Real-time informed path sampling for motion planning search

Knepper, Ross A.; Mason, Matthew T.

doi:10.1177/0278364912456444

Cited by 21 publications

(13 citation statements)

References 49 publications

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“…Waypoint positions are planned in a continuous domain, while state constraints and curvature constraints are checked exactly, i.e., without any approximations and using analytically derived conditions. This differentiates the proposed method from the ones available in the literature and is congruent with the ongoing trend of seeking more efficient constraint checking procedures (visible in works such as [22]), and with efforts to guarantee constraint satisfaction in a continuous domain, see e.g., [25]. The benefits and necessity of seeking feedback control policies as opposed to open-loop controls or parametric paths has been also argued extensively in [5,29,38,39,44].…”

Section: Related Worksupporting

confidence: 67%

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Gawron

Michałek

2017

J Intell Robot Syst

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Integrated motion planning and control for the purposes of maneuvering mobile robots under state-and input constraints is a problem of vital practical importance in applications of mobile robots such as autonomous transportation. Those constraints arise naturally in practice due to specifics of robot mechanical construction and the presence of obstacles in motion environment. In contrast to approaches focusing on feedback control design under the assumption of given reference motion or motion planning with neglection of subsequent feedback motion execution, we adopt a controller-driven motion planning paradigm, which has recently gained attention of many researchers. It postulates design of motion planning algorithms dedicated to specific feedback control policies, which compute a sequence of feedback control subtasks instead of classically planned open-loop controls or parametric paths. In this spirit, we propose a motion planning algorithm driven by the VFO (Vector Field Orientation) control law for the waypointfollowing task. Presented analysis of the VFO control law reveals its beneficial properties, which are subsequently utilized to solve a generally nonlinear and non-convex optimal motion planning problem by formulating it as a mixed-integer linear program (MILP). The solution proposed in this paper yields a waypoint sequence, which is designed for execution by application of the VFO control law to drive a robot to a prescribed final configuration under an input constraint imposed by bounded curvature of robot motion and state constraints resulting from a convex decomposition of task space. Satisfaction of these constraints is guaranteed analytically and exactly, i.e., without utilization of numerical approximations. Moreover, for a given discrete set of possible waypoint orientations, the proposed algorithm computes plans optimal w.r.t. given cost functional, which can be any convex linear combination of quantities such as robot path length, curvature of robot motion, distance to imposed state constraints, etc. Furthermore, the planning algorithm exploits the possibility of both forward or backward movement of the robot to allow maneuvering in demanding environments. Generated waypoint sequences are a compact representation of a motion plan, which can be immediately executed with the VFO controller without any additional postprocessing. Validity of the proposed approach has 266 J Intell Robot Syst (2018) 89:265-297 been confirmed by simulation studies and experimental motion execution with a laboratory-scale mobile robot.

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Section: Related Worksupporting

confidence: 67%

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Gawron

Michałek

2017

J Intell Robot Syst

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“…The utility-guided approach [4], maximizes the information gain of the next sample by predicting the sample utility with a probability distribution. Informed path sampling [5] learns a probability distribution to generate paths which are robust to obstacle uncertainties. These methods bias the sampling distribution of SMP but are not designed to include information from previous experiences.…”

Section: Related Workmentioning

confidence: 99%

Visual Repetition Sampling for Robot Manipulation Planning

Puang

Lehner

Márton

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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One of the main challenges in sampling-based motion planners is to find an efficient sampling strategy. While methods such as Rapidly-exploring Random Tree (RRT) have shown to be more reliable in complex environments than optimization-based methods, they often require longer planning times, which reduces their usability for real-time applications. Recently, biased sampling methods have shown to remedy this issue. For example Gaussian Mixture Models (GMMs) have been used to sample more efficiently in feasible regions of the configuration space. Once the GMM is learned, however, this approach does not adapt its biases to individual planning scene during inference. Hence, we propose in this work a more efficient sampling strategy to further bias the GMM based on visual input upon query. We employ an autoencoder trained entirely in simulation to extract features from depth images and use the latent representation to adjust the weights of each Gaussian components in the GMM. We show empirically that this improves the sampling efficiency of an RRT motion planner in both real and simulated scenes.

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“…Burns et al [13] introduce the utility-guided approach, learning a distribution to predict the next sample with the highest utility, maximizing the information gain. Informed path sampling [14] adapts the sampling distribution to find paths with high variability. Kobilarov et al [15] employ the cross-entropy method by learning a Gaussian Mixture Model and optimizing the sampling distribution of one query to converge towards an optimal path.…”

Section: R Wmentioning

confidence: 99%

“…The construction is detailed in Algorithm 1. The individual steps are 1) Extracting key configurations from the previous paths, learning a GMM based on the key configurations (steps 3-7) and 2) Connecting the components of the GMM based on the previous paths (steps [8][9][10][11][12][13][14][15][16].…”

Section: A Constructing the Repetition Roadmapmentioning

confidence: 99%

The Repetition Roadmap for Repetitive Constrained Motion Planning

Lehner

Albu‐Schäffer

2018

IEEE Robot. Autom. Lett.

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We present the Repetition Roadmap, a motion planner which effectively exploits the repetitiveness of a set of tasks with small variations to efficiently compute new motions. The method learns an abstract roadmap of probability distributions for the configuration space of a particular task set from previous solution paths. We show how to construct the Repetition Roadmap by learning a Gaussian Mixture Model and connecting the distribution components based on the connectivity information of the prior paths. We present an algorithm which exploits the information in the Repetition Roadmap to guide the search for solutions of similar tasks. We illustrate our method in a maze, which explains the construction of the Repetition Roadmap and how the method can generalize over different environments. We show how to apply the Repetition Roadmap to similar constrained manipulation tasks and present our results including significant speedup in computation time when compared to Uniform and Adaptive Sampling.

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Real-time informed path sampling for motion planning search

Cited by 21 publications

References 49 publications

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Visual Repetition Sampling for Robot Manipulation Planning

The Repetition Roadmap for Repetitive Constrained Motion Planning

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