Learning Implicit Sampling Distributions for Motion Planning

Zhang, Clark; Huh, Jinwook; Lee, Daniel D.

doi:10.1109/iros.2018.8594028

Cited by 59 publications

(25 citation statements)

References 30 publications

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“…Deep learning methods' flourishing motivates researchers to utilize them to solve path planning problems. [19] presents a policy-based search method to learn the implicit sampling distribution that is implemented in rejecting sampling manner. [20] proposes critical PRM, which leverages neural networks to identify the critical points in the maps for path planning.…”

Section: A Related Workmentioning

confidence: 99%

Enhance Connectivity of Promising Regions for Sampling-based Path Planning

Ma,

Li,

Liu

et al. 2021

Preprint

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Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many scenarios, such as the environment with a few dead ends. Our previous work proposes to use the promising region to guide the sampling process to address the issue. However, the predicted promising regions are often disconnected, which means they cannot connect the start and goal state, resulting in a lack of probabilistic completeness. This work focuses on enhancing the connectivity of predicted promising regions. Our proposed method regresses the connectivity probability of the edges in the x and y directions. In addition, it calculates the weight of the promising edges in loss to guide the neural network to pay more attention to the connectivity of the promising regions. We conduct a series of simulation experiments, and the results show that the connectivity of promising regions improves significantly. Furthermore, we analyze the effect of connectivity on samplingbased path planning algorithms and conclude that connectivity plays an essential role in maintaining algorithm performance.Note to Practitioners-This work is derived from the promising region prediction for sampling-based path planning. The sampling-based path planning methods have been widely used in robotics due to their efficiency. To further improve the efficiency of these algorithms, sampling in the promising region predicted by a neural network is introduced into the sampling procedure. However, the connectivity of the promising region has yet to be considered, and it will affect the performance of the algorithms in several aspects. To demonstrate this problem, we compare the performance of the neural heuristic algorithms under different connectivity statuses in this paper. Furthermore, to enhance the connectivity of the predicted promising region, the novel prediction output and loss function are proposed. The simulation results show improvements in the algorithms after utilizing our method.

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Section: A Related Workmentioning

confidence: 99%

Enhance Connectivity of Promising Regions for Sampling-based Path Planning

Ma,

Li,

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Among the literatures addressing the path planning, Qureshi et al [17] propose the motion planning network (MPN) to generate an end-to-end feasible path from the point cloud. Zhang et al [18] implement a policy-based search method to learn an implicit sampling distribution for specific environments. De et al [19] propose to learn a lattice planner control set to achieve path planning for autonomous vehicles.…”

Section: Related Workmentioning

confidence: 99%

Learning-based Fast Path Planning in Complex Environments

Liu¹,

Li²,

Tingguang³

et al. 2021

Preprint

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In this paper, we present a novel path planning algorithm to achieve fast path planning in complex environments. Most existing path planning algorithms are difficult to quickly find a feasible path in complex environments or even fail. However, our proposed framework can overcome this difficulty by using a learning-based prediction module and a samplingbased path planning module. The prediction module utilizes an auto-encoder-decoder-like convolutional neural network (CNN) to output a promising region where the feasible path probably lies in. In this process, the environment is treated as RGB image to feed in our designed CNN module, and the output is also RGB image. No extra computation is required so that we can maintain a high processing speed of 60 frame-persecond (FPS). Incorporated with a sampling-based path planner, we can extract a feasible path from the output image so that the robot can track it from start to goal. To demonstrate the advantage of the proposed algorithm, we compare it with conventional path planning algorithms in a series of simulation experiments. The results reveal that the proposed algorithm can achieve much better performance in terms of planning time, success rate, and path length.

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“…[25] proposed using a variant of GMM called Infinite GMMs, which allows the authors to learn typical GMM tuning parameters based on the expert data. [26] proposed modelling a rejection sampling technique using Markov Decision Processes, such that an offline policy can be modelled for environments which are similar.…”

Section: A Literature Reviewmentioning

confidence: 99%

Guaranteed Rejection-free Sampling Method Using Past Behaviours for Motion Planning of Autonomous Systems

Enevoldsen¹,

Galeazzi²

2021

Preprint

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The paper presents a novel learning-based sampling strategy that guarantees rejection-free sampling of the free space in both biased and uniform conditions. Data of past configurations of the autonomous system performing a repetitive task is leveraged to estimate a non-parametric probabilistic description of the region of the free space where feasible solutions of the motion planning problem are likely to be found. The tuning parameters of the kernel density estimator -the bandwidth and the kernel -are then used to properly alter the description of the free space such that no sampled configuration can fall outside the original free space.The paper demonstrates the proposed method on two case studies: the first showcases the sampling strategies on 2D historical data from real surface vessels, whereas the second applies the method on 3D drone data gathered from a real quadrotor system. Both instances show that the proposed biased and approximately uniform sampling schemes are able to guarantee rejection-free sampling of the considered workspaces.

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Learning Implicit Sampling Distributions for Motion Planning

Cited by 59 publications

References 30 publications

Enhance Connectivity of Promising Regions for Sampling-based Path Planning

Enhance Connectivity of Promising Regions for Sampling-based Path Planning

Learning-based Fast Path Planning in Complex Environments

Guaranteed Rejection-free Sampling Method Using Past Behaviours for Motion Planning of Autonomous Systems

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