The Mobile Robot Rhino

Buhmann, Joachim M.; Burgard, Wolfram; Cremers, Armin B.; Fox, Dieter; Hofmann, Thomas; Schneider, Frank E.; Strikos, J.; Thrun, Sebastian

doi:10.1007/978-1-4471-3087-1_26

Cited by 108 publications

(59 citation statements)

References 1 publication

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“…In some approaches to planning, the cost map utilized for computing action cost is binary [2] [5]. In such cases, a cell cost is infinite when the cell is occupied by an obstacle and zero otherwise.…”

Section: Related Workmentioning

confidence: 99%

Efficient cost computation in cost map planning for non-circular robots

King

Likhachev

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-For a robot with a circular footprint, obstacles in a map can be inflated by the radius of the footprint, and planning can treat the robot as a point robot. Many robotic vehicles however have non-circular footprints. When operating in cluttered spaces it therefore becomes important to evaluate the footprint of these robots against a cost map. This evaluation is one of the major computational burdens in planning for robots whose footprints can not be assumed to be circular.In this paper, we propose an efficient method for evaluating a footprint of the robot against a cost map. Our method involves a transformation of the set of points covered by the footprint of the robot into two sets of points: points that should be evaluated against the cost map with inflated obstacles, and points that should be evaluated against the original cost map. The cumulative number of these points is much smaller than the number of points in the original footprint of the robot. Moreover, the method automatically reduces the robot to a single point when its footprint is circular. On the theoretical side, the paper proves the correctness of our method. On the experimental side, the paper shows that the method results in a significant speedup.

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Section: Related Workmentioning

confidence: 99%

Efficient cost computation in cost map planning for non-circular robots

King

Likhachev

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Robotic Systems: Single-robot Versus Multi-robot A single-robot system contains only one individual robot that is able to model itself, the environment and their interaction [10]. Several individual robots are well known, such as RHINO [18], ASIMO [19], MER-A [20], BigDog [21], NAO [22] and PR2 [147]. The robot in a single-robot system is often designed to deal with a task on its own account.…”

Section: Introductionmentioning

confidence: 99%

A Survey and Analysis of Multi-Robot Coordination

Yan

Jouandeau

Chérif

2013

International Journal of Advanced Robotic Systems

530

279

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In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper.

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“…Shakey the robot [3] was the first robot to actually perform tasks in human environments by decomposing tasks into sequences of actions. Rhino [4], a robot contender at the 1994 AAAI Robot Competition and Exhibition, used SONAR readings to build an occupancy grid map [5], and localized by matching its observations to expected wall orientations. Minerva [1] served as a tour guide in a Smithsonian museum.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multi-sensor Mobile Robot Localization for Diverse Environments

Biswas

Veloso

2014

RoboCup 2013: Robot World Cup XVII

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Abstract. Mobile robot localization with different sensors and algorithms is a widely studied problem, and there have been many approaches proposed, with considerable degrees of success. However, every sensor and algorithm has limitations, due to which we believe no single localization algorithm can be "perfect," or universally applicable to all situations. Laser rangefinders are commonly used for localization, and state-of-theart algorithms are capable of achieving sub-centimeter accuracy in environments with features observable by laser rangefinders. Unfortunately, in large scale environments, there are bound to be areas devoid of features visible by a laser rangefinder, like open atria or corridors with glass walls. In such situations, the error in localization estimates using laser rangefinders could grow in an unbounded manner. Localization algorithms that use depth cameras, like the Microsoft Kinect sensor, have similar characteristics. WiFi signal strength based algorithms, on the other hand, are applicable anywhere there is dense WiFi coverage, and have bounded errors. Although the minimum error of WiFi based localization may be greater than that of laser rangefinder or depth camera based localization, the maximum error of WiFi based localization is bounded and less than that of the other algorithms. Hence, in our work, we analyze the strengths of localization using all three sensors -using a laser rangefinder, a depth camera, and using WiFi. We identify sensors that are most accurate at localization for different locations on the map. The mobile robot could then, for example, rely on WiFi localization more in open areas or areas with glass walls, and laser rangefinder and depth camera based localization in corridor and office environments.

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The Mobile Robot Rhino

Cited by 108 publications

References 1 publication

Efficient cost computation in cost map planning for non-circular robots

Efficient cost computation in cost map planning for non-circular robots

A Survey and Analysis of Multi-Robot Coordination

Multi-sensor Mobile Robot Localization for Diverse Environments

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