Probabilistic approaches to motion planning

Overmars, Mark H.; Švestka, P.

doi:10.1007/bfb0037399

Cited by 43 publications

(68 citation statements)

References 8 publications

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“…See the books of Latombe [25] and LaValle [27] for an extensive overview of the situation and for example the proceedings of the yearly IEEE International Conference on Robotics and Automation (ICRA) or the Workshop on Foundations of Robotics (WAFR) for many recent results. A popular motion planning technique is the Probabilistic Roadmap Method (PRM), developed independently at different sites [3,4,19,20,31,36]. It turns out to be very efficient, easy to implement, and applicable for many different types of motion planning problems (see e.g.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Sampling and node adding in probabilistic roadmap planners

Gerærts

Overmars

2006

Robotics and Autonomous Systems

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“…Rather than concentrating on more clever sampling, it may be beneficial to spend more effort on connecting nodes in difficult regions. Actually, already one of the first papers on PRM did this by trying to connect difficult nodes in a second phase using a bouncing strategy [8]. In Section 6, we will show how more powerful local planners can be used for this as well.…”

Section: Bridge Testmentioning

confidence: 99%

“…These operations make a PFLP expensive in comparison to the simple SLLP. To mitigate this effect, we use a modified version of the potential field planner used in [8].…”

Section: Sllpmentioning

confidence: 99%

“…Although this planner was much faster than previous approaches, it could get stuck at a local minimum of the potential function. One year later, different researchers independently devised the Probabilistic Roadmap Method (PRM), which was successfully able to deal with many motion planning problems [6][7][8]. This success is due to its wide applicability and good performance: if a solution to a problem is easy, then it seems that the PRM can solve it very fast.…”

Section: Introductionmentioning

confidence: 99%

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Reachability-based analysis for Probabilistic Roadmap planners

Gerærts

Overmars

2007

Robotics and Autonomous Systems

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In the last fifteen years, sampling-based planners like the Probabilistic Roadmap Method (PRM) have proved to be successful in solving complex motion planning problems. While theoretically, the complexity of the motion planning problem is exponential in the number of degrees of freedom, sampling-based planners can successfully handle this curse of dimensionality in practice. We give a reachability-based analysis for these planners which leads to a better understanding of the success of the approach. This analysis compares the techniques based on coverage and connectivity of the free configuration space. The experiments show, contrary to general belief, that the main challenge is not getting the free space covered but getting the nodes connected, especially when the problems get more complicated, e.g. when a narrow passage is present. By using this knowledge, we can tackle the narrow passage problem by incorporating a refined neighbor selection strategy, a hybrid sampling strategy, and a more powerful local planner, leading to a considerable speed-up.

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“…In turn, these theoretical results suggest improvements of the planner. Other work investigating similar or related randomized planning approaches include 1,5,15,16,35,36]. Formal attempts to predict the behavior of speci c random planners are proposed in very few papers 9, 2 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Random Sampling Scheme for Path Planning

Barraquand¹,

Kavraki²,

Latombe³

et al. 1996

Robotics Research

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Several randomized p ath planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type o f r obots, and their empirically observed s u c cess. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot's con guration space. We then describe t w o p r eviously developed planners as instances of planners based on this scheme, but applying very di erent sampling strategies. These planners are p r obabilistically complete: if a path exists, they will nd one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the \goodness" of the robot's free s p ace, we show that the running time only grows as the absolute value of the logarithm of the probability of failure t h a t w e a r e willing to tolerate. We also show that it increases at a reasonable ra t e a s t h e s p ace g o odness degrades. In the last section we suggest directions for future r esearch.

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Probabilistic approaches to motion planning

Cited by 43 publications

References 8 publications

Sampling and node adding in probabilistic roadmap planners

Sampling and node adding in probabilistic roadmap planners

Reachability-based analysis for Probabilistic Roadmap planners

A Random Sampling Scheme for Path Planning

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