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Relevant bibliographies by topics / Sampling-based motion planning

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Academic literature on the topic 'Sampling-based motion planning'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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Journal articles on the topic "Sampling-based motion planning"

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Nichols, Hayden, Mark Jimenez, Zachary Goddard, Michael Sparapany, Byron Boots, and Anirban Mazumdar. "Adversarial Sampling-Based Motion Planning." IEEE Robotics and Automation Letters 7, no. 2 (2022): 4267–74. http://dx.doi.org/10.1109/lra.2022.3148464.

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Salzman, Oren. "Sampling-based robot motion planning." Communications of the ACM 62, no. 10 (2019): 54–63. http://dx.doi.org/10.1145/3318164.

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Kenye, Lhilo, and Rahul Kala. "Optimistic Motion Planning Using Recursive Sub- Sampling: A New Approach to Sampling-Based Motion Planning." International Journal of Interactive Multimedia and Artificial Intelligence InPress, InPress (2022): 1. http://dx.doi.org/10.9781/ijimai.2022.04.001.

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Kingston, Zachary, Mark Moll, and Lydia E. Kavraki. "Sampling-Based Methods for Motion Planning with Constraints." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 159–85. http://dx.doi.org/10.1146/annurev-control-060117-105226.

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Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: ( a) sampling constraint-satisfying configurations and ( b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.

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Gammell, Jonathan D., and Marlin P. Strub. "Asymptotically Optimal Sampling-Based Motion Planning Methods." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (2021): 295–318. http://dx.doi.org/10.1146/annurev-control-061920-093753.

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Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This article summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.

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Karaman, Sertac, and Emilio Frazzoli. "Sampling-based algorithms for optimal motion planning." International Journal of Robotics Research 30, no. 7 (2011): 846–94. http://dx.doi.org/10.1177/0278364911406761.

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Elbanhawi, Mohamed, and Milan Simic. "Sampling-Based Robot Motion Planning: A Review." IEEE Access 2 (2014): 56–77. http://dx.doi.org/10.1109/access.2014.2302442.

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Kang, Gitae, Yong Bum Kim, Young Hun Lee, Hyun Seok Oh, Won Suk You, and Hyouk Ryeol Choi. "Sampling-based motion planning of manipulator with goal-oriented sampling." Intelligent Service Robotics 12, no. 3 (2019): 265–73. http://dx.doi.org/10.1007/s11370-019-00281-y.

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Solovey, Kiril, and Michal Kleinbort. "The critical radius in sampling-based motion planning." International Journal of Robotics Research 39, no. 2-3 (2019): 266–85. http://dx.doi.org/10.1177/0278364919859627.

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We develop a new analysis of sampling-based motion planning in Euclidean space with uniform random sampling, which significantly improves upon the celebrated result of Karaman and Frazzoli and subsequent work. In particular, we prove the existence of a critical connection radius proportional to [Formula: see text] for n samples and d dimensions: below this value the planner is guaranteed to fail (similarly shown by Karaman and Frazzoli). More importantly, for larger radius values the planner is asymptotically (near-)optimal. Furthermore, our analysis yields an explicit lower bound of [Formula: see text] on the probability of success. A practical implication of our work is that asymptotic (near-)optimality is achieved when each sample is connected to only [Formula: see text] neighbors. This is in stark contrast to previous work that requires [Formula: see text] connections, which are induced by a radius of order [Formula: see text]. Our analysis applies to the probabilistic roadmap method (PRM), as well as a variety of “PRM-based” planners, including RRG, FMT*, and BTT. Continuum percolation plays an important role in our proofs. Lastly, we develop similar theory for all the aforementioned planners when constructed with deterministic samples, which are then sparsified in a randomized fashion. We believe that this new model, and its analysis, is interesting in its own right.

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Sakcak, Basak, Luca Bascetta, Gianni Ferretti, and Maria Prandini. "Sampling-based optimal kinodynamic planning with motion primitives." Autonomous Robots 43, no. 7 (2019): 1715–32. http://dx.doi.org/10.1007/s10514-019-09830-x.

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Dissertations / Theses on the topic "Sampling-based motion planning"

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Morales, Aguirre Marco Antonio. "Metrics for sampling-based motion planning." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2462.

