Sampling techniques for probabilistic roadmap planners (original) (raw)
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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,
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.
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.
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.
Biasing Samplers to Improve Motion Planning Performance
Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007
With the success of randomized sampling-based motion planners such as Probabilistic Roadmap Methods, much work has been done to design new sampling techniques and distributions. To date, there is no sampling technique that outperforms all other techniques for all motion planning problems. Instead, each proposed technique has different strengths and weaknesses. However, little work has been done to combine these techniques to create new distributions. In this paper, we propose to bias one sampling distribution with another such that the resulting distribution out-performs either of its parent distributions. We present a general framework for biasing samplers that is easily extendable to new distributions and can handle an arbitrary number of parent distributions by chaining them together. Our experimental results show that by combining distributions, we can out-perform existing planners. Our results also indicate that not one single distribution combination performs the best in all problems, and we identify which perform better for the specific application domains studied.
Adaptive sampling for generalized sampling based motion planners
49th IEEE Conference on Decision and Control (CDC), 2010
In this paper, an Adaptive Sampling strategy is presented for the generalized sampling based motion planner, Generalized Probabilistic Roadmap (GPRM) [18, 19]. These planners are designed to account for stochastic map and model uncertainty and provide a feedback solution to the motion planning problem. Sampling intelligently, in this framework, can result in huge speedups when compared to naive uniform sampling. By using the information of transition probabilities, encoded in these generalized planners, the proposed strategy biases sampling to improve the efficiency of sampling, and increase the overall success probability of GPRM. The strategy was used to solve the motion planning problem of a fully actuated point robot on several maps of varying difficulty levels, and results show that the strategy helps solve the problem efficiently while simultaneously increasing the success probability of the solution. Results also show that these rewards increase with an increase in map complexity.
Adaptive Strategies for Probabilistic Roadmap Construction
2003
This paper presents an experimental study of prospects for using adaptable local search techniques in probabilistic roadmap based motion planning. The classical PRM approach uses a single fast and simple local planner to build a network representation of the configuration space. Advanced PRM planners utilize heuristic sampling techniques and combine multiple local planners. The planner described here uses a single local planner, but adjusts its competence during the roadmap construction stage according to the problem at hand. Two adjusting strategies are proposed and compared experimentally against using a static local planner at a set competence level. The results indicate that roadmap construction with an adaptive local planner can bring advantages including more robust performance and a reduction in planning cost variance.
Intelligent Robots and …, 1999
Probabilistic roadmap planners have been used with success to plan paths for flexible objects such as metallic plates or plastic flexible pipes. This paper improves the performance of these planners by using the medial axis of the workspace to guide the random sampling. At a preprocessing stage, the medial axis of the workspace is computed using a recent efficient algorithm. Then the flexible object is fitted at random points along the medial axis. The energy of all generated configurations is minimized and the planner proceeds to connect them with low-energy quasi-static paths in a roadmap that captures the connectivity of the free space. Given an initial and a final configuration, the planner connects these to the roadmap and searches the roadmap for a path. Our experimental results show that the new sampling scheme is successful in identifying critical deformations of the object along solution paths which results in a significant reduction of the computation time. Our work on planning for flexible objects has applications in industrial settings, virtual reality environments, and medicine.
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.