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

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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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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Tsianos, Konstantinos I., Ioan A. Sucan, and Lydia E. Kavraki. "Sampling-based robot motion planning: Towards realistic applications." Computer Science Review 1, no. 1 (2007): 2–11. http://dx.doi.org/10.1016/j.cosrev.2007.08.002.

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Kingston, Zachary, Mark Moll, and Lydia E. Kavraki. "Exploring implicit spaces for constrained sampling-based planning." International Journal of Robotics Research 38, no. 10-11 (2019): 1151–78. http://dx.doi.org/10.1177/0278364919868530.

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We present a review and reformulation of manifold constrained sampling-based motion planning within a unifying framework, IMACS (implicit manifold configuration space). IMACS enables a broad class of motion planners to plan in the presence of manifold constraints, decoupling the choice of motion planning algorithm and method for constraint adherence into orthogonal choices. We show that implicit configuration spaces defined by constraints can be presented to sampling-based planners by addressing two key fundamental primitives, sampling and local planning, and that IMACS preserves theoretical properties of probabilistic completeness and asymptotic optimality through these primitives. Within IMACS, we implement projection- and continuation-based methods for constraint adherence, and demonstrate the framework on a range of planners with both methods in simulated and realistic scenarios. Our results show that the choice of method for constraint adherence depends on many factors and that novel combinations of planners and methods of constraint adherence can be more effective than previous approaches. Our implementation of IMACS is open source within the Open Motion Planning Library and is easily extended for novel planners and constraint spaces.

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Le, Duong, and Erion Plaku. "Cooperative Multi-Robot Sampling-Based Motion Planning with Dynamics." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 513–21. http://dx.doi.org/10.1609/icaps.v27i1.13854.

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This paper develops an effective, cooperative, and probabilistically-complete multi-robot motion planner. The approach takes into account geometric and differential constraints imposed by the obstacles and the robot dynamics by using sampling to expand a motion tree in the composite state space of all the robots. Scalability and efficiency is achieved by using solutions to a simplified problem representation that does not take dynamics into account to guide the motion-tree expansion. The heuristic solutions are obtained by constructing roadmaps over low-dimensional configuration spaces and relying on cooperative multi-agent graph search to effectively find graph routes. Experimental results with second-order vehicle models operating in complex environments, where cooperation among the robots is required to find solutions, demonstrate significant improvements over related work.

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Janson, Lucas, Brian Ichter, and Marco Pavone. "Deterministic sampling-based motion planning: Optimality, complexity, and performance." International Journal of Robotics Research 37, no. 1 (2017): 46–61. http://dx.doi.org/10.1177/0278364917714338.

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Probabilistic sampling-based algorithms, such as the probabilistic roadmap (PRM) and the rapidly exploring random tree (RRT) algorithms, represent one of the most successful approaches to robotic motion planning, due to their strong theoretical properties (in terms of probabilistic completeness or even asymptotic optimality) and remarkable practical performance. Such algorithms are probabilistic in that they compute a path by connecting independently and identically distributed (i.i.d.) random points in the configuration space. Their randomization aspect, however, makes several tasks challenging, including certification for safety-critical applications and use of offline computation to improve real-time execution. Hence, an important open question is whether similar (or better) theoretical guarantees and practical performance could be obtained by considering deterministic, as opposed to random, sampling sequences. The objective of this paper is to provide a rigorous answer to this question. Specifically, we first show that PRM, for a certain selection of tuning parameters and deterministic low-dispersion sampling sequences, is deterministically asymptotically optimal, in other words, it returns a path whose cost converges deterministically to the optimal one as the number of points goes to infinity. Second, we characterize the convergence rate, and we find that the factor of sub-optimality can be very explicitly upper-bounded in terms of the[Formula: see text] -dispersion of the sampling sequence and the connection radius of PRM. Third, we show that an asymptotically optimal version of PRM exists with computational and space complexity arbitrarily close to [Formula: see text] (the theoretical lower bound), where n is the number of points in the sequence. This is in contrast to the [Formula: see text] complexity results for existing asymptotically optimal probabilistic planners. Fourth, we discuss extending our theoretical results and insights to other batch-processing algorithms such as FMT*, to non-uniform sampling strategies, to k-nearest-neighbor implementations, and to differentially constrained problems. Importantly, our main theoretical tool is the [Formula: see text]-dispersion, an interesting consequence of which is that all our theoretical results also hold for low-[Formula: see text]-dispersion random sampling (which i.i.d. sampling does not satisfy). In other words, achieving deterministic guarantees is really a matter of i.i.d. sampling versus non-i.i.d. low-dispersion sampling (with deterministic sampling as a prominent case), as opposed to random versus deterministic. Finally, through numerical experiments, we show that planning with deterministic (or random) low-dispersion sampling generally provides superior performance in terms of path cost and success rate.

