Anytime dynamic path-planning with flexible probabilistic roadmaps

Belghith, Khaled; Kabanza, Froduald; Hartman, Leo; Nkambou, Roger

doi:10.1109/robot.2006.1642057

Cited by 42 publications

(26 citation statements)

References 7 publications

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“…The cost function is similar to the one used for optimal trajectory generation, as described in (8). The difference is that driving along a reference trajectory is no longer desired, but rather minimizing the velocity v:…”

Section: ➁ Emergency Trajectorymentioning

confidence: 99%

Fail-safe motion planning of autonomous vehicles

Magdici

Althoff

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-Formally verified methods for motion planning are required in order to guarantee safety for autonomous vehicles. In particular, we consider trajectory generation by considering the most probable trajectory of other traffic participants. However, if the surrounding vehicles perform unexpected maneuvers, a collision might be inevitable. In this paper, a fail-safe motion planner is developed, which generates optimal trajectories, yet guarantees safety at all times. Safety is achieved by maintaining an emergency maneuver which can safely bring the host vehicle to a stop while avoiding any collision. The emergency maneuver is computed by considering for a given time horizon the occupancy prediction which encloses all possible trajectories of the other traffic participants. The performance of the approach is evaluated through simulation against real traffic data.

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Section: ➁ Emergency Trajectorymentioning

confidence: 99%

Fail-safe motion planning of autonomous vehicles

Magdici

Althoff

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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“…Flexible Anytime Dynamic PRM (FADPRM) [15] PRM The workspace is segmented into zones of relative desirability. An A* search is done propagating from the goal state, and samples are generated near frontier nodes based on a priority heuristic.…”

Section: A Prm Based Algorithmsmentioning

confidence: 99%

“…Flexible Anytime Dynamic PRM (FADPRM) [15] is analogous to the anytime AD* extension to the A* graph search algorithm [37] in that motion plans can be continually improved during execution. A search is done from the goal state.…”

Section: A Prm Based Algorithmsmentioning

confidence: 99%

Recent progress on sampling based dynamic motion planning algorithms

Short

Pan

Larkin

et al. 2016

2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored. Review of Sampling Based Dynamic Motion Planning AlgorithmsAndrew Short, Zengxi Pan, Nathan Larkin and Stephen van DuinAbstract-This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored.

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“…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. For deployment of waypoints random, or improved random methods [14][15] could deploy massive waypoints for exploration-based navigation. However, over concentrated waypoints without limiting the distances between waypoints could inch the robot, and might guide the robot toward obstacles.…”

Section: A Deploy Candidate Waypointsmentioning

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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Anytime dynamic path-planning with flexible probabilistic roadmaps

Cited by 42 publications

References 7 publications

Fail-safe motion planning of autonomous vehicles

Fail-safe motion planning of autonomous vehicles

Recent progress on sampling based dynamic motion planning algorithms

Efficient path planning with limit cycle avoidance for mobile robot navigation

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