Autonomous local path planning for a mobile robot using a genetic algorithm

Sedighi, K.H.; Ashenayi, K.; Manikas, Theodore W.; Wainwright, Roger L.; Tai, Heng–Ming

doi:10.1109/cec.2004.1331052

Cited by 137 publications

(74 citation statements)

References 18 publications

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“…Genetic approaches include work on the image as a data base of the problem by implementing several tools of Genetic Algorithm GA for image processing [23][24] [25][28].…”

Section: ) Genetic Approachesmentioning

confidence: 99%

Path Planning in a Dynamic Environment

Khaïli¹

2014

ijacsa

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Abstract-Path planning is an important area in the control of autonomous mobile robots. Recent work has focused on aspects reductions in processing time than the memory requirements. A dynamic environment uses a lot of memory and hence the processing time increases too. Our approach is to reduce the processing time by the use of a pictorial approach to reduce the number of data used. In this paper, we present a path planning approach that operates in three steps. First, a construction of the visibility tree is performed. The following treatments are not performed on the original image but on the result tree whose elements are specific points of the environment linked by the relationship of visibility. We construct thereafter a visibility graph which one seeks the shortest path. This approach has a great interest because of its fast execution speed. The path search is extended also for the case where obstacles can move. The moving obstacles may be other mobile robots whose trajectories and speeds are known initially. At the end, some applications are provided on solving similar problem such civil aviation in order to guide plane avoiding collisions.

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“…Genetic approaches include work on the image as a data base of the problem by implementing several tools of Genetic Algorithm GA for image processing [23][24] [25][28].…”

Section: ) Genetic Approachesmentioning

confidence: 99%

Path Planning in a Dynamic Environment

Khaïli¹

2014

ijacsa

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“…The path planning methods are mainly classified into two methods, global planning method and local planning method (Sedighi et al, 2004). For the global planning method, the unmanned ground vehicle requires prior knowledge about the environment and assumes that the terrain is static.…”

Section: Introductionmentioning

confidence: 99%

Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

Al-Mayyahi

Wang

Hussein

et al. 2017

IJICC

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Purpose -The motion control of unmanned ground vehicles is a challenge in the industry of automation. In this paper, a fuzzy inference system based on sensory information is proposed for the purpose of solving the navigation challenge of unmanned ground vehicles in cluttered and dynamic environments.Design/methodology/approach -The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system. If dynamic obstacles move randomly in the operation field, the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations. This paper considers the construction of the fuzzy inference system which consists of two controllers. The first controller uses three sensors based on the obstacles distances from the front, right and left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay. The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.Findings -Experimental analyses conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the fuzzy inference system. The reported simulation results were obtained using MATLAB software package. The results show that the controllers of the fuzzy inference system consistently perform the manoeuvring task and manage the route plan efficiently, even in a complex environment that populated with dynamic obstacles. The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implications -The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move to at various speeds and directions. The methodology of designing the fuzzy inference system is accomplished to guide the unmanned ground vehicle to the desired destination while avoiding collisions with obstacles.However, our methodology is approached using two-dimensional analyses. Hence, the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/value -This paper presents the design of a fuzzy inference system and its characterizations in dynamic environments, specifically for obstacles that move at different velocities. That facilitates an improved functionality of the operation of unmanned ground vehicles.The first author would like to express his gratefulness for his sponsor, the Ministry of Higher Education and Scientific Research in Iraq for funding his PhD scholarship in the United Kingdom. He also would like to thank the University of Basrah in Iraq for the support.2

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“…Path planning can be classified in two categories, global and local path planning. The global type path planning is made for a static and completely known environment while the local path planning is required if the environment is dynamic (Sedighi et al, 2004). In this example, global path planning was performed.…”

Section: Path Planning Problem For Mobile Robotsmentioning

confidence: 99%