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Bialkowski, Joshua John. "Optimizations for sampling-based motion planning algorithms." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87475.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 141-150).<br>Sampling-basedalgorithms solve the motion planning problem by successively solving several separate suproblems of reduced complexity. As a result, the efficiency of the sampling-based algorithm depends on the complexity of each of the algorithms used to solve the individual subproblems, namely the procedures GenerateSample, FindNearest, LocalPlan, CollisionFree, and AddToGraph. However, it is often the case that these subproblems are quite related, working on common components of the problem definition. Therefore, distinct algorithms and segregated data structures for solving these subproblems might be costing sampling-based algorithms more time than necessary. The thesis of this dissertation is the following: By taking advantage of the fact that these subproblems are solved repeatedly with similar inputs, and the relationships between data structures used to solve the subproblems, we may significantly reduce the practical complexity of sampling-based motion planning algorithms. Moreover, this reuse of information from components can be used to find a middle ground between exact motion planning algorithms which find an explicit representation ofthe collision-free space,and sampling-based algorithms which find no representation of the collision-free space, except for the zeromeasure paths between connected nodes in the roadmap.<br>by Joshua John Bialkowski.<br>Ph. D.

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Jeon, Jeong hwan Ph D. Massachusetts Institute of Technology. "Sampling-based motion planning algorithms for dynamical systems." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101443.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 133-143).<br>Dynamical systems bring further challenges to the problem of motion planning, by additionally complicating the computation of collision-free paths with collision-free dynamic motions. This dissertation proposes efficient approaches for the optimal sampling-based motion planning algorithms, with a strong emphasis on the accommodation of realistic dynamical systems as the subject of motion planning. The main contribution of the dissertation is twofold: advances in general framework for asymptotically-optimal sampling-based algorithms, and the development of fast algorithmic components for certain classes of dynamical systems. The first part of the dissertation begins with key ideas from a number of recent sampling-based algorithms toward fast convergence rates. We reinterpret the ideas in the context of incremental algorithms, and integrate the key ingredients within the strict [omicron](log n) complexity per iteration, which we refer to as the enhanced RRT* algorithm. Subsequently, Goal-Rooted Feedback Motion Trees (GR-FMTs) are presented as an adaptation of sampling-based algorithms into the context of asymptotically-optimal feedback motion planning or replanning. Last but not least, we propose a loop of collective operations, or an efficient loop with cost-informed operations, which minimizes the exposure to the main challenges incurred by dynamical systems, i.e., steering problems or Two-Point Boundary Value Problems (TPBVPs). The second main part of the dissertation directly deals with the steering problems for three categories of dynamical systems. First, we propose a numerical TPBVP method for a general class of dynamical systems, including time-optimal off-road vehicle maneuvers as the main example. Second, we propose a semi-analytic TPBVP approach for differentially flat systems or partially flat systems, by which the computation of vehicle maneuvers is expedited and the capability to handle extreme scenarios is greatly enhanced. Third, we propose an efficient TPBVP algorithm for controllable linear systems, based on the computation of small-sized linear or quadratic programming problems in a progressive and incremental manner. Overall, the main contribution in this dissertation realizes the outcome of anytime algorithms for optimal motion planning problems. An initial solution is obtained within a small time, and the solution is further improved toward the optimal one. To our best knowledge from both simulation results and algorithm analyses, the proposed algorithms supposedly outperform or run at least as fast as other state-of-the-art sampling-based algorithms.<br>by Jeong hwan Jeon.<br>Ph. D.

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Wedge, Nathan Alexander. "Sampling-based Motion Planning Algorithms: Analysis and Development." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1301502703.

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Evestedt, Niclas. "Sampling Based Motion Planning for Heavy Duty Autonomous Vehicles." Licentiate thesis, Linköpings universitet, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132769.

