Choosing good distance metrics and local planners for probabilistic roadmap methods

Amato, Nancy M.; Bayazıt, O. Burçhan; Dale, Lucia K.; Jones, Christopher A.; Vallejo, Daniel

doi:10.1109/robot.1998.677043

Cited by 112 publications

(113 citation statements)

References 28 publications

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“…Also the rotate-at-s technique did not give the improvement suggested in [1]. It should though be noticed that this can depend on the underlying collision checking package used.…”

Section: Collision Checkingmentioning

confidence: 86%

“…rotate-at-s: While the previous methods check collisions along a straight line, the rotate-at-s approach first translates from start to an intermediary configuration s halfway, then rotates, and finally translates to the endpoint [1]. We set s to 0.5.…”

Section: Collision Checkingmentioning

confidence: 99%

“…The chance of success for such a connection is minimal while the collision checks required for testing the path are expensive. An important question here is what we use as a distance measure; see also [1]. In this paper we use d = d t + d r , where d t denotes the translational distance of the origin of the object and d r denotes the rotational distance between two unit quaternions that represent the start and goal orientation; see [22].…”

Section: Node Addingmentioning

confidence: 99%

See 2 more Smart Citations

Sampling and node adding in probabilistic roadmap planners

Gerærts

Overmars

2006

Robotics and Autonomous Systems

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The probabilistic roadmap approach is one of the leading motion planning techniques. Over the past decade the technique has been studied by many different researchers. This has led to a large number of variants of the approach, each with its own merits. It is difficult to compare the different techniques because they were tested on different types of scenes, using different underlying libraries, implemented by different people on different machines. In this paper we provide a comparative study of a number of these techniques, all implemented in a single system and run on the same test scenes and on the same computer. In particular we compare collision checking techniques, sampling techniques, and node adding techniques. The results were surprising in the sense that techniques often performed differently than claimed by the designers. The study also showed how difficult it is to evaluate the quality of the techniques. The results should help future users of the probabilistic roadmap planning approach in deciding which technique is suitable for their situation.

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“…Also the rotate-at-s technique did not give the improvement suggested in [1]. It should though be noticed that this can depend on the underlying collision checking package used.…”

Section: Collision Checkingmentioning

confidence: 86%

Section: Collision Checkingmentioning

confidence: 99%

Section: Node Addingmentioning

confidence: 99%

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Sampling and node adding in probabilistic roadmap planners

Gerærts

Overmars

2006

Robotics and Autonomous Systems

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“…You might have noticed that the previous metric for SO (2) does not give the distance traveling along the circle. It instead takes a shortcut by computing the length of the line segment in R 2 that connects the two points.…”

Section: Example 52 (So(2) Metric By Comparing Angles)mentioning

confidence: 99%

“…Example 5.3 (An SE(2) Metric) Again by using the subspace principle, a metric can easily be obtained for SE (2). Using the complex number representation of SO (2), each element of SE(2) is a point (x t , y t , a, b) ∈ R 4 .…”

Section: Example 52 (So(2) Metric By Comparing Angles)mentioning

confidence: 99%

Combinatorial Motion Planning

LaValle

2006

Planning Algorithms

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Motion Planning for Humanoid Robots

Kuffner

Nishiwaki

Kagami

et al. 2005

Springer Tracts in Advanced Robotics

202

186

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Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable sensors, safety mechanisms, and general integrated software tools and techniques are needed. We believe that the development of practical motion planning algorithms and obstacle avoidance software for humanoid robots represents an important enabling technology. This paper gives an overview of some of our recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions. We show experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware.

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Choosing good distance metrics and local planners for probabilistic roadmap methods

Cited by 112 publications

References 28 publications

Sampling and node adding in probabilistic roadmap planners

Sampling and node adding in probabilistic roadmap planners

Combinatorial Motion Planning

Motion Planning for Humanoid Robots

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