Proceedings of the Fourth International Conference on Autonomous Agents 2000
DOI: 10.1145/336595.336607
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A team of robotic agents for surveillance

Abstract: This paper presents the hardware and software components of a robotic team designed for security and surveillance applications. The team consists of two types of robotic agents. The first type is a larger, heavy-duty robotic platform, called the "ranger." Rangers are used to transport, deploy, and supervise a number of small, mobile sensor platforms called "scouts," the second type of robotic agent. In an example scenario, the scouts are deployed into an office/lab environment, navigate towards dark areas, and… Show more

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Cited by 64 publications
(39 citation statements)
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“…2 > e , where g −1ġ i are tangent vectors at the identity element e (the 3 × 3 identity homogeneous transformation) and therefore lie in se (2). A metric defined in this way is a left-invariant Riemannian metric.…”
Section: Motion Planning In the Abstract Spacementioning
confidence: 99%
See 1 more Smart Citation
“…2 > e , where g −1ġ i are tangent vectors at the identity element e (the 3 × 3 identity homogeneous transformation) and therefore lie in se (2). A metric defined in this way is a left-invariant Riemannian metric.…”
Section: Motion Planning In the Abstract Spacementioning
confidence: 99%
“…Effective strategies for controlling large teams of robots in complex environments are becoming increasingly relevant as the development of pervasive embedded computing, sensing, and wireless communication enables the application of multiagent systems to challenging tasks such as environmental monitoring [1], surveillance and reconnaissance for security and defense [2], and support for first responders in a search and rescue operation [3]. In such scenarios, it is necessary to apply control strategies that allow robots to adapt to different environments and execute complex tasks, while avoiding collisions.…”
Section: Introductionmentioning
confidence: 99%
“…In other words, the group objective is obtained if all rewards are collected, regardless of whether one agent incurs all rewards, or each agent incurs some rewards. For example, in a reconnaissance mission [69,70], or map building mission [60,73,78], if an agent observed and assessed the situation in one part of the environment, there is no reward for a second agent observing or mapping the same part of the environment. We find a second example in formation flight [3,27,28,68].…”
Section: The Collaborative Search For Individually Rewarding Resourcesmentioning
confidence: 99%
“…Other researchers assume a circle shaped local observation around each agent's current position. We find many examples in map building [66,69,73,75] and search problems [65]. In [52], the authors use a scheme whereby each agent observes the state of the agents located within a certain radius R, and prove stability in the sense that all agents reach a consensus on the heading to adopt.…”
Section: Local Observationmentioning
confidence: 99%
“…From the late 1980s, when the IRIS (industrial remote inspection system) robot performed inspection tasks in nuclear plants, 2,3 to current commercial robotic surveillance solutions (e.g., the patrolling of South Korean's Pohang prison by Roboguard 4 ), security robots have been successfully deployed. In this context, in the most advanced sensing systems, not only ground robots [4][5][6] but unmanned aerial vehicles (UAVs), have been incorporated as part of security and surveillance multirobot systems (MRSs). 7,8,9 Examples of these platforms are the AirRobot's AR100-B (http://www.airrobot.com/index.php/products-28.html) and Astec's Pelican (http://www.asctec.de/asctec-pelican-3/) UAVs.…”
Section: Introductionmentioning
confidence: 99%