scholarly journals Optimal randomized path planning for redundant manipulators based on Memory-Goal-Biasing

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878704 ◽  
Author(s):  
Dong Han ◽  
Hong Nie ◽  
Jinbao Chen ◽  
Meng Chen
Keyword(s):  
Path Planning ◽  
Search Tree ◽  
Random Tree ◽  
Complex Environments ◽  
Redundant Manipulator ◽  
Optimal Method ◽  
Key Points ◽  
Industrial Manipulators ◽  
Novel Method ◽  
Better Than

Planning path rapidly and optimally is one of the key technologies for industrial manipulators. A novel method based on Memory-Goal-Biasing–Rapidly-exploring Random Tree is proposed to solve high-dimensional manipulation planning more rapidly and optimally. The tree extension of Memory-Goal-Biasing–Rapidly-exploring Random Tree can be divided into random extension and goal extension. In the goal extension, the nodes extended to the goal are recorded in a memory, and then the node closest to the goal is selected in the search tree excepting the nodes in the memory for overcoming the local minimum. In order to check collisions efficiently, the manipulator is simplified into several key points, and the obstacle area is appropriately enlarged for safety. Taking the redundant manipulator of Baxter robot as an example, the proposed algorithm is verified through MoveIt! software. The results show that Memory-Goal-Biasing–Rapidly-exploring Random Tree only takes a few seconds for the path planning of the redundant manipulator in some complex environments, and within an acceptable time, its optimization performance is better than that of traditional optimal method in terms of the obtained path costs and the corresponding standard deviation.

scholarly journals Extended rapidly exploring random tree–based dynamic path planning and replanning for mobile robots

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877387 ◽  
Author(s):  
Devin Connell ◽  
Hung Manh La
Keyword(s):  
Path Planning ◽  
Search Tree ◽  
Random Tree ◽  
Complex Environments ◽  
Operational Environment ◽  
Moving Obstacles ◽  
Dynamic Path Planning ◽  
Current Location ◽  
Current Plan ◽  
Multi Robot

It is necessary for a mobile robot or even a multi-robot team to be able to efficiently plan a path from its starting or current location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing its path. In this article, a new method of dynamic replanning is proposed to allow the robot to efficiently plan a path in such complex environments. Our proposed replanning method is based on an extended rapidly exploring random tree. The robot will modify its current plan when unknown random moving obstacles obstruct the path. We extend the proposed replanning method to multi-robot scenarios in which the ability to share path planning and search tree information is valuable. An efficient method of node sharing is proposed to allow the multi-robot team to quickly develop path plans. Various experimental results in both single and multi-robot scenarios show the effectiveness of the proposed methods.

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.

An Adaptive Rapidly-Exploring Random Tree Algorithm for Assembly Path Planning in Complex Environments

2011 ◽  
Author(s):  
Wei Shang ◽  
Jian-hua Liu
Keyword(s):  
Path Planning ◽  
Performance Improvement ◽  
Random Tree ◽  
Complex Environments ◽  
Tree Algorithm ◽  
The Future ◽  
Constrained Environments ◽  
Significant Performance

We present a refined Rapidly-exploring Random Tree (RRT) algorithm for assembly path planning in complex environments. This algorithm adapts its expansion automatically to explore complex environments with narrow passages and cluttered obstacles more efficiently. In this algorithm, the nodes in the tree are classified by various criterions and different extending values are assigned on them indicating the nearby environment and are used to control the future expansion. A series of tree extending schemes are designed and selectively used based on the attributes of the node and the extending result in each step. We show that the algorithm becomes greedy in constrained environments and promising nodes have higher priority to extend than the non-promising ones. The algorithm is evaluated and applied in assembly path planning. The results show significant performance improvement over the standard RRT planner.

