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People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters
- The International Journal of Robotics Research
"... One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probab ..."
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Cited by 175 (17 self)
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One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects. Our method applies Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot. The approach has been implemented and tested on a real robot using laser-range data. We present experiments illustrating that our algorithm is able to robustly keep track of multiple persons. The experiments furthermore show that the approach outperforms other techniques developed so far. 1
Learning motion patterns of people for compliant robot motion
, 2004
"... Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve ..."
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Cited by 105 (3 self)
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Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve its behavior. In this paper we propose a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders are clustered using the expectation maximization algorithm. Based on the result of the clustering process, we derive a hidden Markov model that is applied to estimate the current and future positions of persons based on sensory input. We also describe how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot. We present several experiments carried out in different environments with a mobile robot equipped with a laser-range scanner and a camera system. The results demonstrate that our approach can reliably learn motion patterns of persons, can robustly estimate and predict positions of persons, and can be used to improve the navigation behavior of a mobile robot.
Motion Planning for Multitarget Surveillance with Mobile Sensor Agents
- IEEE Transactions on Robotics
, 2005
"... Abstract—In the surveillance of multiple targets by mobile sensor agents (MSAs), system performance relies greatly on the motion-control strategy of the MSAs. This paper investigates the motion-planning problem for a limited resource of MSAs in an environment of targets (). The kinematics of the MSA ..."
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Cited by 41 (0 self)
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Abstract—In the surveillance of multiple targets by mobile sensor agents (MSAs), system performance relies greatly on the motion-control strategy of the MSAs. This paper investigates the motion-planning problem for a limited resource of MSAs in an environment of targets (). The kinematics of the MSA is modeled as a point mass moving at a constant speed with a bounded turning radius. Based on the fact that the track information of each target degrades over time, the motion-plan-ning problem is formulated as an optimization problem whose objective is to minimize the average time duration between two consecutive observations of each target. In the case of a single MSA, the motion-planning problem is further interpreted so as to find a time-optimal loop path to traverse the targets. A gradient-approximation algorithm is then proposed to generate a suboptimal loop path for a mobile agent to traverse a sequence of target points. For the multi-MSA-multitarget case, a cooperative online motion-planning approach is developed. Index Terms—Cooperative motion control, mobile sensor agents (MSAs), multi-MSA-multitarget (MMMT) tracking, robot motion planning, sensor management, uninhabited air vehicles (UAVs). I.
Target enumeration via Euler characteristic integrals I: sensor fields
, 2007
"... We solve the problem of counting the total number of observable targets (e.g., persons, vehicles, landmarks) in a region using local counts performed by a dense field of sensors, each of which measures the number of targets nearby but not their identities nor any positional information. We formulat ..."
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Cited by 30 (11 self)
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We solve the problem of counting the total number of observable targets (e.g., persons, vehicles, landmarks) in a region using local counts performed by a dense field of sensors, each of which measures the number of targets nearby but not their identities nor any positional information. We formulate and solve several such problems based on the types of sensors and mobility of the targets. The main contribution of this paper is the adaptation of a topological integration theory — integration with respect to Euler characteristic — to yield complete solutions to these problems.
A Multi-Agent Simulation for Assessing Massive Sensor Deployment
- JOURNAL OF BATTLEFIELD TECHNOLOGY
, 2004
"... We present the design and implementation of a multi-agent simulation that models deployment and coverage of sensors performing collaborative target detection. The focus is on sensor networks with enough sensors that humans cannot individually manage each. Experiments evaluated both known and novel d ..."
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Cited by 11 (3 self)
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We present the design and implementation of a multi-agent simulation that models deployment and coverage of sensors performing collaborative target detection. The focus is on sensor networks with enough sensors that humans cannot individually manage each. Experiments evaluated both known and novel deployment algorithms, and considered effects of the sensor type, number of sensors deployed, presence of obstacles, and mobility of the sensors. A particular focus was barrier (traversal) coverage which has many military applications but which has been less studied than other sensor placement problems; experiments showed that good algorithms for it are different than those good for area monitoring. This work provides both useful data for guiding sensor deployment and a valuable testbed for planning of sensor networks.
Multi-agent simulations (mas) for assessing massive sensor coverage and deployment
, 2003
"... Approved for public release; distribution is unlimited ..."
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Cited by 3 (0 self)
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Approved for public release; distribution is unlimited
Following a large unpredictable group of targets among obstacles
- Proceedings of the International Conference on Motion in Games (MiG
, 2010
"... Camera control is essential in both virtual and real-world environments. Our work focuses on an instance of camera control called target following, and offers an algorithm, based on the ideas of monotonic tracking regions and ghost targets, for following a large coherent group of targets with unknow ..."
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Cited by 3 (2 self)
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Camera control is essential in both virtual and real-world environments. Our work focuses on an instance of camera control called target following, and offers an algorithm, based on the ideas of monotonic tracking regions and ghost targets, for following a large coherent group of targets with unknown trajectories, among known obstacles. In multiple-target following, the camera’s primary objective is to follow and maximize visibility of multiple moving targets. For example, in video games, a thirdperson view camera may be controlled to follow a group of characters through complicated virtual environments. In robotics, a camera attached to robotic manipulators could also be controlled to observe live performers in a concert, monitor assembly of a mechanical system, or maintain task visibility during teleoperated surgical procedures. To the best of our knowledge, this work is the first attempting to address this particular instance of camera control. 1
Active Localization of People with a Mobile Robot Based on Learned Motion Behaviors
"... Mobile robots that provide service to people can carry out their tasks more efficiently if they know where the people are. In this paper we present an approach to actively maintain a probabilistic belief about the current locations of people in the environment of a mobile robot. We assume that the r ..."
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Cited by 1 (0 self)
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Mobile robots that provide service to people can carry out their tasks more efficiently if they know where the people are. In this paper we present an approach to actively maintain a probabilistic belief about the current locations of people in the environment of a mobile robot. We assume that the robot is equipped with knowledge about typical motion behaviors of people in form of Hidden Markov Models (HMMs), which are updated based on vision and laser information. While the robot is carrying out its task it applies a decision-theoretic approach to actively select points in the environment that are expected to provide information about the positions of people. Experimental results obtained with a mobile robot in a typical office environment illustrate that our method decreases the uncertainty about the positions of people compared to passive approaches which do not consider additional observation actions. 1
TARGET ENUMERATION IN SENSOR NETWORKS VIA INTEGRATION WITH RESPECT TO EULER CHARACTERISTIC ∗
"... Abstract. We solve the problem of counting the total number of observable targets (e.g., persons, vehicles, etc.) in a region based on local counts performed by a network of sensors, each of which measures the number of targets nearby but not their identities nor any positional information. We formu ..."
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Cited by 1 (0 self)
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Abstract. We solve the problem of counting the total number of observable targets (e.g., persons, vehicles, etc.) in a region based on local counts performed by a network of sensors, each of which measures the number of targets nearby but not their identities nor any positional information. We formulate several such problems based on the types of sensors and mobility of the targets. The main contribution of this paper is the adaptation of a topological integration theory — integration with respect to Euler characteristic — to yield complete solutions to these problems. 1. Introduction. The