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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9781538691519

This paper introduces a global path planning method for autonomous systems. Global path planning finds a feasible and collision-free path in an environment in which various kinds of regions and objects exist. However, the most planning methods use information such as collision-free space and obstacles in the environment. Interactions at each region (e.g., sidewalk and pavement) would be different. In this paper, we propose a method for global path planning taking semantic scene context into account. In contrast to conventional path planning methods which use collision-free and obstacle regions, the proposed method represents an environment as a cost map. The cost map is estimated from demonstrated human behaviors and feature maps derived from semantic scene context. To find a path on the cost map, we define a path cost and leverage an optimal rapidly-exploring random tree (RRT*) algorithm. We evaluate the proposed method regarding accuracy and computational efficiency with two public datasets and our contributed dataset. Experimental results show that our method successfully reproduces paths like human behaviors in short computational time.

关键词： path planning rapidly-exploring random tree semantic scene context

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Adaptive Caging Configuration Design Algorithm of Hyper-Redundant Manipulator for Dysfunctional Satellite Pre-Capture

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IEEE ACCESS 2020年 8卷 22546-22559页

作者： Wan, Wenya Sun, Chong Yuan, Jianping Northwestern Polytech Univ Natl Key Lab Aerosp Flight Dynam Xian 710072 Peoples R China

This paper presents an adaptive caging configuration design algorithm of the hyper-redundant manipulator for dysfunctional satellite pre-capture. Taking advantages of the extreme flexibility and hyper-redundancy, the hyper-redundant manipulator wraps its whole body around the dysfunctional satellite to restrain its motion without requiring grappling points and accurate information. However, the hyper-redundancy also makes the caging configuration design more complicated and challenging. In this paper, the dynamic sequential caging following algorithm based on rapidly-exploring random tree algorithm is proposed to search the caging configuration in real-time. First, according to the discretized caging trace, which is twining around the grasped object and selected based on the caging conditions, the joints of the hyper-redundant manipulator are divided into several groups in advance. Then, the joint angles are searched group by group to realize the match of the discretized caging trace by the hyper-redundant manipulator. As a result, the configuration between the grasped object and the hyper-redundant manipulator satisfies the caging conditions. The main advantages of the proposed caging motion planning algorithm lie in the avoidance of the inversion and the efficiency of computation. Finally, the pre-capture of two dysfunctional satellites with different shapes using a twenty universal joint manipulator is implemented, and the simulation results verify the efficiency of the proposed method.

关键词： Caging configuration design hyper-redundant manipulator caging trace dynamic sequential caging following algorithm rapidly-exploring random tree

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Control Framework for Trajectory Planning of Soft Manipulator Using Optimized RRT Algorithm

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IEEE ACCESS 2020年 8卷 171730-171743页

作者： Khan, Ameer Tamoor Li, Shuai Kadry, Seifedine Nam, Yunyoung Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Beirut Arab Univ Fac Sci Dept Math & Comp Sci Beirut 11072809 Lebanon Soonchunhyang Univ Dept Comp Sci & Engn Asan 31538 South Korea

This paper proposes a model-free control framework for the path planning of the rigid and soft robotic manipulator using an intelligent algorithm called Weighted Jacobian rapidly-exploring random tree (WJRRT). The optimization approach is used to model the path planning problem, which is independent of the robotic model, and then used the WJRRT algorithm to solve it. WJRRT algorithm not only explores the cartesian space for the end-effector of the robotic manipulator randomly but also directs it towards the goal-position when required. It is robust enough to tackle the uncertainties in the manipulator and make the computation of path planning more efficient. WJRRT assigned a fitness value to each node of the tree. Based on the fitness values algorithm computes the final path, which is a trade-off between efficiency and safety of the path. The simulation results of two, three, and seven degrees of freedom (DOF) robotic manipulators are presented and compared with JT-RRT, Bi-RRT, and TB-RRT algorithms. Experimental results are verified using a soft manipulator made from flexible materials, i.e., polypropylene and polychloroprene. Their flexible structure makes their control complex and creates uncertainties in the model. The simulation and experimental results demonstrate that WJRRT can efficiently and accurately control the motion of manipulators.

