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An Incremental Self-Deployment Algorithm for Mobile Sensor Networks
- AUTONOMOUS ROBOTS, SPECIAL ISSUE ON INTELLIGENT EMBEDDED SYSTEMS
, 2001
"... This paper describes an incremental deployment algorithm for mobile sensor networks. A mobile sensor network is a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. The algorithm deploys nodes one-at-atime into an unknown environment, ..."
Abstract
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Cited by 228 (9 self)
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This paper describes an incremental deployment algorithm for mobile sensor networks. A mobile sensor network is a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. The algorithm deploys nodes one-at-atime into an unknown environment, with each node making use of information gathered by previously deployed nodes to determine its target location. The algorithm is designed to maximize network `coverage' whilst simultaneously ensuring that nodes retain line-of-sight with one another (this latter constraint arises from the need to localize the nodes; in our previous work on mesh-based localization [12, 13] we have shown how nodes can localize themselves in a completely unknown environment by using other nodes as landmarks). This paper describes the incremental deployment algorithm and presents the results of an extensive series of simulation experiments. These experiments serve to both validate the algorithm and illuminate its empirical properties.
Frontier-Based Exploration Using Multiple Robots
, 1998
"... Frontier-based exploration directs mobile robots to regions on the boundary between unexplored space and space that is known to be open. Previously, we have demonstrated that frontier-based exploration can be used to map indoor environments where walls and obstacles may be in arbitrary orientations. ..."
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Cited by 175 (5 self)
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Frontier-based exploration directs mobile robots to regions on the boundary between unexplored space and space that is known to be open. Previously, we have demonstrated that frontier-based exploration can be used to map indoor environments where walls and obstacles may be in arbitrary orientations. In this paper, we show how frontier-based exploration can be extended to multiple robots. In our approach, robots share perceptual information, but maintain separate global maps, and make independent decisions about where to explore. This approach enables robots to make use of information from other robots to explore more effectively, but it also allows the team to be robust to the loss of individual robots. We have implemented our multirobot exploration system on real robots, and we demonstrate that they can explore and map office environments as a team.
Coverage, Exploration and Deployment by a Mobile Robot and Communication Network
- Telecommunication Systems Journal, Special Issue on Wireless Sensor Networks
, 2003
"... We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot(s). The environment is assumed to be large enough such that constant motion by the robot(s) is needed to cover the environment. We present an e#cient minimalist algorithm which assumes that ..."
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Cited by 109 (12 self)
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We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot(s). The environment is assumed to be large enough such that constant motion by the robot(s) is needed to cover the environment. We present an e#cient minimalist algorithm which assumes that global information is not available (neither a map, nor GPS). Our algorithm deploys a network of radio beacons which assists the robot(s) in coverage. This network is also used for navigation. The deployed network can also be used for applications other than coverage. Simulation experiments are presented which show the collaboration between the deployed network and mobile robot(s) for the tasks of coverage/exploration, network deployment and maintenance (repair), and mobile robot(s) recovery (homing behavior). We present a theoretical basis for our algorithm on graphs and show the results of the simulated scenario experiments.
Mobile Robot Exploration and Map-Building with Continuous Localization
- In Proceedings of the 1998 IEEE/RSJ International Conference on Robotics and Automation
, 1998
"... Our research addresses how to integrate exploration and localization for mobile robots. A robot exploring and mapping an unknown environment needs to know its own location, but it may need a map in order to determine that location. In order to solve this problem, we have developed ARIEL, a mobile ro ..."
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Cited by 100 (5 self)
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Our research addresses how to integrate exploration and localization for mobile robots. A robot exploring and mapping an unknown environment needs to know its own location, but it may need a map in order to determine that location. In order to solve this problem, we have developed ARIEL, a mobile robot system that combines frontierbased exploration with continuous localization. ARIEL explores by navigating to frontiers, regions on the boundary between unexplored space and space that is known to be open. ARIEL finds these regions in the occupancy grid map that it builds as it explores the world. ARIEL localizes by matching its recent perceptions with the information stored in the occupancy grid. We have implemented ARIEL on a real mobile robot and tested ARIEL in a realworld office environment. We present quantitative results that demonstrate that ARIEL can localize accurately while exploring, and thereby build accurate maps of its environment. 1.0 Introduction We have been investiga...
