scholarly journals A Re-Entry Path Planning Method for Service Robots Based on Dynamic Inver-Over Evolutionary Algorithm

2019 ◽  
Vol 10 (1) ◽  
pp. 305
Author(s):  
Yong Tao ◽  
Chaoyong Chen ◽  
Tianmiao Wang ◽  
Youdong Chen ◽  
Hegen Xiong ◽  
...  
Keyword(s):  
Path Planning ◽  
Dynamic Properties ◽  
Planning Method ◽  
Service Robots ◽  
Planning Problem ◽  
Time Dynamic ◽  
Coverage Path Planning ◽  
Cleaning Robots ◽  
Changes Over Time ◽  
Path Planning Problem

A re-entry path planning method in omitting areas for service robots is suggested based on dynamic Inver-Over evolutionary algorithms after the robot automatically avoids obstacles. The complete coverage path planning is researched for cleaning service robots. Combined with features of dynamic travelling salesmen problem (DTSP), a local operator is employed for the path planning to enhance real-time dynamic properties of the Inver-Over algorithm. The method addresses the path planning problem that a number of cells undergo dynamic changes over time under work environment of cleaning robots. With simulations and experiments performed, it is discovered that the average relative error is 2.2% between the re-entry path planning and the best path, which validates the effectiveness and feasibility of the method.

scholarly journals Proposed Smooth-STC Algorithm for Enhanced Coverage Path Planning Performance in Mobile Robot Applications

Robotics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 44 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore ◽  
Dinh Xuan Truong
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Forest Monitoring ◽  
Planning Problem ◽  
Operational Environment ◽  
Uncertain Environments ◽  
Coverage Path Planning ◽  
Agricultural Robots ◽  
Cleaning Robots ◽  
Path Planning Problem

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement. The aim of path planning is to optimise the efficacy of robotic movement in a defined operational environment. For example, robots have been employed in many domains including: Cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, and agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.

Coverage Path Planning Using Mobile Robot Team Formations

2015 ◽  
pp. 214-247
Author(s):  
Prithviraj Dasgupta
Keyword(s):  
Path Planning ◽  
A Priori ◽  
Experimental Results ◽  
Future Research ◽  
Planning Problem ◽  
Coverage Path Planning ◽  
Preliminary Results ◽  
Time And Energy ◽  
Path Planning Problem ◽  
Multi Robot

The multi-robot coverage path-planning problem involves finding collision-free paths for a set of robots so that they can completely cover the surface of an environment. This problem is non-trivial as the geometry and location of obstacles in the environment is usually not known a priori by the robots, and they have to adapt their coverage path as they discover obstacles while moving in the environment. Additionally, the robots have to avoid repeated coverage of the same region by each other to reduce the coverage time and energy expended. This chapter discusses the research results in developing multi-robot coverage path planning techniques using mini-robots that are coordinated to move in formation. The authors present theoretical and experimental results of the proposed approach using e-puck mini-robots. Finally, they discuss some preliminary results to lay the foundation of future research for improved coverage path planning using coalition game-based, structured, robot team reconfiguration techniques.

Research on Travel Path Planning Based on Group Related Mapping

2014 ◽  
Vol 644-650 ◽  
pp. 5836-5839
Author(s):  
Li Na Tan
Keyword(s):  
Path Planning ◽  
Mapping Method ◽  
Planning Method ◽  
Planning Problem ◽  
Travel Path ◽  
Path Planning Problem

This paper analyzed travel path planning problem. Firstly, it reviewed some references about path planning method and found that those methods were not suit for travel path planning. Secondly, it proposed group related mapping method to solve travel path planning problem. This method had two steps, arranging trips when conflicts were overlooked and rearranging the trips when conflicts were eliminated. Thirdly, to explain the arrangement clearly, it took schedule of ten days travel along the Big Long River as an example. The result showed that the arrangement of all the accessible trips could be worked out during the whole rafting season.

Rough level path planning method for a robot using SOFM neural network

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 415-423 ◽  
Author(s):  
Kimmo Pulakka ◽  
Veli Kujanpää
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Industrial Robot ◽  
Planning Method ◽  
Planning Problem ◽  
Test Results ◽  
Feature Map ◽  
Working Environments ◽  
Path Planning Problem

In this paper a path planning method for off-line programming of a joint robot is described. The method can automatically choose the easiest and safest route for an industrial robot from one position to another. The method is based on the use of a Self Organised Feature Map (SOFM) neural network. By using the SOFM neural network the method can adapt to different working environments of the robot. According to test results one can conclude that the SOFM neural network is a useful tool for the path planning problem of a robot.

Human Diver-Inspired Visual Navigation: Towards Coverage Path Planning of Shipwrecks

2021 ◽  
Vol 55 (4) ◽  
pp. 24-32
Author(s):  
Nare Karapetyan ◽  
James V. Johnson ◽  
Ioannis Rekleitis
Keyword(s):  
Path Planning ◽  
Region Of Interest ◽  
Visual Navigation ◽  
Planning Problem ◽  
Coverage Path Planning ◽  
Autonomous Operation ◽  
Navigation Strategy ◽  
Control Command ◽  
Path Planning Problem ◽  
Underwater Structures

Abstract This work proposes vision-only navigation strategies for an autonomous underwater robot. This approach is a step towards solving the coverage path planning problem in a 3-D environment for surveying underwater structures. Given the challenging conditions of the underwater domain, it is very complicated to obtain accurate state estimates reliably. Consequently, it is a great challenge to extend known path planning or coverage techniques developed for aerial or ground robot controls. In this work, we are investigating a navigation strategy utilizing only vision to assist in covering a complex underwater structure. We propose to use a navigation strategy akin to what a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. The focus of this article is a step towards enabling the autonomous operation of lightweight robots near underwater wrecks in order to collect data for creating photo-realistic maps and volumetric 3-D models while at the same time avoiding collisions. The proposed method uses convolutional neural networks to learn the control commands based on the visual input. We have demonstrated the feasibility of using a system based only on vision to learn specific strategies of navigation with 80% accuracy on the prediction of control command changes. Experimental results and a detailed overview of the proposed method are discussed.

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