Bounded Uncertainty Roadmaps for Path Planning (original) (raw)
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The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
Robotics: Science and Systems III, 2007
We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a goal. In many motion planning applications ranging from maneuvering vehicles over unfamiliar terrain to steering flexible medical needles through human tissue, the response of a robot to commanded actions cannot be precisely predicted. We propose to build a roadmap by sampling collision-free states in the configuration space and then locally sampling motions at each state to estimate state transition probabilities for each possible action. Given a query specifying initial and goal configurations, we use the roadmap to formulate a Markov Decision Process (MDP), which we solve using Infinite Horizon Dynamic Programming in polynomial time to compute stochastically optimal plans. The Stochastic Motion Roadmap (SMR) thus combines a sampling-based roadmap representation of the configuration space, as in PRM's, with the well-established theory of MDP's. Generating both states and transition probabilities by sampling is far more flexible than previous Markov motion planning approaches based on problem-specific or grid-based discretizations. We demonstrate the SMR framework by applying it to nonholonomic steerable needles, a new class of medical needles that follow curved paths through soft tissue, and confirm that SMR's generate motion plans with significantly higher probabilities of success compared to traditional shortest-path plans.
Anytime dynamic path-planning with flexible probabilistic roadmaps
Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006
Probabilistic roadmaps (PRM) have been demonstrated to be very promising for planning paths for robots with high degrees of freedom in complex 3D workspaces. In this paper we describe a PRM path-planning method presenting three novel features that are useful in various real-world applications. First, it handles zones in the robot workspace with different degrees of desirability. Given the random quality of paths that are calculated by traditional PRM approaches, this provides a mean to specify a sampling strategy that controls the search process to generate better paths by simply annotating regions in the free workspace with degrees of desirability. Second, our approach can efficiently re-compute paths in dynamic environments where obstacles and zones can change shape or move concurrently with the robot. Third, it can incrementally improve the quality of a generated path, so that a suboptimal solution is available when required for immediate action, but get improved as more planning time is affordable.
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.
An Efficient Random Walk Strategy for Sampling based Robot Motion Planners
Abstract. 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. We present a new technique based on a random walk strategy to generate samples in narrow regions quickly, thus improving efficiency of Probabilistic Roadmap 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, thus giving significant improvement over other known methods.
Current Issues in Sampling-Based Motion Planning
Springer Tracts in Advanced Robotics, 2005
In this paper, we discuss the field of sampling-based motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, sampling-based methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful motion planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how samplingbased motion planning has developed. We then discuss a variety of important issues for sampling-based motion planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in sampling-based motion planning.
Realtime Informed Path Sampling for Motion Planning Search
ri.cmu.edu
Robot motions typically originate from an uninformed path sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive collision-testing process. Although all necessary information for collision-testing a path is known to the planner, that information is typically stored in a relatively unavailable form in a costmap. By summarizing the most salient data in a more accessible form, our process delivers a denser sampling of the free space per unit time than open-loop sampling techniques. We obtain this result by probabilistically modeling-in real time and with minimal information-the locations of obstacles, based on collision test results. We demonstrate up to a 780% increase in paths surviving collision test.
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.
Obstacle Aware Sampling for Path Planning
arXiv (Cornell University), 2022
Many path planning algorithms are based on sampling the state space. While this approach is very simple, it can become costly when the obstacles are unknown, since samples hitting these obstacles are wasted. The goal of this paper is to efficiently identify obstacles in a map and remove them from the sampling space. To this end, we propose a preprocessing algorithm for space exploration that enables more efficient sampling. We show that it can boost the performance of other space sampling methods and path planners. Our approach is based on the fact that a convex obstacle can be approximated provably well by its minimum volume enclosing ellipsoid (MVEE), and a non-convex obstacle may be partitioned into convex shapes. Our main contribution is an algorithm that strategically finds a small sample, called the activecoreset, that adaptively samples the space via membershiporacle such that the MVEE of the coreset approximates the MVEE of the obstacle. Experimental results confirm the effectiveness of our approach across multiple planners based on Rapidly-exploring random trees, showing significant improvement in terms of time and path length.
International Journal of Interactive Multimedia and Artificial Intelligence, 2022
Sampling-based motion planning in the field of robot motion planning has provided an effective approach to finding path for even high dimensional configuration space and with the motivation from the concepts of sampling based-motion planners, this paper presents a new sampling-based planning strategy called Optimistic Motion Planning using Recursive Sub-Sampling (OMPRSS), for finding a path from a source to a destination sanguinely without having to construct a roadmap or a tree. The random sample points are generated recursively and connected by straight lines. Generating sample points is limited to a range and edge connectivity is prioritized based on their distances from the line connecting through the parent samples with the intention to shorten the path. The planner is analysed and compared with some sampling strategies of probabilistic roadmap method (PRM) and the experimental results show agile planning with early convergence.
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.