Algorithm of navigation for a mobile robot

Keirsey, D.; Koch, Edmund; McKisson, J.; Meystel, A.; Mitchell, Joseph S. B.

doi:10.1109/robot.1984.1087205

Cited by 20 publications

(9 citation statements)

References 5 publications

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“…Algorithms based upon visibility lines became a standard solution [393,394], • Planning of optimum tracking was performed as scheduling of motion for the trajectory that was preassigned as a contour [386]. Dealing with disturbances was interpreted as a part of following preassigned trajectory [387] [397], The theoretical background for these efforts was presented [398].…”

Section: Planningmentioning

confidence: 99%

Intelligent systems:

Meystel¹

2002

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Section: Planningmentioning

confidence: 99%

Intelligent systems:

Meystel¹

2002

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“…An A*-like computational structure was applied with different costfunctionals [21,22]. Comparison between different strategies is described in [23][24][25], more advanced ideas of nodes generation are described in [26]. A strategy based upon using A* algorithm for finding minimum path on the graph was accepted in [27] after it was recommended in [26,28].…”

Section: State-of-the-artmentioning

confidence: 99%

Intelligent module for planning/control of master-dependent systems

Meystel

1988

Proceedings of the First International Conference on Industrial and Engineering Applications of Artificial Intelligence and Exp

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An intelligent module is proposed in this paper which is capable of performing a joint recursive planning/control operation which propagates through the intelligent module at all planning/control levels simultaneously. Each of the actuators is equipped by an intelligent module, and all of these modules are working independently and concurrently. The model of the world is being constantly updated based upon vision and a multiplicity of other available sensors, and at various resolutions is submitted to each of the intelligent modules as applied to particular properties of the link being controlled by this module. Together these intelligent modules are working as a team of the actuators controllers, and all decisions are constantly negotiated among the members of the team. Each of the resolutional levels within the actuator intelligent modules is using the following set of planning/control tools: learning rule base, learning heuristic search, and situational decision generator which are first applied simultaneously, and then one of them takes over. A neural network is collecting information about the progress and the results of planning/control processes, and is modifying heuristics, as well as enriching the system of rules. The same neural network is used for supplying the provisional analytical model required for the lowest levels of execution control. Provisional analytical models are applied in a simple form which allows for simple real-time controller operation, and the parameters of this provisional model are constantly being updated by the neural network.

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“…In [11]- [13] a two-dimensional navigation problem is considered. In [11]- [13] a two-dimensional navigation problem is considered.…”

mentioning

confidence: 99%

Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

Lumelsky

Stepanov²

1987

Algorithmica

558

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The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's "'tactile sensor." This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.

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Algorithm of navigation for a mobile robot

Cited by 20 publications

References 5 publications

Intelligent systems:

Intelligent systems:

Intelligent module for planning/control of master-dependent systems

Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

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