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Garrett, Caelan Reed, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. "Sampling-based methods for factored task and motion planning." International Journal of Robotics Research 37, no. 13-14 (2018): 1796–825. http://dx.doi.org/10.1177/0278364918802962.

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This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing.

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Rickert, Markus, Arne Sieverling, and Oliver Brock. "Balancing Exploration and Exploitation in Sampling-Based Motion Planning." IEEE Transactions on Robotics 30, no. 6 (2014): 1305–17. http://dx.doi.org/10.1109/tro.2014.2340191.

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Plaku, E., K. E. Bekris, B. Y. Chen, A. M. Ladd, and L. E. Kavraki. "Sampling-based roadmap of trees for parallel motion planning." IEEE Transactions on Robotics 21, no. 4 (2005): 597–608. http://dx.doi.org/10.1109/tro.2005.847599.

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Agha-mohammadi, Ali-akbar, Suman Chakravorty, and Nancy M. Amato. "FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements." International Journal of Robotics Research 33, no. 2 (2013): 268–304. http://dx.doi.org/10.1177/0278364913501564.

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Wang, Lin Lin, Hong Jian Wang, and Li Xin Pan. "Autonomous Underwater Vehicle Motion Planning via Sampling Based Model Predictive Control." Applied Mechanics and Materials 670-671 (October 2014): 1370–77. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1370.

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In order to improve the ability of independent planning for AUV (Autonomous Underwater Vehicle), a new method of motion planning based on SBMPC (Sampling Based Model Predictive Control) is proposed, which is combined with model predictive control theory. Input sampling is directly made in control variable space, and sampling data is substituted into the predictive model of AUV motion. Then surge velocity and yaw angular rate in next sampling time are obtained through calculations. If predictive states are evaluated according to the performance index previously defined, optimal prediction of AUV states in next sampling can be used to realize motion planning optimization. Effects of three sampling methods (viz. uniform sampling, Halton sampling and CVT sampling) on motion planning performance are also compared in simulations. Statistical analysis demonstrates that CVT sampling points has the most uniform coverage in two-dimensional plane when amount of sampling points is the same for three methods. Simulation results show that it is effective and feasible to plan a route for AUV by using CVT sampling and rolling optimization of MPC (Model Predictive Control).

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Mi, Kai, Haojian Zhang, Jun Zheng, Jianhua Hu, Dengxiang Zhuang, and Yunkuan Wang. "A sampling-based optimized algorithm for task-constrained motion planning." International Journal of Advanced Robotic Systems 16, no. 3 (2019): 172988141984737. http://dx.doi.org/10.1177/1729881419847378.

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We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization structure similar to dynamic programming into the algorithm. The proposed algorithm can generate an asymptotically optimized smooth path in joint space, which continuously satisfies task space constraints and avoids obstacles. We have confirmed that the proposed algorithm is probabilistically complete and asymptotically optimized. Finally, we conduct multiple experiments with path length and tracking error as optimization targets and the planning results reflect the optimization effect of the algorithm.