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The automotive industry is undergoing a revolution where the more traditional mechanical values are replaced by an ever increasing number of Advanced Driver Assistance Systems (ADAS) where advanced algorithms and software development are taking a bigger role. Increased safety, reduced emissions and the possibility of completely new business models are driving the development and most automotive companies have started projects that aim towards fully autonomous vehicles. For industrial applications that provide a closed environment, such as mining facilities, harbors, agriculture and airports, full implementation of the technology is already available with increased productivity, reliability and reduced wear on equipment as a result. However, it also gives the opportunity to create a safer working environment when human drivers can be removed from dangerous working conditions. Regardless of the application an important part of any mobile autonomous system is the motion planning layer. In this thesis sampling-based motion planning algorithms are used to solve several non-holonomic and kinodynamic planning problems for car-like robotic vehicles in different application areas that all present different challenges. First we present an extension to the probabilistic sampling-based Closed-Loop Rapidly exploring Random Tree (CL-RRT) framework that significantly increases the probability of drawing a valid sample for platforms with second order differential constraints. When a tree extension is found infeasible a new acceleration profile that tries to brings the vehicle to a full stop before the collision occurs is calculated. A resimulation of the tree extension with the new acceleration profile is then performed. The framework is tested on a heavy-duty Scania G480 mining truck in a simple constructed scenario. Furthermore, we present two different driver assistance systems for the complicated task of reversing with a truck with a dolly-steered trailer. The first is a manual system where the user can easily construct a kinematically feasible path through a graphical user interface. The second is a fully automatic planner, based on the CL-RRT algorithm where only a start and goal position need to be provided. For both approaches, the internal angles of the trailer configuration are stabilized using a Linear Quadratic (LQ) controller and path following is achieved through a pure-pursuit control law. The systems are demonstrated on a small-scale test vehicle with good results. Finally, we look at the planning problem for an autonomous vehicle in an urban setting with dense traffic for two different time-critical maneuvers, namely, intersection merging and highway merging. In these situations, a social interplay between drivers is often necessary in order to perform a safe merge. To model this interaction a prediction engine is developed and used to predict the future evolution of the complete traffic scene given our own intended trajectory. Real-time capabilities are demonstrated through a series of simulations with varying traffic densities. It is shown, in simulation, that the proposed method is capable of safe merging in much denser traffic compared to a base-line method where a constant velocity model is used for predictions.

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Kunz, Tobias. "Time-optimal sampling-based motion planning for manipulators with acceleration limits." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53569.

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Robot actuators have physical limitations in how fast they can change their velocity. The more accurately planning algorithms consider these limitations, the better the robot is able to perform. Sampling-based algorithms have been successful in geometric domains, which ignore actuator limitations. They are simple, parameter-free, probabilistically complete and fast. Even though some algorithms like RRTs were specifically designed for kinodynamic problems, which take actuator limitations into account, they are less efficient in these domains or are, as we show, not probabilistically complete. A common approach to this problem is to decompose it, first planning a geometric path and then time-parameterizing it such that actuator constraints are satisfied. We improve the reliability of the latter step. However, the decomposition approach can neither deal with non-zero start or goal velocities nor provides an optimal solution. We demonstrate that sampling-based algorithms can be extended to consider actuator limitations in the form of acceleration limits while retaining the same advantageous properties as in geometric domains. We present an asymptotically optimal planner by combining a steering method with the RRT* algorithm. In addition, we present hierarchical rejection sampling to improve the efficiency of informed kinodynamic planning in high-dimensional spaces.

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Montana, Felipe. "Sampling-based algorithms for motion planning with temporal logic specifications." Thesis, University of Sheffield, 2019. http://etheses.whiterose.ac.uk/22637/.

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Rahman, S. M. Rayhan. "Performance of local planners with respect to sampling strategies in sampling-based motion planning." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96891.

