Path Optimization of Spray Painting Robot for Zigzag Path Pattern

2013 ◽  
Vol 712-715 ◽  
pp. 2260-2263 ◽  
Author(s):  
Yong Zeng ◽  
Xiao Hua Ni
Keyword(s):  
Free Surface ◽  
Coating Thickness ◽  
Path Optimization ◽  
Satisfactory Performance ◽  
Coating Uniformity ◽  
Painting Robot ◽  
Planning Algorithm ◽  
Thickness Error ◽  
Path Planning Algorithm ◽  
Parallel Condition

in order to solve the coating uniformity of common border between two patches based on zigzag path pattern, an algorithm of zigzag path is developed. In this paper, a complex free surface is divided into several patches based on the constraint of coating thickness error. Based on optimized parameters of spray path on a patch, an algorithm that using variable path parameter is used to optimize zigzag path pattern by modifying the planning direction of paint gun path when satisfy parallel condition. The results of simulations have shown that the path planning algorithm achieves satisfactory performance.

The Path Optimization of Spray Painting Robot for Two Path Pattern

2012 ◽  
Vol 605-607 ◽  
pp. 1563-1567
Author(s):  
Kai Teng ◽  
Yong Zeng
Keyword(s):  
Path Planning ◽  
Complex Geometry ◽  
Path Optimization ◽  
Curved Surfaces ◽  
Spray Painting ◽  
Automotive Manufacturing ◽  
Spiral Path ◽  
Satisfactory Performance ◽  
Painting Robot ◽  
Paint Thickness

Automatic trajectory generation for spray painting is highly desirable for today’s automotive manufacturing. Generating paint gun trajectories for complex curved surfaces to satisfy paint thickness requirements is still highly challenging due to the complex geometry of surfaces. In this paper, based on existing of method which complex surfaces are divided into patches to satisfy the constraints, two path optimization algorithms for path pattern have been developed. Two algorithms are developed to optimize the spiral path and zigzag path on a patch by modifying the planning direction of paint gun path. The results of simulations have shown that the two path planning algorithms achieve satisfactory performance.

Target Estimate PDF-Based Optimal Path Planning Algorithm With Application to UAV Systems

2010 ◽  
Author(s):  
Jared G. Wood ◽  
Benjamin Kehoe ◽  
J. Karl Hedrick
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Optimal Path ◽  
Finite Horizon ◽  
Path Optimization ◽  
Optimal Path Planning ◽  
Planning Algorithm ◽  
Local Path ◽  
Path Planning Algorithm ◽  
Insight Into

Companies are starting to explore investing in UAV systems that come with standard autopilot trackers. There is a need for general cooperative local path planning algorithms that function with these types of systems. We have recently finished a project in which algorithms for autonomously searching for, detecting, and tracking ground targets was developed for a fixed-wing UAV with a visual spectrum gimballed camera. A set of scenarios are identified in which finite horizon path optimization results in a non-optimal ineffective path. For each of these scenarios, an appropriate path optimization problem is defined to replace finite horizon optimization. An algorithm is presented that determines which path optimization should be performed given a UAV state and target estimate probability distribution. The algorithm was implemented and thoroughly tested in flight experiments. The experimental work was successful and gave insight into what is required for a path planning algorithm to robustly work with standard waypoint tracking UAV systems. This paper presents the algorithm that was developed, theory supporting the algorithm, and experimental results.

scholarly journals A Global Path Planning Algorithm Based on Bidirectional SVGA

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Taizhi Lv ◽  
Chunxia Zhao ◽  
Jiancheng Bao
Keyword(s):  
Path Planning ◽  
Multicore Processors ◽  
Computing Time ◽  
Optimal Path ◽  
Visibility Graph ◽  
Path Optimization ◽  
Time And Space ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎). This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning.

scholarly journals Human-Machine Cooperation and Path Planning for Complex Road Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guanghong Zhou
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Industrial Robot ◽  
Rapid Development ◽  
Industrial Robots ◽  
Path Optimization ◽  
Planning System ◽  
Target Point ◽  
Planning Algorithm ◽  
Path Planning Algorithm

With the rapid development of the information age, the development of industrial robots is also advancing by leaps and bounds. In the scenes of automobile, medicine, aerospace, and public service, we have fully enjoyed the convenience brought by industrial robots. However, with the continuous development of industrial robot-related concepts and technologies, human-computer interaction and cooperation have become the development trend of industrial robot. In this paper, the human-machine cooperation and path optimization of industrial robot in a complex road environment are studied and analyzed. At the theoretical modeling level, firstly, the industrial robot is modeled and obstacle avoidance is analyzed based on the kinematics of industrial robot; thus, an efficient and concise collision detection model of industrial robot is proposed. At the algorithm level, in view of the complex road conditions faced by industrial robots, this paper will study and analyze the obstacle avoidance strategy of human-computer cooperation and real-time path optimization algorithm of industrial robots. Based on the virtual target point algorithm, this paper further improves the problem that the goal of the traditional path planning algorithm cannot be fully covered, so as to propose the corresponding improved path planning algorithm of industrial robots. In the experimental part, based on the existing industrial robot system, the human-machine cooperation and path planning system proposed in this paper are designed. The experimental results show that the algorithm proposed in this paper improves the accuracy of obstacle avoidance by about 10 points and the corresponding convergence speed by about 5% compared with the traditional algorithm and the experimental effect is remarkable.

scholarly journals A new method on motion planning for mobile robots using jump point search and Bezier curves

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110192
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Motion Planning ◽  
Initial Point ◽  
Target Point ◽  
Jump Point ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Front End ◽  
Planning Algorithm ◽  
Point Search ◽  
Path Planning Algorithm

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

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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