The virtual wall approach to limit cycle avoidance for unmanned ground vehicles

Ordóñez, Camilo; Collins, Emmanuel G.; Selekwa, Majura F.; Dunlap, Damion D.

doi:10.1016/j.robot.2007.11.010

Cited by 44 publications

(30 citation statements)

References 5 publications

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“…Limited randomization permits to resolve navigational limit cycles without violation of the reactive nature of the overall algorithm. A number of deterministic methods to prevent these cycles are known (see, e.g., [205] and the above literature on the Bug-type algorithms), however they hardly can be classified as purely reactive.…”

Section: Bug Algorithmsmentioning

confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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SUMMARYWe review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

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Section: Bug Algorithmsmentioning

confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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“…Thus, the robot won't get stuck in Repulsive Area, and can seek other probable routes freely in an opportunistic manner. A notable distinction from virtual wall approach [24] is that a region marked as Repulsive Area is not prohibited for traversing, but the preferred motion direction is goal-repulsion. Fig.4 shows the scheme of our method in comparison with trajectory memorization methods.…”

Section: A Limit Cycle Situationsmentioning

confidence: 99%

“…Techniques are developed to guide the robot to escape from limit cycle situation upon detection of a limit cycle path. Some of the approaches are the setup of a virtual wall [24] or virtual target [26], or adaptive rotation using clockwise or counterclockwise turning along a periodic orbit [25]. [26] proposed to separate the deliberative (A*) layer and reliable reactive (DH-bug) layer based on bug algorithm of control system architecture on grid map to escape from traps and improve the efficiency.…”

mentioning

confidence: 99%

Efficient path planning with limit cycle avoidance for mobile robot navigation

Ting

Liu

2013

2013 IEEE International Conference on Mechatronics and Automation

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Abstract-Autonomous navigation in an unknown environment may encounter the limit cycle problem causing lower efficiency when a mobile robot attempts to reach the goal while avoiding obstacles on the way. To cover a wider range of navigation tasks, this paper presents a novel waypoint navigation method in polar coordinate. Navigation limit cycle avoidance is handled by creating and memorizing a specific traversable but less preferred areas during waypoint navigation in which the robot changes its orientation to follow from goal-directed to opportunistic goal-repulsive behavior. This allows the robot to be able to predict, thus avoiding upcoming limit cycle situations. The saving of time and memory resources of path computation using the novel polar-coordinate based waypoint navigation has been verified by simulations, and an implementation of the navigation method on a real mobile robot works well in an indoor experiment with demonstrated planning and route selection capabilities for waypoint navigation.

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“…The path planning strategy adopted by a reactive mobile robot is therefore based on route-like spatial cognition through sensory information and motion control. Quite a number of different algorithms have been developed for reactive motion of mobile robots including: virtual wall (Ordonez et al, 2007), graph-based method (Kelarev, 2003), landmark learning (Krishna and Kalra, 2001), fuzzy logic minimum risk (Wang and Liu, 2007), target switching strategy (Xu and Tso, 1999;Motlagh et al, 2009a), 3-step potential field (Tu and Baltes, 2006), and many others. A summary of the recent literature is given in (Motlagh et al, 2009a).…”

Section: Introductionmentioning

confidence: 99%

Motion Modeling using Motion Concepts of Fuzzy Artificial Potential Fields

Motlagh¹,

Ramli²,

Motlagh³

et al. 2010

Int J Automot Mech Eng

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Artificial potential fields(APF) are well established for reactive navigation of mobile robots. This paper describes a fast and robust fuzzy-APF on an ActivMedia AmigoBot.Obstacle-related information is fuzzified by using sensory fusion, which results in a shorter runtime. In addition, the membership functions of obstacle direction and range have been merged into one function, obtaining a smaller block of rules. The system is tested in virtual environments with non-concave obstacles. Then, the paper describes a new approach to motion modelling where the motion of intelligent travellers is modelled by consecutive path segments. In previous work, the authors described a reliable motion modelling technique using causal inference of fuzzy cognitive maps (FCM) which has been efficiently modified for the purpose of this contribution. Results and analysis are given to demonstrate the efficiency and accuracy of the proposed motion modelling algorithm.

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The virtual wall approach to limit cycle avoidance for unmanned ground vehicles

Cited by 44 publications

References 5 publications

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Efficient path planning with limit cycle avoidance for mobile robot navigation

Motion Modeling using Motion Concepts of Fuzzy Artificial Potential Fields

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