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Automatically planning the motion of rigid bodies moving in 3D by translation and rotation in the presence of obstacles has long been a research challenge for mathematicians, algorithm designers and roboticists. The field made dramatic progress with the introduction of the probabilistic and sampling-based "roadmap" approach. However, motion planning when narrow passages are present has remained a challenge. This thesis presents a framework for experimenting with combinations of sampling strategies and local planners, and for comparing their performance on user defined input problems. Our framework also allows parallel implementations on a variable number of processing cores. We present experimental results. In particular, our framework has allowed us to find combinations of sampling strategy choice with local planner choice that can solve difficult benchmark motion planningproblems.<br>La planification automatique du mouvement de corps rigides en mouvement 3D par translation et rotation en présence d'obstacles a longtemps été un défi pour la recherche pour les mathématiciens, les concepteurs de l'algorithme et roboticiens. Le champ a fait d'importants progrès avec l'introduction de la méthode de "feuille de route" probabiliste basée sur l'échantillonnage. Mais la planification du mouvement en présence de passages étroits est resté un défi.Cette thése présente un cadre d'expérimentation avec des combinaisons de stratégies d'échantillonnage et les planificateurs locaux, et de comparaison de leurs performances sur des problémes définis par l'utilisateur. Notre programme peut également être exécuté parallèle sur un nombre variable de processeurs. Nous présentons des résultats expérimentaux. En particulier, notre cadre nous a permis de trouver des combinaisons de choix d'une stratégie d'échantillonnage avec choix de planificateur local qui peut résoudre des problèmes difficiles de référence.

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Luders, Brandon (Brandon Douglas). "Robust sampling-based motion planning for autonomous vehicles in uncertain environments." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90727.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.<br>CD-ROM has video of vehicle.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 223-237).<br>While navigating, autonomous vehicles often must overcome significant uncertainty in their understanding of the world around them. Real-world environments may be cluttered and highly dynamic, with uncertainty in both the current state and future evolution of environmental constraints. The vehicle may also face uncertainty in its own motion. To provide safe navigation under such conditions, motion planning algorithms must be able to rapidly generate smooth, certifiably robust trajectories in real-time. The primary contribution of this thesis is the development of a real-time motion planning framework capable of generating feasible paths for autonomous vehicles in complex environments, with robustness guarantees under both internal and external uncertainty. By leveraging the trajectory-wise constraint checking of sampling-based algorithms, and in particular rapidly-exploring random trees (RRT), the proposed algorithms can efficiently evaluate and enforce complex robustness conditions. For linear systems under bounded uncertainty, a sampling-based motion planner is presented which iteratively tightens constraints in order to guarantee safety for all feasible uncertainty realizations. The proposed bounded-uncertainty RRT* (BURRT*) algorithm scales favorably with environment complexity. Additionally, by building upon RRT*, BU-RRT* is shown to be asymptotically optimal, enabling it to efficiently generate and optimize robust, dynamically feasible trajectories. For large and/or unbounded uncertainties, probabilistically feasible planning is provided through the proposed chance-constrained RRT (CC-RRT) algorithm. Paths generated by CC-RRT are guaranteed probabilistically feasible for linear systems under Gaussian uncertainty, with extensions considered for nonlinear dynamics, output models, and/or non-Gaussian uncertainty. Probabilistic constraint satisfaction is represented in terms of chance constraints, extending existing approaches by considering both internal and external uncertainty, subject to time-step-wise and path-wise feasibility constraints. An explicit bound on the total risk of constraint violation is developed which can be efficiently evaluated online for each trajectory. The proposed CC-RRT* algorithm extends this approach to provide asymptotic optimality guarantees; an admissible risk-based objective uses the risk bounds to incentivize risk-averse trajectories. Applications of this framework are shown for several motion planning domains, including parafoil terminal guidance and urban navigation, where the system is subject to challenging environmental and uncertainty characterizations. Hardware results demonstrate a mobile robot utilizing this framework to safely avoid dynamic obstacles.<br>by Brandon Luders.<br>Ph. D.

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Pettersson, Per Olof. "Sampling-based Path Planning for an Autonomous Helicopter." Licentiate thesis, Linköping : Univ, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5270.

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Book chapters on the topic "Sampling-based motion planning"

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Lindemann, Stephen R., and Steven M. LaValle. "Current Issues in Sampling-Based Motion Planning." In Springer Tracts in Advanced Robotics. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11008941_5.

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Bialkowski, Joshua, Sertac Karaman, Michael Otte, and Emilio Frazzoli. "Efficient Collision Checking in Sampling-Based Motion Planning." In Springer Tracts in Advanced Robotics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36279-8_22.

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Janson, Lucas, Brian Ichter, and Marco Pavone. "Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance." In Springer Proceedings in Advanced Robotics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60916-4_29.

