Quantitative Evaluation of Voronoi Graph Search Algorithm in UAV Path Planning

2018 ◽  
Author(s):  
Changwu Zhang ◽  
Hengzhu Liu ◽  
Yuchen Tang
Keyword(s):  
Path Planning ◽  
Quantitative Evaluation ◽  
Search Algorithm ◽  
Graph Search ◽  
Voronoi Graph

scholarly journals Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*

2020 ◽  
Vol 10 (21) ◽  
pp. 7846
Author(s):  
Hyejeong Ryu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Search Algorithm ◽  
Random Tree ◽  
Graph Search ◽  
Complex Environments ◽  
Sampling Process ◽  
Planning Algorithm ◽  
Local Grid ◽  
Path Planning Algorithm

An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.

Two-Point Flight Path Planning Using A Fast Graph-Search Algorithm

2006 ◽  
Vol 3 (9) ◽  
pp. 453-488 ◽  
Author(s):  
Wen-Ying Chang ◽  
Fei-Bin Hsiao ◽  
Donglong Sheu
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Flight Path ◽  
Graph Search

scholarly journals Environment Detection and Path Planning Using the E-puck Robot

2016 ◽  
Vol 5 (3) ◽  
pp. 151
Author(s):  
Muhammad Saleem Sumbal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robotics ◽  
Autonomous Robots ◽  
Search Algorithm ◽  
Optimal Path ◽  
Graph Search ◽  
Optimal Paths ◽  
Planning Project ◽  
Environment Detection

Automatic path planning is one of the most challenging problems confronted by autonomous robots. Generating optimal paths for autonomous robots are some of the heavily studied subjects in mobile robotics applications. This paper documents the implementation of a path planning project using a mobile robot in a structured environment. The environment is detected through a camera and then a roadmap of the environment is built using some algorithms. Finally a graph search algorithm called A* is implemented that searches through the roadmap and finds an optimal path for robot to move from start position to goal position avoiding obstacles

Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation

2011 ◽  
Vol 142 ◽  
pp. 12-15
Author(s):  
Ping Feng
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Traffic Condition ◽  
Planning Time ◽  
Dynamic Path Planning ◽  
Short Period ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm ◽  
Chaos Genetic Algorithm

The paper puts forward the dynamic path planning algorithm based on improving chaos genetic algorithm by using genetic algorithms and chaos search algorithm. In the practice of navigation, the algorithm can compute at the best path to meet the needs of the navigation in such a short period of planning time. Furthermore,this algorithm can replan a optimum path of the rest paths after the traffic condition in the sudden.

Optimal Robotic Assembly Planning Using Dijkstra’s Algorithm

1994 ◽  
Author(s):  
Sam Anand ◽  
Mohamed Sabri
Keyword(s):  
Configuration Space ◽  
Material Handling ◽  
Search Algorithm ◽  
Robotic Manipulators ◽  
Robotic Assembly ◽  
Graph Search ◽  
Assembly Planning ◽  
Robotic Welding ◽  
Fast Collision ◽  
Space Approach

Abstract Robots play an important role in the modern factory and are used in a manufacturing cell for several functions such as assembly, material handling, robotic welding, etc. One of the principal problems faced by robots while performing their tasks is the presence of obstacles such as fixtures, tools, and objects in the robot workspace. Such objects could result in a collision with one of the arms of the robots. Fast collision-free motion planning algorithms are therefore necessary for robotic manipulators to operate in a wide variety of changing environments. The configuration space approach is one of the widely used methods for collision-free robotic path planning. This paper presents a novel graph-based method of searching the configuration space for a collision-free path in a robotic assembly operation. Dijkstra’s graph search algorithm is used for optimizing the joint displacements of the robot while performing the assembly task. The methodology is illustrated using a simple robotic assembly planning task.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

Micro-Assembly Sequence and Path Planning Using Subassemblies

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
Vinoth Venkatesan ◽  
Joseph Seymour ◽  
David J. Cappelleri
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Micro Assembly ◽  
Final Assembly ◽  
Custom Made ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Assembly Constraints

This paper presents a novel assembly sequence planning (ASP) procedure utilizing a subassembly based search algorithm (SABLS) for micro-assembly applications involving geometric and other assembly constraints. The breakout local search (BLS) algorithm is adapted to provide sequencing solutions in assemblies with no coherent solutions by converting the final assembly into subassemblies which can be assembled together. This is implemented using custom-made microparts which fit together only in a predefined fashion. Once the ASP is done, the parts are manipulated from a cluttered space to their final positions in the subassemblies using a path-planning algorithm based on rapidly exploring random tree (RRT*), a random-sampling based execution, and micromanipulation motion primitives. The entire system is demonstrated by assembling LEGO® inspired microparts into various configurations which involve subassemblies, showing the versatility of the system.

Sign in / Sign up

Export Citation Format

Share Document