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Ma, Liang, Jianru Xue, Kuniaki Kawabata, Jihua Zhu, Chao Ma, and Nanning Zheng. "Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving." IEEE Transactions on Intelligent Transportation Systems 16, no. 4 (2015): 1961–76. http://dx.doi.org/10.1109/tits.2015.2389215.

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Wang, Zhuping, Yunsong Li, Hao Zhang, Chun Liu, and Qijun Chen. "Sampling-Based Optimal Motion Planning With Smart Exploration and Exploitation." IEEE/ASME Transactions on Mechatronics 25, no. 5 (2020): 2376–86. http://dx.doi.org/10.1109/tmech.2020.2973327.

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Ichnowski, Jeffrey, Yahav Avigal, Vishal Satish, and Ken Goldberg. "Deep learning can accelerate grasp-optimized motion planning." Science Robotics 5, no. 48 (2020): eabd7710. http://dx.doi.org/10.1126/scirobotics.abd7710.

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Robots for picking in e-commerce warehouses require rapid computing of efficient and smooth robot arm motions between varying configurations. Recent results integrate grasp analysis with arm motion planning to compute optimal smooth arm motions; however, computation times on the order of tens of seconds dominate motion times. Recent advances in deep learning allow neural networks to quickly compute these motions; however, they lack the precision required to produce kinematically and dynamically feasible motions. While infeasible, the network-computed motions approximate the optimized results. The proposed method warm starts the optimization process by using the approximate motions as a starting point from which the optimizing motion planner refines to an optimized and feasible motion with few iterations. In experiments, the proposed deep learning–based warm-started optimizing motion planner reduces compute and motion time when compared to a sampling-based asymptotically optimal motion planner and an optimizing motion planner. When applied to grasp-optimized motion planning, the results suggest that deep learning can reduce the computation time by two orders of magnitude (300×), from 29 s to 80 ms, making it practical for e-commerce warehouse picking.

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McMahon, Troy, Shawna Thomas, and Nancy M. Amato. "Sampling-based motion planning with reachable volumes for high-degree-of-freedom manipulators." International Journal of Robotics Research 37, no. 7 (2018): 779–817. http://dx.doi.org/10.1177/0278364918779555.

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Motion planning for constrained systems is a version of the motion planning problem in which the motion of a robot is limited by constraints. For example, one can require that a humanoid robot such as a PR2 remain upright by constraining its torso to be above its base or require that an object such as a bucket of water remain upright by constraining the vertices of the object to be parallel to the robot’s base. Grasping can be modeled by requiring that the end effectors of the robot be located at specified handle positions. Constraints might require that the robot remain in contact with a surface, or that certain joints of the robot remain in contact with each other (e.g., closed chains). Such problems are particularly difficult because the constraints form a manifold in C-space, and planning must be restricted to this manifold. High-degree-of-freedom motion planning and motion planning for constrained systems has applications in parallel robotics, grasping and manipulation, computational biology and molecular simulations, and animation. We introduce a new concept, reachable volumes, that are a geometric representation of the regions the joints and end effectors of a robot can reach, and use it to define a new planning space called RV-space where all points automatically satisfy a problem’s constraints. Visualizations of reachable volumes can enable operators to see the regions of workspace that different parts of the robot can reach. Samples and paths generated in RV-space naturally conform to constraints, making planning for constrained systems no more difficult than planning for unconstrained systems. Consequently, constrained motion planning problems that were previously difficult or unsolvable become manageable and in many cases trivial. We introduce tools and techniques to extend the state-of-the-art sampling-based motion planning algorithms to RV-space. We define a reachable volumes sampler, a reachable volumes local planner, and a reachable volumes distance metric. We showcase the effectiveness of RV-space by applying these tools to motion planning problems for robots with constraints on the end effectors and/or internal joints of the robot. We show that RV-based planners are more efficient than existing methods, particularly for higher-dimensional problems, solving problems with 1000 or more degrees of freedom for multi-loop and tree-like linkages.

