Multi-Level Free-Space Dilation for Sampling Narrow Passages in PRM Planning (original) (raw)

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Sampling techniques for probabilistic roadmap planners

2004

The probabilistic roadmap approach is a commonly used motion planning technique. A crucial ingredient of the approach is a sampling algorithm that samples the configuration space of the moving object for free configurations. Over the past decade many sampling techniques have been proposed. 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. We compared 12 of such sampling techniques within a single environment on the same scenes. 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 users in deciding which technique is suitable for their situation.

Robot Motion Planning Using Adaptive Hybrid Sampling in Probabilistic Roadmaps

Electronics, 2016

Motion planning deals with finding a collision-free trajectory for a robot from the current position to the desired goal. For a high-dimensional space, sampling-based algorithms are widely used. Different sampling algorithms are used in different environments depending on the nature of the scenario and requirements of the problem. Here, we deal with the problems involving narrow corridors, i.e., in order to reach its destination the robot needs to pass through a region with a small free space. Common samplers used in the Probabilistic Roadmap are the uniform-based sampler, the obstacle-based sampler, maximum clearance-based sampler, and the Gaussian-based sampler. The individual samplers have their own advantages and disadvantages; therefore, in this paper, we propose to create a hybrid sampler that uses a combination of sampling techniques for motion planning. First, the contribution of each sampling technique is deterministically varied to create time efficient roadmaps. However, this approach has a limitation: The sampling strategy cannot adapt as per the changing configuration spaces. To overcome this limitation, the sampling strategy is extended by making the contribution of each sampler adaptive, i.e., the sampling ratios are determined on the basis of the nature of the environment. In this paper, we show that the resultant sampling strategy is better than commonly used sampling strategies in the Probabilistic Roadmap approach.

Preprocessing of Configuration Space for Improved Sampling Based Path Planning

Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ineffective when the configuration space has a narrow passage. This paper presents two new techniques of preprocessing the configuration space. The first technique called a Random Walk to Surface (RWS), uses a random walk strategy to generate samples in narrow regions quickly, thus improving effciency of Probabilistic Roadmap (PRM) based planners. The algorithm substantially reduces instances of collision checking and thereby decreases computational time. The method is powerful even for cases where the structure of the narrow passage is not known a priori, thus giving significant improvement over other known methods. The second method, by preprocessing the configuration space, improves the effiency of Rapidly Exploring Random Tree (RRT) like planners by identifying key regions of the configuration space to search for a solution path. The Experiments show a significant improvement in effiency for both PRM and RRT like planners.

Sampling and node adding in probabilistic roadmap planners

Robotics and Autonomous Systems, 2006

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.

A comparative study of probabilistic roadmap planners

Springer Tracts in Advanced Robotics, 2004

The probabilistic roadmap approach is one of the leading motion planning techniques. Over the past eight years 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, basic sampling techniques, and node adding techniques. The results should help future users of the probabilistic roadmap planning approach to choose the correct techniques.

The bridge test for sampling narrow passages with probabilistic roadmap planners

2003

Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but narrow passages in a robot's configuration space create significant difficulty for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which boosts the sampling density inside narrow passages. The bridge test relies on simple tests of local geometry and can be implemented efficiently in high-dimensional configuration spaces. The strengths of the bridge test and uniform sampling complement each other naturally and are combined to generate the final hybrid sampling strategy. Our planner was tested on point robots and articulated robots in planar workspaces. Preliminary experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.

Reachability-based analysis for Probabilistic Roadmap planners

Robotics and Autonomous Systems, 2007

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.

Finding narrow passages with probabilistic roadmaps: The small-step retraction …

Autonomous robots

The efficiency of Probabilistic Roadmap (PRM) planners drops dramatically in spaces with narrow passages. This paper presents a new method-small-step retraction-that helps PRM planners find paths through such passages. The method consists of slightly fattening the robot's free space, constructing a roadmap in the fattened free space, and repairing colliding portions of this roadmap by retracting them out of collision. The fattened free space is not explicitly computed. Instead, the robot links and/or obstacles are thinned around their medial axis. A robot configuration lies in fattened free space if the thinned objects do not collide at this configuration. Two repair strategies are used. The "optimist" strategy waits until a complete path has been found in fattened free space before repairing it. The "pessimist" strategy repairs the roadmap as it is being built. The former is faster, but the latter is more reliable. A simple combination yields an integrated planner that is both fast and reliable.

A comparitive study of probabilistic roadmap planners

2002

The probabilistic roadmap approach is a commonly used motion planning technique. A crucial ingredient of the approach is a sampling algorithm that samples the configuration space of the moving object for free configurations. Over the past decade many sampling techniques have been proposed. It is difficult to compare the different techniques because they were tested on different types of scenes,

Analysis of Obstacle based Probabilistic RoadMap Method using Geometric Probability

3rd International conference on Advances in Control and Optimization of Dynamical Systems, 2014

Sampling based planners have been successful in robot motion planning, with many degrees of freedom, but still remain in effective in the presence of narrow passages within the confi guration space. There exist several heuristics, which generate samples in the critical regions and improve the efficiency of probabilistic roadmap planners. In this paper, we present an evaluation of success probability of one such heuristic method, called obstacle based probabilistic roadmap planners or OBPRM, using geometric probability theory. The result indicates that the probability of success of generating free sample points around the surface of the n dimensional con guration space obstacle is directly proportional to the surface area of the obstacles."