2,616 research outputs found
A Real-time Global Optimal Path Planning for mobile robot in Dynamic Environment Based on Artificial Immune Approach
This paper illustrates a method to finding a globaloptimal path in a dynamic environment of known obstacles foran Mobile Robot (MR) to following a moving target. Firstly, theenvironment is defined by using a practical and standard graphtheory. Then, a suboptimal path is obtained by using DijkstraAlgorithm (DA) that is a standard graph searching method. Theadvantages of using DA are; elimination the uncertainness ofheuristic algorithms and increasing the speed, precision andperformance of them. Finally, Continuous Clonal SelectionAlgorithm (CCSA) that is combined with Negative SelectionAlgorithm (NSA) is used to improve the suboptimal path andderive global optimal path. To show the effectiveness of themethod it is compared with some other methods in this area
Mobile robot transportation in laboratory automation
In this dissertation a new mobile robot transportation system is developed for the modern laboratory automation to connect the distributed automated systems and workbenches. In the system, a series of scientific and technical robot indoor issues are presented and solved, including the multiple robot control strategy, the indoor transportation path planning, the hybrid robot indoor localization, the recharging optimization, the robot-automated door interface, the robot blind arm grasping & placing, etc. The experiments show the proposed system and methods are effective and efficient
Motion Planning for Autonomous Ground Vehicles Using Artificial Potential Fields: A Review
Autonomous ground vehicle systems have found extensive potential and
practical applications in the modern world. The development of an autonomous
ground vehicle poses a significant challenge, particularly in identifying the
best path plan, based on defined performance metrics such as safety margin,
shortest time, and energy consumption. Various techniques for motion planning
have been proposed by researchers, one of which is the use of artificial
potential fields. Several authors in the past two decades have proposed various
modified versions of the artificial potential field algorithms. The variations
of the traditional APF approach have given an answer to prior shortcomings.
This gives potential rise to a strategic survey on the improved versions of
this algorithm. This study presents a review of motion planning for autonomous
ground vehicles using artificial potential fields. Each article is evaluated
based on criteria that involve the environment type, which may be either static
or dynamic, the evaluation scenario, which may be real-time or simulated, and
the method used for improving the search performance of the algorithm. All the
customized designs of planning models are analyzed and evaluated. At the end,
the results of the review are discussed, and future works are proposed
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