Strategies for Speeding Up Manipulator Path Planning to Find High Quality Paths in Cluttered Environments

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Pradeep Rajendran ◽  
Shantanu Thakar ◽  
Prahar M. Bhatt ◽  
Ariyan M. Kabir ◽  
Satyandra K. Gupta
Keyword(s):  
Path Planning ◽  
Failure Rate ◽  
Search Space ◽  
Tree Search ◽  
High Quality ◽  
Planning Time ◽  
Cluttered Environments ◽  
Manufacturing Applications ◽  
Point To Point ◽  
Path Quality

Abstract In many manufacturing applications, robotic manipulators need to operate in cluttered environments. Quickly finding high-quality paths is very important in applications that require high part fix and frequent setup changes. This paper presents a point-to-point path planning framework for manipulators operating in cluttered environments. It uses a bi-directional tree-search to find path and facilitates finding a balance between path quality and planning time. The framework dynamically switches between search strategies based on the search progress to produce high-quality paths quickly. This paper three main contributions. First, we extend a previously developed sampling-based modular tree-search. Specifically, we present a strategy that can sample effectively in challenging regions of the search-space by using local approximations of the configuration space. Second, we add new strategies and scheduling logic that decreases the failure rate as well as the planning time compared to the prior work. We also present an inter-tree connection strategy that adapts to collision information gathered over time. We introduce a scheduling rule that regulates the exploitation of focusing hints derived from the workspace obstacles. Third, we present theoretical reasoning behind strategy switching and how it can help decrease planning times and increase path quality. Together, these new features the reduce average failure rate by a factor of 4 and improve the average planning time by 22% over the previous work.

Randomized path planning with preferences in highly complex dynamic environments

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1195-1208 ◽  
Author(s):  
Khaled Belghith ◽  
Froduald Kabanza ◽  
Leo Hartman
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Control Strategies ◽  
Sampling Strategy ◽  
Search Space ◽  
Dynamic Environments ◽  
Planning Time ◽  
Probabilistic Roadmap ◽  
Simple Sampling ◽  
Search Control

SUMMARYIn this paper we consider the problem of planning paths for articulated bodies operating in workplaces containing obstacles and regions with preferences expressed as degrees of desirability. Degrees of desirability could specify danger zones and desire zones. A planned path should not collide with the obstacles and should maximize the degrees of desirability. Region desirability can also convey search-control strategies guiding the exploration of the search space. To handle desirability specifications, we introduce the notion of flexible probabilistic roadmap (flexible PRM) as an extension of the traditional PRM. Each edge in a flexible PRM is assigned a desirability degree. We show that flexible PRM planning can be achieved very efficiently with a simple sampling strategy of the configuration space defined as a trade-off between a traditional sampling oriented toward coverage of the configuration space and a heuristic optimization of the path desirability degree. For path planning problems in dynamic environments, where obstacles and region desirability can change in real time, we use dynamic and anytime search exploration strategies. The dynamic strategy allows the planner to replan efficiently by exploiting results from previous planning phases. The anytime strategy starts with a quickly computed path with a potentially low desirability degree which is then incrementally improved depending on the available planning time.

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.

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

2022 ◽  
pp. 1-20
Author(s):  
Amin Basiri ◽  
Valerio Mariani ◽  
Giuseppe Silano ◽  
Muhammad Aatif ◽  
Luigi Iannelli ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Flight Control ◽  
Precision Agriculture ◽  
Navigation Systems ◽  
Agricultural Crops ◽  
Cluttered Environments ◽  
Main Motivation ◽  
Autonomous Guidance ◽  
Planning Problems

Abstract Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landing (VTOL) capabilities and the capacity to carry out tasks with complete autonomy, they are now a standard platform for both research and industrial uses. However, while the flight control architecture is well established in the literature, there are still many challenges in designing autonomous guidance and navigation systems to make the UAV able to work in constrained and cluttered environments or also indoors. Therefore, the main motivation of this work is to provide a comprehensive and exhaustive literature review on the numerous methods and approaches to address path-planning problems for multi-rotor UAVs. In particular, the inclusion of a review of the related research in the context of Precision Agriculture (PA) provides a unified and accessible presentation for researchers who are initiating their endeavours in this subject.

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

scholarly journals A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 163694-163702
Author(s):  
Yu-Wei Xia ◽  
Chao Yang ◽  
Bing-Qiu Chen
Keyword(s):  
Monte Carlo ◽  
Path Planning ◽  
Planning Method ◽  
Tree Search ◽  
Monte Carlo Tree Search

Planning the Shortest Path in Cluttered Environments: A Review and a Planar Convex Hull-Based Approach

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Nafiseh Masoudi ◽  
Georges M. Fadel ◽  
Margaret M. Wiecek
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Time Complexity ◽  
Optimal Path ◽  
Optimal Solution ◽  
Free Graph ◽  
Worst Case ◽  
Cluttered Environments ◽  
Planar Convex ◽  
Polyhedral Obstacles

Abstract Routing or path-planning is the problem of finding a collision-free and preferably shortest path in an environment usually scattered with polygonal or polyhedral obstacles. The geometric algorithms oftentimes tackle the problem by modeling the environment as a collision-free graph. Search algorithms such as Dijkstra’s can then be applied to find an optimal path on the created graph. Previously developed methods to construct the collision-free graph, without loss of generality, explore the entire workspace of the problem. For the single-source single-destination planning problems, this results in generating some unnecessary information that has little value and could increase the time complexity of the algorithm. In this paper, first a comprehensive review of the previous studies on the path-planning subject is presented. Next, an approach to address the planar problem based on the notion of convex hulls is introduced and its efficiency is tested on sample planar problems. The proposed algorithm focuses only on a portion of the workspace interacting with the straight line connecting the start and goal points. Hence, we are able to reduce the size of the roadmap while generating the exact globally optimal solution. Considering the worst case that all the obstacles in a planar workspace are intersecting, the algorithm yields a time complexity of O(n log(n/f)), with n being the total number of vertices and f being the number of obstacles. The computational complexity of the algorithm outperforms the previous attempts in reducing the size of the graph yet generates the exact solution.

scholarly journals Mobile robot path planning using an improved ant colony optimization

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877467 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Search Space ◽  
Step Length ◽  
Ant Colony ◽  
Dynamic Adjustment ◽  
Feedback Information ◽  
Intelligent Optimization Algorithm ◽  
Selection Of

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

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