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Karlsson, Jesper, Fernando S. Barbosa, and Jana Tumova. "Sampling-based Motion Planning with Temporal Logic Missions and Spatial Preferences." IFAC-PapersOnLine 53, no. 2 (2020): 15537–43. http://dx.doi.org/10.1016/j.ifacol.2020.12.2397.

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Zhang, Yifan, Jinghuai Zhang, Jindi Zhang, Jianping Wang, Kejie Lu, and Jeff Hong. "Integrating Algorithmic Sampling-Based Motion Planning with Learning in Autonomous Driving." ACM Transactions on Intelligent Systems and Technology 13, no. 3 (2022): 1–27. http://dx.doi.org/10.1145/3469086.

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Sampling-based motion planning (SBMP) is a major algorithmic trajectory planning approach in autonomous driving given its high efficiency and outstanding performance in practice. However, driving safety still calls for further refinement of SBMP. In this article we organically integrate algorithmic motion planning with learning models to improve SBMP in highway traffic scenarios from the following two perspectives. First, given the number of points to be sampled, we develop a new model to sample “important” points for SBMP by predicting the intention of surrounding vehicles and learning the distribution of human drivers’ trajectory. Second, we empirically study the relationship between the number of sample points and the environment, which is largely ignored in conventional SBMP. Then, we provide a guideline to select the appropriate number of points to be sampled under different scenarios to guarantee efficiency. The simulation experiments are conducted based on the vehicle trajectory dataset NGSIM. The results show that the proposed sampling strategy outperforms existing sampling strategies in terms of the computing time, traveling time, and smoothness of the trajectory.

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Bialkowski, Joshua, Michael Otte, Sertac Karaman, and Emilio Frazzoli. "Efficient collision checking in sampling-based motion planning via safety certificates." International Journal of Robotics Research 35, no. 7 (2016): 767–96. http://dx.doi.org/10.1177/0278364915625345.

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Solovey, Kiril, Oren Salzman, and Dan Halperin. "New perspective on sampling-based motion planning via random geometric graphs." International Journal of Robotics Research 37, no. 10 (2018): 1117–33. http://dx.doi.org/10.1177/0278364918802957.

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Roadmaps constructed by many sampling-based motion planners coincide, in the absence of obstacles, with standard models of random geometric graphs (RGGs). Those models have been studied for several decades and by now a rich body of literature exists analyzing various properties and types of RGGs. In their seminal work on optimal motion planning, Karaman and Frazzoli conjectured that a sampling-based planner has a certain property if the underlying RGG has this property as well. In this paper, we settle this conjecture and leverage it for the development of a general framework for the analysis of sampling-based planners. Our framework, which we call localization–tessellation, allows for easy transfer of arguments on RGGs from the free unit hypercube to spaces punctured by obstacles, which are geometrically and topologically much more complex. We demonstrate its power by providing alternative and (arguably) simple proofs for probabilistic completeness and asymptotic (near-)optimality of probabilistic roadmaps (PRMs) in Euclidean spaces. Furthermore, we introduce three variants of PRMs, analyze them using our framework, and discuss the implications of the analysis.

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Sun, Wen, Sachin Patil, and Ron Alterovitz. "High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning." IEEE Transactions on Robotics 31, no. 1 (2015): 104–16. http://dx.doi.org/10.1109/tro.2014.2380273.

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Zhang, Hongwen, and Zhanxia Zhu. "Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics." Applied Sciences 10, no. 24 (2020): 9137. http://dx.doi.org/10.3390/app10249137.