Spreading Out: A Local Approach to Multi-robot Coverage
- in Proc. of 6th International Symposium on Distributed Autonomous Robotic Systems
, 2002
"... The problem of coverage without a priori global information about the environment is a key element of the general exploration problem. Applications vary from exploration of the Mars surface to the urban search and rescue (USAR) domain, where neither a map, nor a Global Positioning System (GPS) are a ..."
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Cited by 93 (9 self)
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The problem of coverage without a priori global information about the environment is a key element of the general exploration problem. Applications vary from exploration of the Mars surface to the urban search and rescue (USAR) domain, where neither a map, nor a Global Positioning System (GPS) are available. We propose two algorithms for solving the 2D coverage problem using multiple mobile robots. The basic premise of both algorithms is that local dispersion is a natural way to achieve global coverage. Thus, both algorithms are based on local, mutually dispersive interaction between robots when they are within sensing range of each other. Simulations show that the proposed algorithms solve the problem to within 5-7% of the (manually generated) optimal solutions. We show that the nature of the interaction needed between robots is very simple; indeed anonymous interaction slightly outperforms a more complicated local technique based on ephemeral identification.
Experiments with a large heterogeneous mobile robot team: Exploration, mapping, deployment and detection
- International Journal of Robotics Research
, 2006
"... We describe the design and experimental validation of a large heterogeneous mobile robot team built for the DARPA Software for Distributed Robotics (SDR) program. The core challenge for the SDR program was to develop a multi-robot system capable of carrying out a specific mission: to deploy a large ..."
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Cited by 77 (11 self)
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We describe the design and experimental validation of a large heterogeneous mobile robot team built for the DARPA Software for Distributed Robotics (SDR) program. The core challenge for the SDR program was to develop a multi-robot system capable of carrying out a specific mission: to deploy a large number of robots into an unexplored building, map the building interior, detect and track intruders, and transmit all of the above information to a remote operator. To satisfy these requirements, we developed a heterogeneous robot team consisting of approximately 80 robots. We sketch the key technical elements of this team, focusing on the novel aspects, and present selected results from supervised experiments conducted in a 600 m 2 indoor environment. 1
Learning Occupancy Grid Maps With Forward Sensor Models
"... This article describes a new algorithm for acquiring occupancy grid maps with mobile robots. Existing occupancy grid mapping algorithms decompose the high-dimensional mapping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently. Thi ..."
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Cited by 59 (0 self)
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This article describes a new algorithm for acquiring occupancy grid maps with mobile robots. Existing occupancy grid mapping algorithms decompose the high-dimensional mapping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently. This induces conflicts that may lead to inconsistent maps, even for noise-free sensors. This article shows how to solve the mapping problem in the original, high-dimensional space, thereby maintaining all dependencies between neighboring cells. As a result, maps generated by our approach are often more accurate than those generated using traditional techniques. Our approach relies on a statistical formulation of the mapping problem using forward models. It employs the expectation maximization algorithm for searching maps that maximize the likelihood of the sensor measurements.
An Incremental Deployment Algorithm for Mobile Robot Teams
- In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS
, 2002
"... This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is d ..."
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Cited by 45 (0 self)
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This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is designed to maximize the area covered by the robots' sensors, while simultaneously ensuring that the robots maintain line-of-sight contact with one another. This paper describes the basic algorithm and presents results obtained from a series of experiments conducted using both real and simulated robots.
Autonomous flight in unstructured and unknown indoor environments
- in Proceedings of EMAV
, 2009
"... This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance ..."
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Cited by 43 (8 self)
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This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms developed for ground robots. We then describe our solutions to the key problems, including a multi-level sensing and control hierarchy, a high-speed laser scan-matching algorithm, EKF data fusion, and a high-level SLAM implementation. Finally, we show experimental results that illustrate the helicopter’s ability to navigate accurately and autonomously in unknown environments. Figure 1: Our quadrotor helicopter. Sensing and computation components include a Hokuyo Laser Rangefinder (1), laserdeflecting mirrors for altitude (2), a monocular camera (3), an IMU (4), a Gumstix processor (5), and the helicopter’s internal processor (6) 1
