Robot Navigation by Waypoints

Wan, Shaohua; Mulvaney, David; Sillitoe, Ian Pw; Swere, Erick

doi:10.1007/s10846-008-9209-6

Cited by 27 publications

(18 citation statements)

References 42 publications

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“…This will be slated for inclusion in the next update to the MRDS CARMI navigation system. The local minima problem was also encountered and studied in an related work involving the use of waypoints for robot navigation [32]. This work suggested a solution that incorporates a basic memory map of previously travelled locations.…”

Section: Discussionmentioning

confidence: 99%

An Improved Indoor Robot Human-Following Navigation Model Using Depth Camera, Active IR Marker and Proximity Sensors Fusion

Tsun

2018

Robotics

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Creating a navigation system for autonomous companion robots has always been a difficult process, which must contend with a dynamically changing environment, which is populated by a myriad of obstructions and an unspecific number of people, other than the intended person, to follow. This study documents the implementation of an indoor autonomous robot navigation model, based on multi-sensor fusion, using Microsoft Robotics Developer Studio 4 (MRDS). The model relies on a depth camera, a limited array of proximity sensors and an active IR marker tracking system. This allows the robot to lock onto the correct target for human-following, while approximating the best starting direction to begin maneuvering around obstacles for minimum required motion. The system is implemented according to a navigation algorithm that transforms the data from all three types of sensors into tendency arrays and fuses them to determine whether to take a leftward or rightward route around an encountered obstacle. The decision process considers visible short, medium and long-range obstructions and the current position of the target person. The system is implemented using MRDS and its functional test performance is presented over a series of Virtual Simulation Environment scenarios, greenlighting further extensive benchmark simulations.

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Section: Discussionmentioning

confidence: 99%

An Improved Indoor Robot Human-Following Navigation Model Using Depth Camera, Active IR Marker and Proximity Sensors Fusion

Tsun

2018

Robotics

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“…Besides, a good navigation system should possess the function to keep the robot in a safe, trapped-free state at all time intervals and if possible, it should be smart enough to provide a high efficiency movement strategy. In static environments, waypoint navigation [17], [18], [28] is a popular reactive navigation method in practical applications. The robot follows an ordered set of waypoints which guides the robot to reach the goal, despite its robustness issue to robot localization errors [18].…”

Section: Waypoint Selectionmentioning

confidence: 99%

“…The robot begins to change its heading as it passes through a waypoint [17], [28]. Before each movement, the waypoint navigation system will deploy several waypoints located in free space within the robot's sensing range, and one of them is chosen as the next waypoint for local navigation.…”

Section: A Deploy Candidate Waypointsmentioning

confidence: 99%

“…For limited sensing range, waypoint navigation system [17], [18], [28] is a popular navigation approach by planning a sequence of subgoals that leads the robot to the target. To develop a more efficient waypoint navigation system, a deliberative layer could be integrated in the higher level to provide global planning capability based on accumulated knowledge of the environment and navigation experience stored in memory [17].…”

mentioning

confidence: 99%

“…To develop a more efficient waypoint navigation system, a deliberative layer could be integrated in the higher level to provide global planning capability based on accumulated knowledge of the environment and navigation experience stored in memory [17]. In this aspect, some features of ecological robotics, especially those learned from human intelligence in navigation, are helpful.…”

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confidence: 99%

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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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