Vision-Based Moving Obstacle Avoidance for Cable-Driven Parallel Robots Using Improved Rapidly Exploring Random Tree

2020 ◽  
Author(s):  
Jiajun Xu ◽  
Kyoung-su Park
Keyword(s):  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Parallel Robots ◽  
Random Tree ◽  
Experimental Setup ◽  
Bounding Box ◽  
Vision Algorithm ◽  
Moving Obstacle

Abstract In this study, the improved vision-based rapidly exploring random tree (RRT) algorithm is proposed to address moving obstacle avoidance for cable-driven parallel robots (CDPRs). The improved RRT algorithm is goal-biased with dynamic stepsize makes it possible to implement in dynamic environments. For the implementation of algorithm on CDPRs, the improved RRT considers various collisions caused by the cable. The axis-aligned bounding box (AABB) algorithm is used for the fast re-planning during the RRT process. Additionally, the improved RRT algorithm premeditates the complex constrains include force feasible workspace (FFW) and the segment-to-segment angle. The related simulation is given in order to illustrate the algorithm. An experimental setup is also presented using the drone as a moving obstacle and the Faster-RCNN vision algorithm to obtain the coordinate of the drone. The experiment result shows that the proposed algorithm can apply in CDPRs with the dynamic environment validly.

scholarly journals Cash Collection Model of Electric Power Business Office Based on Computer Algorithm

2022 ◽  
Vol 2146 (1) ◽  
pp. 012023
Author(s):  
Binghua Guo ◽  
Nan Guo
Keyword(s):  
Mobile Robot ◽  
Medical Treatment ◽  
Electric Power ◽  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Main Trend ◽  
Intelligent Robot ◽  
Treatment Service ◽  
Intelligent Algorithms

Abstract With the continuous development of intelligent algorithms, mobile robot (hereinafter referred to as MR) technology is gradually mature, which has been widely used in a variety of industries, such as industry, agriculture, medical treatment, service and so on. With the improvement of intelligent level, people have higher and higher requirements for MRs, which requires MRs to constantly adapt to different environments, especially dynamic environments. In the dynamic environment, obstacle avoidance technology has become the focus of intelligent robot research, which needs to continuously develop a variety of algorithms. By combining a variety of algorithms, we can realize obstacle avoidance and PP (hereinafter referred to as PP) of MR, which can realize obstacle avoidance more efficiently, in real time and intelligently. Multi algorithm fusion of MR has become the main trend of obstacle avoidance in the future, which will realize PP and optimization. Firstly, this paper analyzes the differences between traditional algorithms and intelligent algorithms. Then, the kinematics model and PP algorithm of MR are analyzed. Finally, the simulation is carried out.

scholarly journals The Ariadne's Clew Algorithm

1998 ◽  
Vol 9 ◽  
pp. 295-316 ◽  
Author(s):  
E. Mazer ◽  
J. M. Ahuactzin ◽  
P. Bessiere
Keyword(s):  
Path Planning ◽  
Degrees Of Freedom ◽  
Optimization Problems ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Experimental Results ◽  
High Dimensional ◽  
New Approach ◽  
Six Degrees Of Freedom ◽  
Moving Obstacle

We present a new approach to path planning, called the ``Ariadne's clew algorithm''. It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments --- ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called SEARCH and EXPLORE, applied in an interleaved manner. EXPLORE builds a representation of the accessible space while SEARCH looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing.

scholarly journals Dynamic Obstacle Avoidance for an Omnidirectional Mobile Robot

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Robert L. Williams ◽  
Jianhua Wu
Keyword(s):  
Obstacle Avoidance ◽  
Experimental Validation ◽  
Vision System ◽  
Dynamic Environment ◽  
Ohio University ◽  
Omnidirectional Mobile Robot ◽  
Dynamic Obstacle Avoidance ◽  
Novel Method ◽  
Dynamic Obstacle ◽  
Moving Obstacle

We have established a novel method of obstacle-avoidance motion planning for mobile robots in dynamic environments, wherein the obstacles are moving with general velocities and accelerations, and their motion profiles are not preknown. A hybrid system is presented in which a global deliberate approach is applied to determine the motion in the desired path line (DPL), and a local reactive approach is used for moving obstacle avoidance. A machine vision system is required to sense obstacle motion. Through theoretical analysis, simulation, and experimental validation applied to the Ohio University RoboCup robot, we show the method is effective to avoid collisions with moving obstacles in a dynamic environment.

scholarly journals Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Robotica ◽  
2014 ◽  
Vol 33 (4) ◽  
pp. 807-827 ◽  
Author(s):  
Sivaranjini Srikanthakumar ◽  
Wen-Hua Chen
Keyword(s):  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Case Analysis ◽  
Unmanned Vehicles ◽  
Optimization Techniques ◽  
Worst Case Analysis ◽  
Physical Parameters ◽  
Worst Case ◽  
Automatic Search ◽  
Moving Obstacle

SUMMARYThis paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, and illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncertainties in physical parameters, sensor measurements, and even the model structure of the robot are taken into account in the worst-case analysis. The minimum distance to a moving obstacle is considered as an objective function in automatic search process. It is demonstrated that a local nonlinear optimization method may not be adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. The Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches.

Vision-Based Motion Planning of Cable-Driven Parallel Robots in the Presence of Moving Obstacle Using Sampling-Based Method

2021 ◽  
Author(s):  
Jiajun Xu ◽  
Kyoung-Su Park
Keyword(s):  
Closed Loop ◽  
Convex Bodies ◽  
Parallel Robots ◽  
Suggested Method ◽  
Forward Simulation ◽  
Closed Loop System ◽  
The Past ◽  
Complex Constraints ◽  
Vision Algorithm ◽  
Moving Obstacle

Abstract In the past decades, cable-driven parallel robots (CDPRs) have been proven the extraordinary performance for various applications. However, the multiple cables lead the robot easy to interfere with environments. Especially the large workspace of CDPR may introduce unknown moving obstacles. In this study, a sampling-based path planning method is presented for a CDPR to find the collision-free path in the presence of the moving obstacle. The suggested method is based on rapidly exploring random tree (RRT) algorithm which gives CDPRs advantages to handle complex constraints such as cable collision and dynamic feasible workspace (DFW). Moreover, we conduct the forward simulation to check the feasibility in a closed-loop system. The moving parts of both CDPRs and the moving obstacle are assumed as convex bodies, so that Gilbert-Johnson-Keerthi (GJK) algorithm is adopted to detect collision in real-time. Finally, the related simulation is carried out to illustrate the algorithm. The experiment is also presented using the drone as a moving obstacle and YOLO vision algorithm to detect the drone. The experiment results demonstrate the reliability of the suggested method.

scholarly journals An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows

Machines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 50
Author(s):  
Liwei Yang ◽  
Lixia Fu ◽  
Ping Li ◽  
Jianlin Mao ◽  
Ning Guo
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Ant Colony ◽  
Complex Dynamic ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Window Approach ◽  
Search Capability

To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO from the pheromone update and heuristic function and then design a strategy to solve the deadlock problem. Considering the actual path requirements of the robot, a new path smoothing method is present. Finally, the robot modeled by DWA obtains navigation information from the global path, and we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving the evaluation function. The simulation and experimental results show that our algorithm improves the robot's navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.

Sign in / Sign up

Export Citation Format

Share Document