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Searching Multiple Approximate Solutions in Configuration Space to Guide Sampling-Based Motion Planning

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2020年第3-4期100卷 1527-1543页

作者： Vonasek, Vojtech Penicka, Robert Kozlikova, Barbora Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic Masaryk Univ Fac Informat Bot 68a Brno 60200 Czech Republic

High-dimensional configuration space is usually searched using sampling-based motion planning methods. The well-known issue of sampling-based planners is the narrow passage problem caused by small regions of the configuration space that are difficult to cover by random samples. Practically, the presence of narrow passages decreases the probability of finding a solution, and to cope with it, the number of random samples has to be significantly increased, which also increases the planning time. By dilating the free space, e.g., by scaling-down or thinning the robot (or obstacles), narrow passages become wider, which allows us to compute an approximate solution. Then, the configuration space can be sampled densely around the approximate solution to find the solution of the original problem. However, this process may fail if the final solution is too far from the approximate one. In this paper, we propose a method to find multiple approximate solutions in the configuration space to increase the chance of finding the final solution. The approximate solutions are computed by repeated search of the configuration space while avoiding, if possible, the already discovered solutions. This enables us to search for distinct solutions leading through different parts of the configuration space. The number of approximate solutions is automatically determined based on their similarity. All approximate solutions are then used to guide the sampling of the configuration space. The performance of the proposed approach is verified in scenarios with multiple narrow passages and the benefits of the method are demonstrated by comparing the results with the state-of-the-art planners.

关键词： Motion planning rapidly-exploring random tree

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A simulation based method for vehicle motion prediction

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COMPUTER VISION AND IMAGE UNDERSTANDING 2015年 136卷 79-91页

作者： Park, Jae-Hyuck Tai, Yu-Wing Korea Adv Inst Sci & Technol Taejon South Korea

The movement of a vehicle is much affected by surrounding environments such as road shapes and other traffic participants. This paper proposes a new vehicle motion prediction method to predict future motion of an on-road vehicle which is observed by a stereo camera system mounted on a moving vehicle. Our proposed algorithm considers not only the history movement of the observed vehicle, but also the environment configuration around the vehicle. To find feasible paths under a dynamic road environment, the rapidly-exploring random tree (RRT) is used. A simulation based method is then applied to generate future trajectories by combining results from RRT and a motion prediction algorithm modelled as a Gaussian Mixture Model (GMM). Our experiments show that our approach can predict future motion of a vehicle accurately, and outperforms previous works where only motion history is considered for motion prediction. (C) 2015 Elsevier Inc. All rights reserved.

关键词： On-road vehicle motion prediction rapidly-exploring random tree Gaussian mixture model Simulation

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Obstacle Avoidance and Path Planning for Multi-Joint Manipulator in a Space Robot

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IEEE ACCESS 2020年 8卷 3511-3526页

作者： Xie, Yaen Zhang, Zhidan Wu, Xiande Shi, Zhen Chen, Yangyang Wu, Baixuan Mantey, Kofi Akrofi Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China Harbin Engn Univ Coll Aerosp & Civil Engn Harbin 150001 Peoples R China Beijing Inst Elect Syst Engn Beijing 100854 Peoples R China

An obstacle avoidance and path planning algorithm for a multi-joint manipulator in a space robot is presented in this paper. In this paper, the end-effector of the manipulator is used to capture some special target in a space environment with obstacles. To ensure the safety of the operation, a collision-free path from the initial position to the target position is essential. Therefore, an obstacle avoidance and path planning algorithm based on the rapidly-exploring random tree (RRT) algorithm and the Forward and Backward Reaching Inverse Kinematics (FABRIK) algorithm is presented in this paper. First, a path planning algorithm based on the rapidly-exploring random tree (RRT) algorithm is designed for the multi-joint manipulator. Further, a method to generate a random point by artificial guidance is introduced for a higher searching speed. The RRT algorithm can effectively explore the entire workspace and find a feasible path without collision for the end-effector. To calculate the positions of each joint, the Forward and Backward Reaching Inverse Kinematics (FABRIK) algorithm is introduced and improved for the problem of inverse kinematics. The FABRIK algorithm avoids the use of rotational angles or matrices, and instead finds each joint position by locating a point on a line, and thus, it has a low computational cost. Therefore, the improved obstacle avoidance and path planning algorithm can quickly plan a feasible path for the multi-joint manipulator in a space environment with obstacles. A numerical simulation is carried out to analyze the proposed obstacle avoidance and path planning method. It is observed that the method finds a feasible path without collision for the multi-joint manipulator with a low computational cost. These results validated the effectiveness of the proposed method for path planning to avoid the obstacles.

