distance-only measurements. We further conduct a nonlinear observability analysis and provide sufficient conditions for the 3D relative position and orientation (pose) to become locally weakly observable. Finally, we present a nonlinear weighted least squares estimator to refine the algebraic pose estimate in the presence of noise. We use simulations to evaluate the performance of our methods in terms of accuracy and robustness.
On Active Target Tracking and Cooperative Localization for Multiple Aerial Vehicles
"... Abstract — This paper presents a new cooperative active target-tracking strategy for a team of double-integrator aerial vehicles equipped with 3-D range-finding sensors. Our strategy is active because it moves the vehicles along paths that minimize the combined uncertainty about the target’s positio ..."
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Cited by 8 (6 self)
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Abstract — This paper presents a new cooperative active target-tracking strategy for a team of double-integrator aerial vehicles equipped with 3-D range-finding sensors. Our strategy is active because it moves the vehicles along paths that minimize the combined uncertainty about the target’s position. We propose a gradient-based control approach that encompasses the three major optimum experimental-design criteria and relies on the Kalman filter for estimation fusion. We derive analytical lower and upper bounds on the target’s position uncertainty by exploiting the monotonicity property of the Riccati differential equation arising from the Kalman-Bucy filter. These bounds allow us to study the impact of sensors ’ accuracy and target’s dynamics on the steady-state performance of our coordination algorithm. Finally, in the case that the position of the vehicles is not perfectly known, we introduce a more challenging problem, termed Active Cooperative Localization and Multitarget Tracking (ACLMT). In this problem, the vehicles move in the 3-D space in order to maximize the accuracy of their own position estimate and that of multiple moving targets. I.
On the Global Optimum of Planar, Range-based Robot-to-Robot Relative Pose Estimation
"... In this paper, we address the problem of determining the relative position and orientation (pose) of two robots navigating in 2D, based on known egomotion and noisy robot-to-robot distance measurements. We formulate this as a weighted Least Squares (WLS) estimation problem, and determine the exact ..."
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Cited by 8 (3 self)
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In this paper, we address the problem of determining the relative position and orientation (pose) of two robots navigating in 2D, based on known egomotion and noisy robot-to-robot distance measurements. We formulate this as a weighted Least Squares (WLS) estimation problem, and determine the exact global optimum by directly solving the multivariate polynomial system resulting from the first-order optimality conditions. Given the poor scalability of the original WLS problem, we propose an alternative formulation of the WLS problem in terms of squared distance measurements (squared distances WLS or SD-WLS). Using a hybrid algebraic-numeric technique, we are able to solve the corresponding first-order optimality conditions of the SD-WLS in 125 ms in Matlab. Both methods solve the minimal (3 distance measurements) as well as the overdetermined problem (more than 3 measurements) in a
Active sensing for range-only mapping using multiple hypothesis
- In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
, 2010
"... Abstract — Radio signal-based localization and mapping is becoming more interesting in robotics as applications involving the collaboration between robots and static wireless devices are more common. This paper describes a method for mapping with a mobile robot the position of a set of nodes using r ..."
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Cited by 5 (3 self)
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Abstract — Radio signal-based localization and mapping is becoming more interesting in robotics as applications involving the collaboration between robots and static wireless devices are more common. This paper describes a method for mapping with a mobile robot the position of a set of nodes using radio signal measurements. The method employs Gaussian Mixtures Models (GMM) for undelayed initialization of the position of the wireless nodes within a Kalman filter. Moreover, the paper extends the method to consider active sensing strategies in order to map the nodes. Entropy variation is used as a measurement of information gain, and allows to prioritize control actions of the robot. However, as there is no analytical expression for the entropy of a GMM, upper bounds of the entropy, for which close form computation is possible, are used instead. The paper describes simulations that show the feasibility of the approach. I.
COOPERATIVE LOCALIZATION: ON MOTION-INDUCED INITIALIZATION AND JOINT STATE ESTIMATION UNDER COMMUNICATION CONSTRAINTS
"... This thesis would not have been possible without the support of a number of people. First of all, my thanks go to my adviser, Professor Stergios Roumeliotis, for his constant encouragement and guidance, for the long hours of passing along his knowledge and experience, for pushing me beyond my own li ..."
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Cited by 3 (0 self)
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This thesis would not have been possible without the support of a number of people. First of all, my thanks go to my adviser, Professor Stergios Roumeliotis, for his constant encouragement and guidance, for the long hours of passing along his knowledge and experience, for pushing me beyond my own limitations, and for his seemingly endless supply of interesting research problems. I am also thankful for the time and invaluable advice from
Distributed multi-target tracking via mobile robotic networks: a localized non-iterative SDP approach
- In Proceedings of the 50th IEEE conference on decision and control and European control conference
, 2011
"... Abstract — We consider a robotic network composed of mo-bile robots capable of communicating with each other. We study the problem of collectively tracking a number of moving targets while maintaining a certain level of connectivity among the robots, by moving them into appropriate positions. The di ..."