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Motion planning is one of the most important technologies for free-floating space robots (FFSRs) to increase operation safety and autonomy in orbit. As a nonholonomic system, a first-order differential relationship exists between the joint angle and the base attitude of the space robot, which makes it pretty challenging to implement the relevant motion planning. Meanwhile, the existing planning framework must solve inverse kinematics for goal configuration and has the limitation that the goal configuration and the initial configuration may not be in the same connected domain. Thus, faced with these questions, this paper investigates a novel motion planning algorithm based on rapidly-exploring random trees (RRTs) for an FFSR from an initial configuration to a goal end-effector (EE) pose. In a motion planning algorithm designed to deal with differential constraints and restrict base attitude disturbance, two control-based local planners are proposed, respectively, for random configuration guiding growth and goal EE pose-guiding growth of the tree. The former can ensure the effective exploration of the configuration space, and the latter can reduce the possibility of occurrence of singularity while ensuring the fast convergence of the algorithm and no violation of the attitude constraints. Compared with the existing works, it does not require the inverse kinematics to be solved while the planning task is completed and the attitude constraint is preserved. The simulation results verify the effectiveness of the algorithm.

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Peng Cheng, E. Frazzoli, and S. LaValle. "Improving the Performance of Sampling-Based Motion Planning With Symmetry-Based Gap Reduction." IEEE Transactions on Robotics 24, no. 2 (2008): 488–94. http://dx.doi.org/10.1109/tro.2007.913993.

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Ghaffari Jadidi, Maani, Jaime Valls Miro, and Gamini Dissanayake. "Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring." International Journal of Robotics Research 38, no. 6 (2019): 658–85. http://dx.doi.org/10.1177/0278364919844575.

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We propose a sampling-based motion-planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly exploring information-gathering algorithms and benefits from the advantages of sampling-based optimal motion-planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information-gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle.

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Plaku, Erion. "Robot Motion Planning with Dynamics as Hybrid Search." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1415–21. http://dx.doi.org/10.1609/aaai.v27i1.8544.

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This paper presents a framework for motion planning with dynamics as hybrid search over the continuous space of feasible motions and the discrete space of a low-dimensional workspace decomposition. Each step of the hybrid search consists of expanding a frontier of regions in the discrete space using cost heuristics as guide followed by sampling-based motion planning to expand a tree of feasible motions in the continuous space to reach the frontier. The approach is geared towards robots with many degrees-of-freedom (DOFs), nonlinear dynamics, and nonholonomic constraints, which make it difficult to follow discrete-search paths to the goal, and hence require a tight coupling of motion planning and discrete search. Comparisons to related work show significant computational speedups.

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Zhang, Yifan, Jinghuai Zhang, Jindi Zhang, Jianping Wang, Kejie Lu, and Jeff Hong. "A Novel Learning Framework for Sampling-Based Motion Planning in Autonomous Driving." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1202–9. http://dx.doi.org/10.1609/aaai.v34i01.5473.

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Sampling-based motion planning (SBMP) is a major trajectory planning approach in autonomous driving given its high efficiency in practice. As the core of SBMP schemes, sampling strategy holds the key to whether a smooth and collision-free trajectory can be found in real-time. Although some bias sampling strategies have been explored in the literature to accelerate SBMP, the trajectory generated under existing bias sampling strategies may lead to sharp lane changing. To address this issue, we propose a new learning framework for SBMP. Specifically, we develop a novel automatic labeling scheme and a 2-Stage prediction model to improve the accuracy in predicting the intention of surrounding vehicles. We then develop an imitation learning scheme to generate sample points based on the experience of human drivers. Using the prediction results, we design a new bias sampling strategy to accelerate the SBMP algorithm by strategically selecting necessary sample points that can generate a smooth and collision-free trajectory and avoid sharp lane changing. Data-driven experiments show that the proposed sampling strategy outperforms existing sampling strategies, in terms of the computing time, traveling time, and smoothness of the trajectory. The results also show that our scheme is even better than human drivers.

