Moving obstacle avoidance for cable-driven parallel robots using improved RRT

2020 ◽  
Author(s):  
Jiajun Xu ◽  
Kyoung-Su Park
Keyword(s):  
Obstacle Avoidance ◽  
Parallel Robots ◽  
Moving Obstacle

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 Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance

2014 ◽  
Vol 15 (5) ◽  
pp. 2101-2110 ◽  
Author(s):  
Andrea Cherubini ◽  
Fabien Spindler ◽  
Francois Chaumette
Keyword(s):  
Obstacle Avoidance ◽  
Visual Navigation ◽  
Moving Obstacle

scholarly journals Real-time Trajectory Planning for Automated Vehicle Safety and Performance in Dynamic Environments

2021 ◽  
pp. 1-22
Author(s):  
Huckleberry Febbo ◽  
Paramsothy Jayakumar ◽  
Jeffrey L. Stein ◽  
Tulga Ersal
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
High Speed ◽  
High Performance ◽  
Simultaneous Optimization ◽  
Problem Solver ◽  
Automated Vehicles ◽  
Control Effort ◽  
Moving Obstacle

Abstract Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-to-goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. The ability to solve this formulation in real-time is evaluated using NLOptControl, an open-source, direct-collocation based, optimal control problem solver in conjunction with the KNITRO nonlinear programming problem solver. The formulation is tested with various sets of the specifications. A parametric study relating execution horizon and obstacle speed indicates that the moving obstacle avoidance specification is not needed for safety when the planner has a small execution horizon and the obstacles are moving slowly. However, a moving obstacle avoidance specification is needed when the obstacles are moving faster, and this specification improves the overall safety without, in most cases, increasing the solve-times. The results indicate that (i) safe trajectory planners for high-performance automated vehicles should include the entire set of specifications mentioned above, unless a static or low-speed environment permits a less comprehensive planner; and (ii) the resulting formulation can be solved in real-time.

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