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Cortés, Juan, and Thierry Siméon. "Sampling-Based Motion Planning under Kinematic Loop-Closure Constraints." In Springer Tracts in Advanced Robotics. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/10991541_7.

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Huh, Jinwook, Ömür Arslan, and Daniel D. Lee. "Probabilistically Safe Corridors to Guide Sampling-Based Motion Planning." In Springer Proceedings in Advanced Robotics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95459-8_19.

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Ogay, Dmitriy, Jee-Hwan Ryu, and Eun-Gyung Kim. "Polar Histogram Based Sampling Method for Autonomous Vehicle Motion Planning." In Frontiers of Intelligent Autonomous Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35485-4_19.

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Ogay, Dmitriy, Jee-Hwan Ryu, and Eun-Gyung Kim. "Polar Histogram Based Sampling Method for Autonomous Vehicle Motion Planning." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33926-4_70.

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Shahidi, Amir, Thomas Kinzig, Mathias Hüsing, and Burkhard Corves. "Kinematically Adapted Sampling-Based Motion Planning Algorithm for Robotic Manipulators." In Advances in Robot Kinematics 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08140-8_49.

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Plaku, Erion. "Guiding Sampling-Based Motion Planning by Forward and Backward Discrete Search." In Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33503-7_29.

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Wells, Andrew, and Erion Plaku. "Adaptive Sampling-Based Motion Planning for Mobile Robots with Differential Constraints." In Towards Autonomous Robotic Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22416-9_32.

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Conference papers on the topic "Sampling-based motion planning"

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"Sampling-based Multi-robot Motion Planning." In Special Session on Intelligent Vehicle Controls & Intelligent Transportation Systems. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004605005490554.

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Arslan, Omur, Vincent Pacelli, and Daniel E. Koditschek. "Sensory steering for sampling-based motion planning." In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8206218.

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Englert, Peter, Isabel Rayas Fernández, Ragesh Ramachandran, and Gaurav Sukhatme. "Sampling-Based Motion Planning on Sequenced Manifolds." In Robotics: Science and Systems 2021. Robotics: Science and Systems Foundation, 2021. http://dx.doi.org/10.15607/rss.2021.xvii.039.

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Burns, Brendan, and Oliver Brock. "Sampling-Based Motion Planning With Sensing Uncertainty." In 2007 IEEE International Conference on Robotics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/robot.2007.363984.

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Bhatia, Amit, Lydia E. Kavraki, and Moshe Y. Vardi. "Sampling-based motion planning with temporal goals." In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010). IEEE, 2010. http://dx.doi.org/10.1109/robot.2010.5509503.

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Kim, Chyon Hae, Shimon Sugawara, and Shigeki Sugano. "Linear prediction based uniform state sampling for sampling based motion planning systems." In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012). IEEE, 2012. http://dx.doi.org/10.1109/humanoids.2012.6651603.

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Boeuf, Alexandre, Juan Cortes, Rachid Alami, and Thierry Simeon. "Enhancing sampling-based kinodynamic motion planning for quadrotors." In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7353709.

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Karaman, S., and E. Frazzoli. "Incremental Sampling-based Algorithms for Optimal Motion Planning." In Robotics: Science and Systems 2010. Robotics: Science and Systems Foundation, 2010. http://dx.doi.org/10.15607/rss.2010.vi.034.

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Hollinger, Geoffrey, and Gaurav Sukhatme. "Sampling-based Motion Planning for Robotic Information Gathering." In Robotics: Science and Systems 2013. Robotics: Science and Systems Foundation, 2013. http://dx.doi.org/10.15607/rss.2013.ix.051.

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Solovey, Kiril, and Michal Kleinbort. "The Critical Radius in Sampling-based Motion Planning." In Robotics: Science and Systems 2018. Robotics: Science and Systems Foundation, 2018. http://dx.doi.org/10.15607/rss.2018.xiv.017.

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Reports on the topic "Sampling-based motion planning"

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Ruiz, Javier Matias. Predictive Sampling-Based Robot Motion Planning in Unmodeled Dynamic Environments. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1573326.

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Boardman, Beth Leigh. Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1400115.

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