关键词： FABRIK multi-joint manipulator obstacle avoidance path planning rapidly-exploring random tree

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DBVS-APF-RRT*: A global path planning algorithm with ultra-high speed generation of initial paths and high optimal path quality

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期249卷

作者： Feng, Zihao Zhou, Lang Qi, Jie Hong, Shaohua Xiamen Univ Sch Elect Sci & Engn Xiamen 361005 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

During the last decade, RRT* algorithm has been widely concerned by researchers because of its asymptotic optimality. However, the slow convergence rate of the RRT* algorithm leads to poor quality of the optimal paths at small number of iterations. Meanwhile, searching the initial path slowly limits its application scenarios. To overcome these problems, a directionally biased variable step APF-RRT* (DBVS-APF-RRT*) algorithm proposed in this paper. Firstly, a novel directionally biased variable step sampling strategy is used in RRT* algorithm to quickly generate the initial path. Then, global random sampling and key region sampling strategies are added to improve global search ability and optimal path quality. At the same time, an artificial potential field (APF) method is introduced to improve the ability of obstacle avoidance. In addition, we propose pruning strategy based on triangular inequality with direct connection of goal points to reduce the number of redundant nodes and shorten the path length. Finally, DBVS-APF-RRT* algorithm is compared with RRT*, Bias-RRT*, Informed-RRT* and Bias-P-RRT* algorithms to verify its superiority of optimal path quality, stability of the algorithm and its rapidity of finding the initial path.

关键词： rapidly-exploring random tree Directionally biased sampling Variable step size sampling Initial path Optimal path planning

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A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV

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ADVANCED ROBOTICS 2013年第6期27卷 431-443页

作者： Yang, Kwangjin Gan, Seng Keat Sukkarieh, Salah Korea AF Acad Dept Aerosp & Mech Engn Cheongwon Chungbuk South Korea Korea AF Acad Cheongwon Chungbuk South Korea

A new framework which adopts a rapidly-exploring random tree (RRT) path planner with a Gaussian process (GP) occupancy map is developed for the navigation and exploration of an unknown but cluttered environment. The GP map outputs the probability of occupancy given any selected query point in the continuous space and thus makes it possible to explore the full space when used in conjunction with a continuous path planner. Furthermore, the GP map-generated path is embedded with the probability of collision along the path which lends itself to obstacle avoidance. Finally, the GP map-building algorithm is extended to include an exploration mission considering the differential constraints of a rotary unmanned aerial vehicle and the limitation arising from the environment. Using mutual information as an information-theoretic measure, an informative path which reduces the uncertainty of the environment is generated. Simulation results show that GP map combined with RRT planner can achieve the 3D navigation and exploration task successfully in unknown and complex environments.

关键词： Gaussian processes information-theoretic exploration map building rapidly-exploring random tree unmanned aerial vehicle

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Spline-Based RRT Path Planner for Non-Holonomic Robots

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2014年第1-4期73卷 763-782页

作者： Yang, Kwangjin Moon, Sangwoo Yoo, Seunghoon Kang, Jaehyeon Doh, Nakju Lett Kim, Hong Bong Joo, Sanghyun Korea Air Force Acad Dept Aerosp & Mech Engn Cheongwon South Korea Korea Air Force Acad Dept Elect Engn & Comp Sci Cheongwon South Korea Korea Univ Sch Elect Engn Seoul South Korea Future Man Elect Taejon South Korea Agcy Def Dev Taejon South Korea

Planning in a cluttered environment under differential constraints is a difficult problem because the planner must satisfy the external constraints that arise from obstacles in the environment and the internal constraints due to the kinematic/dynamic limitations of the robot. This paper proposes a novel Spline-based rapidly-exploring random tree (SRRT) algorithm which treats both the external and internal constraints simultaneously and efficiently. The computationally expensive numerical integration of the system dynamics is replaced by an efficient spline curve parameterization. In addition, the SRRT guarantees continuity of curvature along the path satisfying any upper-bounded curvature constraints. This paper presents the underlying theory to the SRRT algorithm and presents simulation and experiment results of a mobile robot efficiently navigating through cluttered environments.

关键词： Differential constraints rapidly-exploring random tree Spline curve parameterization Mobile robot

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Reinforcement Learning Aided Robot-Assisted Navigation: A Utility and RRT Two-Stage Approach

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INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS 2020年第3期12卷 689-707页

作者： Garrote, Luis Paulo, Joao Nunes, Urbano J. Univ Coimbra Dept Elect & Comp Engn Inst Syst & Robot Coimbra Portugal

This work proposes a robot-assisted navigation approach based on user intent adjustment, in the context of robotic walkers. Walkers are prescribed to users with gait disorders so that they can support their body weight on the upper limbs, however, the manipulation of such devices can be cumbersome for some users. Common problems for the users are lack of dexterous upper limb control and visual impairments. These problems can render walkers' users helpless, making them unable to operate these devices effectively and efficiently. We present a new approach to robot-assisted navigation using a utility decision and safety analysis procedure with user intent adjustments learned by reinforcement learning (RL) and supported on a rapidly-exploring random tree inspired algorithm. The proposed approach offers full control of the assistive platform to the user until obstacles are detected. In dangerous scenarios, corrections are computed in order that the assistive platform avoids collisions and follows social norms, effectively guiding the user through the environment while enforcing safer routes. The experimental validation was carried out in a virtual environment and in a real world scenario using a robotic walker built in our lab (ISR-AIWALKER). Experimental results have shown that the proposed approach provides a reliable solution to the robot-assisted navigation of a robotic walker, in particular the use of utility theory to evaluate candidate motions together with a RL model increases the safety of the user's navigation.

