Fuzzy Greedy RRT Path Planning Algorithm in a Complex Configuration Space

2018 ◽  
Vol 16 (6) ◽  
pp. 3026-3035 ◽  
Author(s):  
Ehsan Taheri ◽  
Mohammad Hossein Ferdowsi ◽  
Mohammad Danesh
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Complex Configuration ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path Planning for Origami Carton Folding With a Multi-Fingered Robotic System

2007 ◽  
Author(s):  
Wei Yao ◽  
Jian S. Dai
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Robotic System ◽  
Test Rig ◽  
Packaging System ◽  
Mechanism Model ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Equivalent Mechanism ◽  
Robotic Fingers

This paper investigates the algorithm of origami carton folding with a multi-fingered robotic carton-packaging system. The equivalent mechanism structure of origami cartons is developed by modeling carton boards as links and creases as revolution joints. The trajectories of carton folding are analyzed by the mechanism model. Particularly the vertex of the carton is identified as a spherical linkage. A path planning algorithm is then generated based on the trajectory that is passed on to the tip of a five-bar robotic finger and the finger configuration space is identified. A test rig with two robotic fingers was developed to demonstrate the principle.

An Efficient Path-Planning Algorithm for a Robotic Manipulator by Automatic Selection Search of Indispensable Regions in Its Configuration Space

1992 ◽  
Vol 4 (5) ◽  
pp. 378-385
Author(s):  
Hiroshi Noborio ◽  
◽  
Motohiko Watanabe ◽  
Takeshi Fujii
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Configuration Space ◽  
Robotic Manipulator ◽  
Joint Space ◽  
Practical Applications ◽  
Continuous Sequence ◽  
Angle Difference ◽  
Planning Algorithm ◽  
Path Planning Algorithm

In this paper, we propose a feasible motion planning algorithm for a robotic manipulator and its obstacles. The algorithm quickly selects a feasible sequence of collision-free motions while adaptively expanding a graph in the implicit configuration joint-space. In the configuration graph, each arc represents an angle difference of the manipulator joint; therefore, an arc sequence represents a continuous sequence of robot motions. Thus, the algorithm can execute a continuous sequence of collision-free motions. Furthermore, the algorithm expands the configuration graph only in space which is to be cluttered in the implicit configuration joint-space and which is needed to select a collision-free sequence between the initial and target positions/orientations. The algorithm maintains the configuration graph in a small size and quickly selects a collision-free sequence from the configuration graph, whose shape is to be simple enough to move the manipulator in practical applications.

Robot Obstacle Avoidance and Path Planning in Configuration Space

1993 ◽  
Author(s):  
R. W. Toogood ◽  
Chi Wong
Keyword(s):  
Path Planning ◽  
Work Environment ◽  
Free Path ◽  
Obstacle Avoidance ◽  
Configuration Space ◽  
Complex Shape ◽  
Joint Space ◽  
Simple Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Abstract This paper deals with the problem of planning a collision-free path for a 3 link, revolute robot among fixed obstacles within its work environment. Both the payload and the robot links are checked for collisions with the obstacles. All path planning is performed in joint or configuration space. The first part of the paper is concerned with the visualization of the complex shape of the obstacles as they appear in joint space. The second part of the paper describes and presents results of a simple path planning algorithm.

scholarly journals Path Planning for Multi-Arm Manipulators Using Deep Reinforcement Learning: Soft Actor–Critic with Hindsight Experience Replay

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5911
Author(s):  
Evan Prianto ◽  
MyeongSeop Kim ◽  
Jae-Han Park ◽  
Ji-Hun Bae ◽  
Jung-Su Kim
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Configuration Space ◽  
Entropy Term ◽  
High Dimensional Problem ◽  
Experience Replay ◽  
Planning Algorithm ◽  
Simulation And Experiment ◽  
Efficient Exploration ◽  
Path Planning Algorithm

Since path planning for multi-arm manipulators is a complicated high-dimensional problem, effective and fast path generation is not easy for the arbitrarily given start and goal locations of the end effector. Especially, when it comes to deep reinforcement learning-based path planning, high-dimensionality makes it difficult for existing reinforcement learning-based methods to have efficient exploration which is crucial for successful training. The recently proposed soft actor–critic (SAC) is well known to have good exploration ability due to the use of the entropy term in the objective function. Motivated by this, in this paper, a SAC-based path planning algorithm is proposed. The hindsight experience replay (HER) is also employed for sample efficiency and configuration space augmentation is used in order to deal with complicated configuration space of the multi-arms. To show the effectiveness of the proposed algorithm, both simulation and experiment results are given. By comparing with existing results, it is demonstrated that the proposed method outperforms the existing results.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

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