scholarly journals RimJump: Edge-based Shortest Path Planning for a 2D Map

Robotica ◽  
2018 ◽  
Vol 37 (4) ◽  
pp. 641-655 ◽  
Author(s):  
Zhuo Yao ◽  
Weimin Zhang ◽  
Yongliang Shi ◽  
Mingzhu Li ◽  
Zhenshuo Liang ◽  
...  
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Planning Method ◽  
Time Cost ◽  
Map Scale ◽  
Edge Based

SummaryPath planning under 2D map is a key issue in robot applications. However, most related algorithms rely on point-by-point traversal. This causes them usually cannot find the strict shortest path, and their time cost increases dramatically as the map scale increases. So we proposed RimJump to solve the above problem, and it is a new path planning method that generates the strict shortest path for a 2D map. RimJump selects points on the edge of barriers to form the strict shortest path. Simulation and experimentation prove that RimJump meets the expected requirements.

scholarly journals RimJump*: Tangent Based Shortest Path Planning for Cluttered 3D Environment

2021 ◽  
Author(s):  
Zhuo Yao
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Autonomous Vehicles ◽  
Shortest Paths ◽  
Research Area ◽  
Time Cost ◽  
Geometrical Properties ◽  
3D Environment ◽  
Starting Point ◽  
Map Scale

Path planning in 3D environment is a fundamental research area for robots and autonomous vehicles. Based on the principle ``the shortest path consists of tangents'', RimJump* is proposed as a tangent-based path planning method suitable for finding the shortest path (both off-ground and on-ground) in 3D space (e.g., octomap and point cloud) for mobile platform to follow. RimJump* searches the tangent graph in the form of a path tree and considers the geometrical properties of the locally shortest path. Therefore, the method can provide all of the locally shortest paths that connect the starting point and the target, including the globally shortest path. And the time cost of RimJump* is insensitive to map scale increases in comparison to methods that search the whole passable space rather than the surface of the obstacle, e.g., Dijkstra and A*. In the Results, RimJump* is compared with other methods in terms of path length and time cost.

Path Planning for Manipulator Robots in Cluttered Environments

2005 ◽  
Author(s):  
Samir Lahouar ◽  
Said Zeghloul ◽  
Lotfi Romdhane
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Planning Method ◽  
Search Process ◽  
Grid Cells ◽  
Heuristic Function ◽  
Search Mode ◽  
Cluttered Environments ◽  
Grid Sampling ◽  
Cluttered Environment

In this paper we propose a new path planning method for robot manipulators in cluttered environments, based on lazy grid sampling. Grid cells are built while searching for the path to the goal configuration. The proposed planner acts in two modes. A depth mode, while the robot is far from obstacles, makes it evolve toward its goal. While a width search mode becomes active when the robot gets close to an obstacle. This method provides the shortest path to go around the obstacle. It also reduces the gap between pre-computed grid methods and lazy grid methods. No heuristic function is needed to guide the search process. An example dealing with a robot in a cluttered environment is presented to show the efficiency of the method.

Formation Vector Based Shortest Path-Planning in CGF Formation

2013 ◽  
Vol 756-759 ◽  
pp. 3351-3355
Author(s):  
Fei Jie ◽  
Zhao Han Lu ◽  
Bao Di Xie
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Approximation Method ◽  
Change Process ◽  
Simulation System ◽  
Planning Method ◽  
Experimental Results ◽  
The Poor ◽  
Improved Algorithm

In order to improve the poor reality and bad flexibility of the mapping relationship which matched entities with aim locations in traditional approximation method, the formation vector shortest path-planning method was presented in this paper. By analyzing the lack of aim path-planning in the approximation method, shortest path-planning was discussed and was improved by introducing the formation vector and the idea of pheromone. Furthermore, the improved algorithm was applied in a CGF simulation system. The experimental results showed that the mapping relationship had better reality and rationality and the possibility of collision was significantly reduced than the traditional formation change process.

scholarly journals Automatic Parking Path Planning Based on Ant Colony Optimization and the Grid Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guo Liang Han
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Shortest Path ◽  
Grid Method ◽  
Path Tracking ◽  
Planning Method ◽  
Ant Colony ◽  
Planning Problem ◽  
Automatic Parking ◽  
Vehicle Path

This paper analyzes the path planning problem in the automatic parking process, and studies a path planning method for automatic parking. The grid method and the ant colony optimization are combined to find the shortest path from the parking start point to the end point. The grid method is used to model the parking environment to simulate the actual parking space of automatic parking; then this paper makes some improvements to the basic ant colony optimization, finds the destination by setting the ants’ movement rules in the grid, and finds the shortest path after N iterations; since the optimal path found is a polyline, it will increase the difficulty of controlling vehicle path tracking and affect the accuracy of vehicle path tracking. The bezier curve is used to generate a smooth path suitable for vehicle walking. Finally, through matlab simulation, the obstacles in the environment are simulated, and the parking trajectory is obtained. The results show that the path planning method proposed in this paper is feasible.

Neural Network Based Control System for Robots Group Operating in 2-d Uncertain Environment

2020 ◽  
Vol 21 (8) ◽  
pp. 470-479
Author(s):  
A. R. Gaiduk ◽  
O. V. Martjanov ◽  
M. Yu. Medvedev ◽  
V. Kh. Pshikhopov ◽  
N. Hamdan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Path Planning ◽  
Shortest Path ◽  
Optimal Path ◽  
Type A ◽  
Planning Method ◽  
Target Point ◽  
Optimal Path Planning

This study is devoted to development of a neural network based control system of robots group. The control system performs estimation of an environment state, searching the optimal path planning method, path planning, and changing the trajectories on via the robots interaction. The deep learning neural networks implements the optimal path planning method, and path planning of the robots. The first neural network classifies the environment into two types. For the first type a method of the shortest path planning is used. For the second type a method of the most safety path planning is used. Estimation of the path planning algorithm is based on the multi-objective criteria. The criterion includes the time of movement to the target point, path length, and minimal distance from the robot to obstacles. A new hybrid learning algorithm of the neural network is proposed. The algorithm includes elements of both a supervised learning as well as an unsupervised learning. The second neural network plans the shortest path. The third neural network plans the most safety path. To train the second and third networks a supervised algorithm is developed. The second and third networks do not plan a whole path of the robot. The outputs of these neural networks are the direction of the robot’s movement in the step k. Thus the recalculation of the whole path of the robot is not performed every step in a dynamical environment. Likewise in this paper algorithm of the robots formation for unmapped obstructed environment is developed. The results of simulation and experiments are presented.

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