关键词： Manipulators Soft robotics Path planning Space exploration Jacobian matrices Robustness rapidly-exploring random tree robotic manipulator redundant manipulator robust path planning soft robotics

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

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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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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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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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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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Online planning for relative optimal and safe paths for USVs using a dual sampling domain reduction-based RRT* method

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第12期11卷 2665-2687页

作者： Wen, Naifeng Zhang, Rubo Wu, Junwei Liu, Guanqun Dalian Minzu Univ Sch Electromech Engn Dalian Peoples R China

A heuristic Dual sampling domain Reduction-based Optimal rapidly-exploring random tree scheme is proposed by guiding the planning procedure of the optimal rapidly-exploring random tree (RRT*) method through learning environmental knowledge. The scheme aims to plan low fuel expenditure, easy-execution, and low collision probability paths online for an unmanned surface vehicle (USV) under constraints. First, an elliptic sampling domain, which is subject to an elliptic equation and the shortest obstacle avoidance path estimation, is created to plan short paths. Second, by the consideration of the USV motion states, obstacles and external interferences of the current, the near sampling domains of tree nodes are reduced to exclude high-cost sampling domains. Path feasibility is ensured by explicitly handling motion constraints. Third, a safe distance-based collision detection (CD) scheme and a velocity-based bounding box of USV are proposed to decrease the path collision probability. Additionally, a layered USV online path planning framework is built in accordance with the model predictive control method, and the path smoothing scheme is applied via the Dubins curve under the curvature constraint. Results demonstrate that the proposed dual sampling domain reduction method outperforms traditional reduction schemes in terms of improving the execution efficiency of RRT*. Meanwhile, the proposed CD method is more reliable than the conventional one.

关键词： Unmanned surface vehicle Online path planning Obstacle avoidance Sampling space reduction rapidly-exploring random tree Collision detection

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A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm

A local trajectory planning and control method for autonomou...

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AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)

作者： Feraco, Stefano Luciani, Sara Bonfitto, Angelo Amati, Nicola Tonoli, Andrea Politecn Torino Dept OfMech & Aerosp Engn Turin Italy

ISBN: (纸本)9788887237498

This paper presents a local trajectory planning and control method based on the rapidly-exploring random tree algorithm for autonomous racing vehicles. The paper aims to provide an algorithm allowing to compute the planned trajectory in an unknown environment, structured with non-crossable obstacles, such as traffic cones. The investigated method exploits a perception pipeline to sense the surrounding environment by means of a LIDAR-based sensor and a high-performance Graphic Processing Unit. The considered vehicle is a four-wheel drive electric racing prototype, which is modeled as a 3 Degree-of-Freedom bicycle model. A Stanley controller for both lateral and longitudinal vehicle dynamics is designed to perform the path tracking task. The performance of the proposed method is evaluated in simulation using real data recorded by on-board perception sensors. The algorithm can successfully compute a feasible trajectory in different driving scenarios.

关键词： Trajectory planning Autonomous driving rapidly-exploring random tree Vehicle control Environment perception Local planning

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Motion Planning and Task Allocation for a Jumping Rover Team

Motion Planning and Task Allocation for a Jumping Rover Team

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Tan, Kai Chuen Jung, Myungjin Shyu, Isaac Wan, Changhuang Dai, Ran Ohio State Univ Mech & Aerosp Engn Dept Columbus OH 43210 USA

ISBN: (纸本)9781728173955

This paper presents a cooperative robotic team composed of unmanned ground vehicles (UGVs) with hybrid operational modes to tackle the multiple traveling salesman problem (mTSP) with obstacles. The hybrid operational modes allow every UGV in the team to not only travel on a ground surface but also jump over obstacles. We name these UGVs jumping rovers. The jumping capability provides a flexible form of locomotion by leaping and landing on top of obstacles instead of navigating around obstacles. To solve the mTSP, an optimal path between any two objective points in an mTSP is determined by the optimized rapidly-exploring random tree method, named RRT*, and is further improved through a refined RRT* algorithm to find a smoother path between targets. We then formulate the mTSP as a mixed-integer linear programming (MILP) problem to search for the most cost-effective combination of paths for multiple UGVs. The effectiveness of the hybrid operational modes and optimized motion with assigned tasks is verified in an indoor, physical experimental environment using the customized jumping rovers.

关键词： Jumping Robots Multiple Traveling Salesman Problem Path Planning rapidly-exploring random tree Mixed-Integer Linear Programming

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