Detection of Video Shot Changes Based on Image Matching

2012 ◽  
Vol 263-266 ◽  
pp. 2064-2069
Author(s):  
Zong Guo Yao ◽  
Min Qin ◽  
Guan Wang ◽  
Jin Ping Li
Keyword(s):  
Change Detection ◽  
Real Time ◽  
Image Matching ◽  
Gray Level ◽  
Key Points ◽  
Video Shot ◽  
Novel Method ◽  
Cut Detection ◽  
Tv Commercials ◽  
Better Than

In order to improve the supervision of TV commercials and reduce the illegal ones, it is necessary to develop a system to detect TV commercials in real time. One of the key points is to detect video shot changes. We put forward a novel method of image matching for shot change detection: firstly, a feature coding is introduced for describing image local gray level distribution; secondly, image matching is performed according to the feature coding; thirdly, shot changes can be detected. A preliminary system of video shot change detection is developed. Experiments show that the algorithm is better than traditional algorithms and can well meet the need of TV Video shot cut detection.

scholarly journals Collision-Free Path-Planning for Six-DOF Serial Harvesting Robot Based on Energy Optimal and Artificial Potential Field

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Lufeng Luo ◽  
Hanjin Wen ◽  
Qinghua Lu ◽  
Haojie Huang ◽  
Weilin Chen ◽  
...  
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Test System ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Optimal Method ◽  
Planning Algorithm ◽  
Six Dof ◽  
Searching Method ◽  
Harvesting Robot

Collision-free autonomous path planning under a dynamic and uncertainty vineyard environment is the most important issue which needs to be resolved firstly in the process of improving robotic harvesting manipulator intelligence. We present and apply energy optimal and artificial potential field to develop a path planning method for six degree of freedom (DOF) serial harvesting robot under dynamic uncertain environment. Firstly, the kinematical model of Six-DOF serial manipulator was constructed by using the Denavit-Hartenberg (D-H) method. The model of obstacles was defined by axis-aligned bounding box, and then the configuration space of harvesting robot was described by combining the obstacles and arm space of robot. Secondly, the harvesting sequence in path planning was computed by energy optimal method, and the anticollision path points were automatically generated based on the artificial potential field and sampling searching method. Finally, to verify and test the proposed path planning algorithm, a virtual test system based on virtual reality was developed. After obtaining the space coordinates of grape picking point and anticollision bounding volume, the path points were drew out by the proposed method. 10 times picking tests for grape anticollision path planning were implemented on the developed simulation system, and the success rate was up to 90%. The results showed that the proposed path planning method can be used to the harvesting robot.

scholarly journals An approximate method for 1-D simulation of pollution transport in streams with dead zones

2018 ◽  
Vol 66 (4) ◽  
pp. 437-447 ◽  
Author(s):  
Marek Sokáč ◽  
Yvetta Velísková ◽  
Carlo Gualtieri
Keyword(s):  
Approximate Method ◽  
Statistical Evaluation ◽  
Pollution Transport ◽  
Dead Zones ◽  
Advection Diffusion Equation ◽  
Gumbel’S Distribution ◽  
Novel Method ◽  
Substance Transport ◽  
Better Than ◽  
Tracer Experiments

Abstract Analytical solutions describing the 1D substance transport in streams have many limitations and factors, which determine their accuracy. One of the very important factors is the presence of the transient storage (dead zones), that deform the concentration distribution of the transported substance. For better adaptation to such real conditions, a simple 1D approximation method is presented in this paper. The proposed approximate method is based on the asymmetric probability distribution (Gumbel’s distribution) and was verified on three streams in southern Slovakia. Tracer experiments on these streams confirmed the presence of dead zones to various extents, depending mainly on the vegetation extent in each stream. Statistical evaluation confirms that the proposed method approximates the measured concentrations significantly better than methods based upon the Gaussian distribution. The results achieved by this novel method are also comparable with the solution of the 1D advection-diffusion equation (ADE), whereas the proposed method is faster and easier to apply and thus suitable for iterative (inverse) tasks.

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