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Corves, Burkhard, and Amir Shahidi. "Kinematic Graph for Motion Planning of Robotic Manipulators." Robotics 11, no. 5 (2022): 105. http://dx.doi.org/10.3390/robotics11050105.

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We introduce a kinematic graph in this article. A kinematic graph results from structuring the data obtained from the sampling method for sampling-based motion planning algorithms in robotics with the motivation to adapt the method to the positioning problem of robotic manipulators. The term kinematic graph emphasises the fact that any path computed by sampling-based motion planning algorithms using a kinematic graph is guaranteed to correspond to a feasible motion for the positioning of the robotic manipulator. We propose methods to combine the information from the configuration and task spaces of the robotic manipulators to cluster the samples. The kinematic graph is the result of this systematic clustering and a tremendous reduction in the size of the problem. Hence, using a kinematic graph, it is possible to effectively employ sampling-based motion planning algorithms for robotic manipulators, where the problem is defined in higher dimensions than those for which these algorithms were developed. Other barriers that hindered adequate utilisation of such algorithms for robotic manipulators with articulated arms, such as the non-injective surjection of the forward kinematic function, are also addressed in the structure of the kinematic graph.

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Le, Duong, and Erion Plaku. "Cooperative, Dynamics-based, and Abstraction-Guided Multi-robot Motion Planning." Journal of Artificial Intelligence Research 63 (October 31, 2018): 361–90. http://dx.doi.org/10.1613/jair.1.11244.

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This paper presents an effective, cooperative, and probabilistically-complete multi-robot motion planner that enables each robot to move to a desired location while avoiding collisions with obstacles and other robots. The approach takes into account not only the geometric constraints arising from collision avoidance, but also the differential constraints imposed by the motion dynamics of each robot. This makes it possible to generate collision-free and dynamically-feasible trajectories that can be executed in the physical world.The salient aspect of the approach is the coupling of sampling-based motion planning to handle the complexity arising from the obstacles and robot dynamics with multi-agent search to find solutions over a suitable discrete abstraction. The discrete abstraction is obtained by constructing roadmaps to solve a relaxed problem that accounts for the obstacles but not the dynamics. Sampling-based motion planning expands a motion tree in the composite state space of all the robots by adding collision-free and dynamically-feasible trajectories as branches. Efficiency is obtained by using multi-agent search to find non-conflicting routes over the discrete abstraction which serve as heuristics to guide the motion-tree expansion. When little or no progress is made, the routes are penalized and the multi-agent search is invoked again to find alternative routes. This synergistic coupling makes it possible to effectively plan collision-free and dynamically-feasible motions that enable each robot to reach its goal. Experiments using vehicle models with nonlinear dynamics operating in complex environments, where cooperation among robots is required, show significant speedups over related work.

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Xu, Jing, Jinghui Qiao, Xu Han, Yu He, Hongkun Tian, and Zhe Wei. "A Random Sampling-Based Method via Gaussian Process for Motion Planning in Dynamic Environments." Applied Sciences 12, no. 24 (2022): 12646. http://dx.doi.org/10.3390/app122412646.

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Motion planning is widely applied to industrial robots, medical robots, bionic robots, and smart vehicles. Most work environments of robots are not static, which leads to difficulties for robot motion planning. We present a dynamic Gaussian local planner (DGLP) method to solve motion planning problems in dynamic environments. In a dynamic environment, dynamic obstacles sometimes make part of the global path invalid, so the local invalid path needs to be local re-planned online. Compared with the node sampling-based methods building large-scale random trees or roadmaps, the Gaussian random path sampling (GRPS) module integrated in the DGLP directly samples smooth random paths discretized into sparse nodes to improve the local path re-planning efficiency. We also provide the path end orientation constraint (PEOC) method for the local re-planning paths in order to merge them smoothly into the global paths. In the robot experiments, the average planning time of the DGLP is 0.04s, which is at least 92.31% faster than the test methods, and its comprehensive evaluation scores, which consider the consuming time, path quality, and success rate of local re-planning, are at least 44.92% higher than the test methods. The results demonstrate that the proposed DGLP method is able to efficiently provide high-quality local re-planning paths in dynamic environments.