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Abstract — We consider a robotic network composed of mo-bile robots capable of communicating with each other. We study the problem of collectively tracking a number of moving targets while maintaining a certain level of connectivity among the robots, by moving them into appropriate positions. The distances of the robots to each other and to the targets are used to define a communication and target tracking graph, respectively. We formulate the combined global objective as a Semi-Definite Program (SDP) and propose a non-iterative distributed solution consisting of localized SDP’s which use information only from nearby neighboring robots. Numerical simulations illustrate the performance of the algorithm with respect to the centralized solution. I.
Active Target Tracking and Cooperative Localization for Teams of Aerial Vehicles
"... Abstract—This paper studies the active target-tracking prob-lem for a team of unmanned aerial vehicles equipped with 3-D range-finding sensors. We propose a gradient-based control strategy that encompasses the three major optimum experimental design criteria, and we use the Kalman filter for estimat ..."
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Cited by 3 (0 self)
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Abstract—This paper studies the active target-tracking prob-lem for a team of unmanned aerial vehicles equipped with 3-D range-finding sensors. We propose a gradient-based control strategy that encompasses the three major optimum experimental design criteria, and we use the Kalman filter for estimating the target’s position both in a cooperative and in a noncooperative scenario. Our control strategy is active because it moves the vehicles along paths that minimize the uncertainty about the location of the target. In the case that the position of the vehicles is not perfectly known, we introduce a new and more challenging problem, termed “Active Cooperative Localization and Multi-target Tracking ” (ACLMT). In this problem, the aerial vehicles must reconfigure themselves in the 3-D space in order to maximize both the accuracy of their own position estimate and that of multiple moving targets. For ACLMT, we derive analytical lower and upper bounds on the targets ’ and vehicles ’ position uncertainty by exploiting the monotonicity property of the Riccati differential equation arising from the Kalman-Bucy filter. These bounds allow us to study the impact of sensors ’ accuracy and targets ’ dynamics on the performance of our coordination strategy. Extensive simulation experiments illustrate the proposed theoretical results. Index Terms—Active sensing, mobile sensors, unmanned aerial vehicles, target tracking, cooperative localization, Kalman filtering I.
Bearing-only Target Tracking using a Bank of MAP Estimators
"... Abstract — Nonlinear estimation problems, such as bearingonly tracking, are often addressed using linearized estimators, e.g., the extended Kalman filter (EKF). These estimators generally suffer from linearization errors as well as the inability to track multimodal probability density functions (pdf ..."
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Cited by 2 (1 self)
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Abstract — Nonlinear estimation problems, such as bearingonly tracking, are often addressed using linearized estimators, e.g., the extended Kalman filter (EKF). These estimators generally suffer from linearization errors as well as the inability to track multimodal probability density functions (pdfs). In this paper, we propose a bank of batch maximum a posteriori (MAP) estimators as a general estimation framework that provides relinearization of the entire state history, multi-hypothesis tracking, and an efficient hypothesis generation scheme. Each estimator in the bank is initialized using a locally optimal state estimate for the current time step. Every time a new measurement becomes available, we convert the nonlinear cost function corresponding to this relaxed one-step subproblem into polynomial form, allowing to analytically and efficiently compute all stationary points. This local optimization generates highly probable hypotheses for the target trajectory and greatly improves the quality of the overall MAP estimate. Additionally, pruning and marginalization are employed to control the computational cost. Monte Carlo simulations and real-world experiments show that the proposed approach significantly outperforms the EKF, the standard batch MAP estimator, and the particle filter (PF), in terms of accuracy and consistency. I.
A Bank of Maximum A Posteriori Estimators for Single-Sensor Range-only Target Tracking
"... Abstract — In this paper, we study estimation consistency of single-sensor target tracking using range-only measurements. We show analytically that the cost function minimized by the iterated extended Kalman filter (IEKF) has up to three local minima, which can potentially result in inconsistency or ..."
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Abstract — In this paper, we study estimation consistency of single-sensor target tracking using range-only measurements. We show analytically that the cost function minimized by the iterated extended Kalman filter (IEKF) has up to three local minima, which can potentially result in inconsistency or even divergence. To address this issue, we instead propose a bank of maximum a posteriori (MAP) estimators to determine the target state-space trajectory. In particular, we use the local minima of the IEKF cost function at each time step as highly accurate initial hypotheses to start a bank of iterative nonlinear optimizations. Moreover, we employ pruning and marginalization to control computational complexity. Extensive Monte Carlo simulations show that the proposed algorithm significantly outperforms the IEKF, the unscented Kalman filter (UKF), the bank of IEKFs, the particle filter (PF), and the standard MAP, both in terms of accuracy and convergence speed. I.