关键词： Assistive robotics Reinforcement learning Robot-assisted navigation Robotic walker rapidly-exploring random tree Robot operating system

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Targettree-RRT*: Continuous-Curvature Path Planning Algorithm for Autonomous Parking in Complex Environments

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第1期21卷 606-617页

作者： Kim, Minsoo Ahn, Joonwoo Park, Jaeheung Seoul Natl Univ DYROS Lab GraduateSchool Convergence Sci & Technol Seoul 08826 South Korea Adv Inst Convergence Technol AICT Suwon 16229 South Korea

rapidly-exploring random tree (RRT) has been studied for autonomous parking as it quickly finds an initial path and is easily scalable in complex environments. However, the planning time increases by searching for the path in narrow parking spots. To reduce the planning time, the target tree algorithm, which substitutes a parking goal in RRT with a set (target tree) of backward parking paths, was proposed. However, as it consists of circular and straight paths, it deteriorates parking accuracy because of curvature-discontinuity. Moreover, the planning time increases in complex environments;backward paths can be blocked by obstacles. Therefore, this paper introduces the Targettree-RRT* algorithm for complex environments. First, a target tree is designed using clothoid paths to address such curvature-discontinuity. Second, to reduce the planning time further, a cost function is defined to initialize a proper target tree that considers obstacles. By integrating with optimal-variant RRT and searching for the shortest path, the proposed Targettree-RRT* algorithm obtains a near-optimal path as the sampling time increases. Experiment results in real environments showed that the vehicle parked more accurately, and continuous-curvature paths were obtained more quickly and with higher success rates than those acquired using other sampling-based and other types of planning algorithms.

关键词： Path planning autonomous parking target tree algorithm rapidly-exploring random tree

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Path planning for indoor Mobile robot based on deep learning

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OPTIK 2020年 219卷 165096-165096页

作者： Zhang, Lin Zhang, Yingjie Li, Yangfan Xi An Jiao Tong Univ Sch Mech Engn Xian Peoples R China

This paper aims to give an optimal path planning of a mobile robot in a known indoor environment. An algorithm based on deep learning, ray tracing algorithm, waiting rule, and rapidly-exploring random tree is proposed to solve the problem of obstacle avoidance and path planning. Firstly, GoogLeNet is used to classify obstacles. Here, it helps to distinguish between static and dynamic obstacles. Secondly, for the static obstacle avoidance, the ray tracing algorithm is proposed to avoid the obstacles which are identified by GoogLeNet. And for the dynamic obstacle avoidance, this paper proposed a waiting rule for dynamic obstacle avoidance. Thirdly, the RRT method plans a path from the start point to the goal point. The novelty of this paper is that the type of obstacles is distinguished by deep learning, and the two different kinds of obstacles used ray tracing algorithm and waiting rule to avoid obstacles collision, respectively. In experimental results, rapidly-exploring random tree is compared with genetic algorithm, and particle swarm optimization method in static environment, and it is compared with artificial potential field approach in dynamic environment. Experiments are carried out in the two-dimensional environment and successfully applied to the path planning of mobile robots in multi-obstacles environment, and they prove the feasibility of the algorithm.

关键词： Path planning Deep learning rapidly-exploring random tree Ray tracing algorithm Waiting rule Obstacle avoidance

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An Adaptive Stepsize RRT Planning Algorithm for Open-Chain Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2018年第1期3卷 312-319页

作者： An, Byungchul Kim, Jinkyu Park, Frank C. Seoul Natl Univ Robot Lab Seoul 08826 South Korea Korea Inst Technol Healthcare Robot Res Grp Seoul 02792 South Korea

Motion planning algorithms that rely upon the randomly exploring random tree (RRT) typically require the user to choose an appropriate stepsize;this is generally a highly problem-dependent and time-consuming process requiring trial and error. We propose an adaptive stepsize RRT path planning algorithm for open-chain robots in which only a minimum obstacle size parameter is required as input. Exploiting the structure of an open chain's forward kinematics as well as a standard inequality bound on the operator-induced matrix norm, we derive a maximum Cartesian displacement bound between two configurations of the same robot, and use this bound to determine a maximum allowable stepsize at each iteration. Numerical experiments involving a ten-DOF planar open chain and a seven-axis industrial robot arm demonstrate the practical advantages of our algorithm over standard fixed-stepsize RRT planning algorithms.

关键词： Adaptive stepsize operator norm path planning rapidly-exploring random tree

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