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Jeong, Yonghwan, Seonwook Kim, Byeong Rim Jo, Hyunseok Shin, and Kyongsu Yi. "Sampling Based Vehicle Motion Planning for Autonomous Valet Parking with Moving Obstacles." International Journal of Automotive Engineering 9, no. 4 (2018): 215–22. http://dx.doi.org/10.20485/jsaeijae.9.4_215.

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Ha, Jung-Su, Han-Lim Choi, and Jeong Hwan Jeon. "Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems." International Journal of Applied Mathematics and Computer Science 28, no. 1 (2018): 155–68. http://dx.doi.org/10.2478/amcs-2018-0012.

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AbstractThis paper extends the RRT* algorithm, a recently developed but widely used sampling based optimal motion planner, in order to effectively handle nonlinear kinodynamic constraints. Nonlinearity in kinodynamic differential constraints often leads to difficulties in choosing an appropriate distance metric and in computing optimized trajectory segments in tree construction. To tackle these two difficulties, this work adopts the affine quadratic regulator-based pseudo-metric as the distance measure and utilizes iterative two-point boundary value problem solvers to compute the optimized segments. The proposed extension then preserves the inherent asymptotic optimality of the RRT* framework, while efficiently handling a variety of kinodynamic constraints. Three numerical case studies validate the applicability of the proposed method.

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Otte, Michael, and Emilio Frazzoli. "RRTX: Asymptotically optimal single-query sampling-based motion planning with quick replanning." International Journal of Robotics Research 35, no. 7 (2015): 797–822. http://dx.doi.org/10.1177/0278364915594679.

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Plaku, Erion. "Region-Guided and Sampling-Based Tree Search for Motion Planning With Dynamics." IEEE Transactions on Robotics 31, no. 3 (2015): 723–35. http://dx.doi.org/10.1109/tro.2015.2424031.

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Banzhaf, Holger, Paul Sanzenbacher, Ulrich Baumann, and J. Marius Zollner. "Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning." IEEE Robotics and Automation Letters 4, no. 2 (2019): 1053–60. http://dx.doi.org/10.1109/lra.2019.2893975.

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Pendleton, Scott Drew, Wei Liu, Hans Andersen, et al. "Numerical Approach to Reachability-Guided Sampling-Based Motion Planning Under Differential Constraints." IEEE Robotics and Automation Letters 2, no. 3 (2017): 1232–39. http://dx.doi.org/10.1109/lra.2017.2651940.

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Murrieta-Cid, Rafael, BenjamÍn Tovar, and Seth Hutchinson. "A Sampling-Based Motion Planning Approach to Maintain Visibility of Unpredictable Targets." Autonomous Robots 19, no. 3 (2005): 285–300. http://dx.doi.org/10.1007/s10514-005-4052-0.

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Liu, Yiyang, Yang Zhao, Shuaihua Yan, Chunhe Song, and Fei Li. "A Sampling-Based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning." Sensors 22, no. 23 (2022): 9203. http://dx.doi.org/10.3390/s22239203.

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Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* increases rapidly with the number of potential path vertices, resulting in slow convergence or even an inability to converge, which seriously reduces the performance and practical value of RRT*. To solve this issue, this paper proposes a two-phase motion planning algorithm named Metropolis RRT* (M-RRT*) based on the Metropolis acceptance criterion. First, to efficiently obtain the initial path and start the optimal path search phase earlier, an asymptotic vertex acceptance criterion is defined in the initial path estimation phase of M-RRT*. Second, to improve the convergence rate of the algorithm, a nonlinear dynamic vertex acceptance criterion is defined in the optimal path search phase, which preferentially accepts vertices that may improve the current path. The effectiveness of M-RRT* is verified by comparing it with existing algorithms through the simulation results in three test environments.

