Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation

Bency, Mayur J.; Qureshi, Ahmed H.; Yip, Michael C.

doi:10.48550/arxiv.1904.11102

Cited by 5 publications

(5 citation statements)

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“…To determine end-to-end collision-free trajectories in an iterative manner, algorithms in the works of [12][13][14][15] have been raised. Bency et al [12] introduced a Recurrent Neural Networks (RNNs)-based motion-planning algorithm called OracleNet that can determine end-to-end collision-free trajectories for static environments and generate near-optimal paths iteratively. In Ref.…”

Section: End-to-end Algorithmsmentioning

confidence: 99%

A survey of learning‐based robot motion planning

Wang

Zhang

et al. 2021

IET Cyber-Syst and Robotics

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A fundamental task in robotics is to plan collision-free motions among a set of obstacles. Recently, learning-based motion-planning methods have shown significant advantages in solving different planning problems in high-dimensional spaces and complex environments. This article serves as a survey of various different learning-based methods that have been applied to robot motion-planning problems, including supervised, unsupervised learning, and reinforcement learning. These learning-based methods either rely on a human-crafted reward function for specific tasks or learn from successful planning experiences. The classical definition and learning-related definition of motion-planning problem are provided in this article. Different learning-based motion-planning algorithms are introduced, and the combination of classical motion-planning and learning techniques is discussed in detail.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: End-to-end Algorithmsmentioning

confidence: 99%

A survey of learning‐based robot motion planning

Wang

Zhang

et al. 2021

IET Cyber-Syst and Robotics

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“…A similar work is done in [34]. Bency et al present OracleNet, a recurrent neural network (RNN)-based approach to generate fast near-optimal paths for robotic arms [35]. OracleNet needs training on each new environment which makes the algorithm suitable for static environments.…”

Section: Related Workmentioning

confidence: 99%

Waypoint Planning Networks

Toma

Jaafar

Hsueh

et al. 2021

Preprint

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With the recent advances in machine learning, path planning algorithms are also evolving; however, the learned path planning algorithms often have difficulty competing with success rates of classic algorithms. We propose waypoint planning networks (WPN), a hybrid algorithm based on LSTMs with a local kernel-a classic algorithm such as A*, and a global kernel using a learned algorithm. WPN produces a more computationally efficient and robust solution. We compare WPN against A*, as well as related works including motion planning networks (MPNet) and value iteration networks (VIN). In this paper, the design and experiments have been conducted for 2D environments. Experimental results outline the benefits of WPN, both in efficiency and generalization. It is shown that WPN's search space is considerably less than A*, while being able to generate near optimal results. Additionally, WPN works on partial maps, unlike A* which needs the full map in advance. The code is available online 1 .

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“…The recent version of MPNet accounts for the kinematics constraint as well [17], [4]. Various architectures and machine learning methods are being used in planning including: recurrent neural networks (RNN) in OracleNet [23], 3D supervised imitation learning in (TDPP-Net) [24], unsupervised Generative Adversarial Networks (GANs) in [25] and [26], and reinforcement learning (RL) strategies in value iteration networks (VIN) [2], gated path planning networks (GPPN) [3], universal planning networks (UPN) [27] [28], guided policy search (GPS) [28], and learning-from demonstration (LfD) [29]. With the continual advancement in machine and deep learning techniques and hardware capabilities, increased development of new learning based path planning algorithms can be foreseen.…”

Section: A Classical and Learned Planning Algorithmsmentioning

confidence: 99%

PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms

Toma

Hsueh

Jaafar

et al. 2021

Preprint

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Path planning is a key component in mobile robotics. A wide range of path planning algorithms exist, but few attempts have been made to benchmark the algorithms holistically or unify their interface. Moreover, with the recent advances in deep neural networks, there is an urgent need to facilitate the development and benchmarking of such learning-based planning algorithms. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learned 2D and 3D path planning algorithms, while offering support for Robot Operating System (ROS). Many existing path planning algorithms are supported; e.g. A*, wavefront, rapidly-exploring random tree, value iteration networks, gated path planning networks; and integrating new algorithms is easy and clearly specified. We demonstrate the benchmarking capability of PathBench by comparing implemented classical and learned algorithms for metrics, such as path length, success rate, computational time and path deviation. These evaluations are done on built-in PathBench maps and external path planning environments from video games and real world databases. PathBench is open source 1 .

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Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation

Cited by 5 publications

References 0 publications

A survey of learning‐based robot motion planning

A survey of learning‐based robot motion planning

Waypoint Planning Networks

PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms

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