scholarly journals Path Planning of Continuum Robot Based on Path Fitting

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guochen Niu ◽  
Yunxiao Zhang ◽  
Wenshuai Li
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Structural Characteristics ◽  
Kinematic Model ◽  
Fuel Tank ◽  
Motion Path ◽  
Joint Motion ◽  
Aircraft Fuel ◽  
Continuum Robot ◽  
The Continuum

The Continuum Robot has a multiredundant dof structure, which is extremely advantageous in the unstructured environment, and can complete such tasks as aircraft fuel tank inspection. However, due to its complex kinematics and coupling of joint motion, its motion path planning is also a challenging task. In this paper, a path planning method for Continuum Robot based on an equal curvature model in an aircraft fuel tank environment is proposed. Considering the complexity of calculation and the structural characteristics of Continuum Robot, a feasible obstacle avoidance discrete path is obtained by using the improved RRT algorithm. Then, joint fitting is performed on the existing discrete path according to the kinematic model of Continuum Robot, joint obstacle avoidance was conducted in the process of fitting, and finally, a motion path suitable for the Continuum Robot was selected. A reasonable experiment is designed based on MATLAB, and simulation and analysis results demonstrate excellent performance of this method and feasibility of path planning.

scholarly journals Path planning based on Q-learning and three-segment method for aircraft fuel tank inspection robot

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1797-1807 ◽  
Author(s):  
Niu Guochen ◽  
Xu Kailu
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Initial Point ◽  
Fuel Tank ◽  
Simulation Experiments ◽  
Q Learning ◽  
Inspection Robot ◽  
Computing Complexity ◽  
Aircraft Fuel ◽  
Continuum Robot

In order to realize the path planning of continuum robot for inspecting defects in the aircraft fuel tank compartment, an approach based on Q-learning and Three-segment Method was proposed, and the posture of the robot meeting the inherent and spatial structure constraint requirements was planned. Firstly, the simulation model of the aircraft fuel tank was established. Moreover, the workspace was rasterized to decrease the computing complexity. Secondly, the Q-learning algorithm was applied and the path from the initial point to the target was generated. In terms of target guided angle and three-segment method, the joint variables corresponding to each transition point on the path could be obtained. Finally, the robot reached the target by progressively updating the joint variables. Simulation experiments were implemented, and the results verified the effectiveness and feasibility of the algorithm.

Follow-the-leader motion strategy for multi-section continuum robots based on differential evolution algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
GuoHua Gao ◽  
Pengyu Wang ◽  
Hao Wang
Keyword(s):  
Differential Evolution ◽  
Kinematic Model ◽  
Differential Evolution Algorithm ◽  
Open Loop ◽  
Continuum Robots ◽  
Content Type ◽  
Loop Control ◽  
Continuum Robot ◽  
Motion Strategy ◽  
The Continuum

Purpose The purpose of this paper is to present a follow-the-leader motion strategy for multi-section continuum robots, which aims to make the robot have the motion ability in a confined environment and avoid a collision. Design/methodology/approach First, the mechanical design of a multi-section continuum robot is introduced and the forward kinematic model is built. After that, the follow-the-leader motion strategy is proposed and the differential evolution (DE) algorithm for calculating optimal posture parameters is presented. Then simulations and experiments are carried out on a series of predefined paths to analyze the performance of the follow-the-leader motion. Findings The follow-the-leader motion can be well performed on the continuum robots this study proposes in this research. The experimental results show that the deviation from the path is less than 9.7% and the tip error is no more than 15.6%. Research limitations/implications Currently, the follow-the-leader motion is affected by the following factors such as gravity and continuum robot design. Furthermore, the position error is not compensated under open-loop control. In future work, this paper will improve the accuracy of the robot and introduce a closed-loop control strategy to improve the motion accuracy. Originality/value The main contribution of this paper is to present an algorithm to generate follow-the-leader motion of the continuum robot based on DE. This method is suitable for solving new arrangements in the process of following a nonlinear path. Then, it is expected to promote the engineering application of the continuum robot.

scholarly journals Task space-based orientability analysis and optimization of a wire-driven continuum robot

2019 ◽  
Vol 233 (23-24) ◽  
pp. 7658-7668
Author(s):  
Cong Wang ◽  
Shineng Geng ◽  
David T Branson ◽  
Chenghao Yang ◽  
Jian S Dai ◽  
...  
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Kinematic Model ◽  
Task Space ◽  
Continuum Robots ◽  
Swarm Optimization ◽  
The Monte Carlo Method ◽  
Continuum Robot ◽  
Two Indices ◽  
Joint Length ◽  
The Continuum

Compared to traditional rigid robots, continuum robots have intrinsic compliance and therefore behave dexterously when performing tasks in restricted environments. Although there have been many researches on the design and application of continuum robots, a theoretical investigation of their dexterity is still lacking. In this paper, a two-joint wire-driven continuum robot is utilized to demonstrate dexterity by introducing the concept of orientability taking into account two indices, the accessible ratio and angle of the robot, when its tip reaches a certain task space inside the workspace. Based on the kinematic model, the accessible ratio and angle of the continuum robot are calculated using the Monte-Carlo method. From this, the influence of individual joint lengths on the proposed orientability indices and the optimal joint length are then investigated via an improved particle swarm optimization algorithm. Finally, the presented methods were validated through experiments showing that the use of optimal joint length can increase the accessible ratio and reduce the minimum accessible angle by more than 10° in the task space.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal 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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