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Rosell, Jan, Raúl Suárez, Néstor García, and Muhayy Ud Din. "Planning Grasping Motions for Humanoid Robots." International Journal of Humanoid Robotics 16, no. 06 (2019): 1950041. http://dx.doi.org/10.1142/s0219843619500415.

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This paper addresses the problem of obtaining the required motions for a humanoid robot to perform grasp actions trying to mimic the coordinated hand–arm movements humans do. The first step is the data acquisition and analysis, which consists in capturing human movements while grasping several everyday objects (covering four possible grasp types), mapping them to the robot and computing the hand motion synergies for the pre-grasp and grasp phases (per grasp type). Then, the grasp and motion synthesis step is done, which consists in generating potential grasps for a given object using the four family types, and planning the motions using a bi-directional multi-goal sampling-based planner, which efficiently guides the motion planning following the synergies in a reduced search space, resulting in paths with human-like appearance. The approach has been tested in simulation, thoroughly compared with other state-of-the-art planning algorithms obtaining better results, and also implemented in a real robot.

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David Boon Moses, E., and G. Anitha. "Goal Directed Approach to Autonomous Motion Planning for Unmanned Vehicles." Defence Science Journal 67, no. 1 (2016): 45. http://dx.doi.org/10.14429/dsj.67.10295.

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<p>Advancement in the field of autonomous motion planning has enabled the realisation of fully autonomous unmanned vehicles. Sampling based motion planning algorithms have shown promising prospects in generating fast, effective and practical solutions to different motion planning problems in unmanned vehicles for both civilian and military applications. But the goal bias introduced by heuristic probability shaping to generate faster solution may result in local collisions. A simple, real-time method is proposed for goal direction by preferential selection of a state from a sampled pair of random state, based on the distance to goal. This limits the graph motions resulting in smaller data structure, making the algorithm optimised for time and solution length. This would enable unmanned vehicles to take shorter paths and avoid collisions in obstacle rich environment. The approach is analysed on a sampling based algorithm, rapidly-exploring random tree (RRT) which computes motion plans under constrain of time. This paper proposes an algorithm called ’goal directed RRT (GRRT)’ building on the basic RRT algorithm, providing an alternative to probabilistic goal biasing, thereby avoiding local collision. The approach is evaluated by benchmarking it with RRT algorithm for kinematic car, dynamic car and a quadrotor and the results show improvements in length of the motion plans and the time of computing.</p>

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Park, Jae-Han, and Tae-Woong Yoon. "Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning." Complexity 2018 (November 8, 2018): 1–23. http://dx.doi.org/10.1155/2018/9104720.

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Automated motion-planning technologies for industrial robots are critical for their application to Industry 4.0. Various sampling-based methods have been studied to generate the collision-free motion of articulated industrial robots. Such sampling-based methods provide efficient solutions to complex planning problems, but their limitations hinder the attainment of optimal results. This paper considers a method to obtain the optimal results in the roadmap algorithm that is representative of the sampling-based method. We define the coverage of a graph as a performance index of its optimality as constructed by a sampling-based algorithm and propose an optimization algorithm that can maximize graph coverage in the configuration space. The proposed method was applied to the model of an industrial robot, and the results of the simulation confirm that the roadmap graph obtained by the proposed algorithm can generate results of satisfactory quality in path-finding tests under various conditions.

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Lorente, María-Teresa, Eduardo Owen, and Luis Montano. "Model-based robocentric planning and navigation for dynamic environments." International Journal of Robotics Research 37, no. 8 (2018): 867–89. http://dx.doi.org/10.1177/0278364918775520.

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This work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented.

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Zha, Fusheng, Yizhou Liu, Wei Guo, et al. "Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning." Electronics 7, no. 12 (2018): 395. http://dx.doi.org/10.3390/electronics7120395.

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Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks.

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