A genetic algorithm for PDA optimal path generation using GPS

Lee, Malrey; Lee, Young-Keun; Rittenhouse, Robert G.

doi:10.1016/j.asoc.2012.03.043

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Heuristics can be understood as a method of an efficient algorithm that can find the best possible solution in near-optimal time. The traditional methods are often cumbersome and time-consuming, whereas the heuristic methods can drastically shorten the search for a solution [6,7].…”

Section: The Idea Of Dynamic Path Planningmentioning

confidence: 99%

Impulse Artificial Neural Networks in Internal Transport

Ochelska-Mierzejewska

2014

Management and Production Engineering Review

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The second most important function of a warehouse, apart from the storing of goods, is internal transport with a focus on time-effectiveness. When there is a time gap between the production and export of products, the goods need to be stored until they are dispatched to the consumers. An important problem that concerns both large and small warehouses is the selection of priorities, that is handling the tasks in order of importance. Another problem is to identify the most efficient routes for forklift trucks to transport goods from a start-point to a desired destination and prevent the routes from overlapping. In automated warehouses, the transport of objects (the so called pallets of goods) is performed by machines controlled by a computer instead of a human operator. Thus, it is the computer, not the man, that makes the difficult decisions regarding parallel route planning, so that the materials are transported within the warehouse in near-optimal time. This paper presents a method for enhancing this process.

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Section: The Idea Of Dynamic Path Planningmentioning

confidence: 99%

Impulse Artificial Neural Networks in Internal Transport

Ochelska-Mierzejewska

2014

Management and Production Engineering Review

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“…The path planning capability of the robot intuitively reflects its operational and risk management capabilities, and its main goal is to allow the target object to find a collision-free and safe path from the starting point to the end point within a defined area [4]. Path planning has been widely used in high technology, daily life, logistics management and other fields, such as: manipulator path planning [5,6], aircraft trajectory planning [7], cruise missile path planning [8], traveling salesman problem [9], path planning based on road networks [10], electronic maps GPS navigation path search and planning [11], and other areas [12,13]. However, most of the current path planning algorithms are for known environments or static models, which are less robust to environmental changes, errors and noise.…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

Li Song,

Li Song

2023

Journal of Computers

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<p>With the complexity of the robot operating environment increases, there becoming higher demands on the optimal path planning for robots. Most of the path planning is performed in known environments and static models. However, there are still challenges for robots to perform path planning in complex unknown or dynamic environments, which will suffer from deadlock problems and obstacle avoidance failures. Reinforcement learning (RL) can help fuzzy algorithm to optimize the strategy. However, the difficulty of designing the rewards in RL makes the algorithm require a large number of samples to learn the strategy, resulting in computational complexity. To solve these problems, a new local path planning based on the improved fuzzy and Q(λ)-learning algorithms is proposed, aiming to plan the shortest path and avoid obstacles. For solving the problems of breaking through and avoiding obstacles, a fuzzy controller is designed. The distance of nearest obstacle in front of the mobile robot and the distance between the obstacles in the two breakout directions are regarded as the two inputs for this controller. And the two fuzzy quantities of the mobile robot’s running angle and the safe step length are outputted. In the path planning, the Q(λ)-learning algorithm are used to optimize the weights of the running angle and the safe step, obtaining a more accurate robot position and speeding up path planning efficiency. Furthermore, to solve the overlap problems among the starting point, end point, and obstacles, a safer running environment is designed considering radiuses of these objects. Besides, the mobile robot breakout scheme and sustainable obstacle avoidance scheme are designed to solve the deadlock problem and “large obstacle” avoidance problem, respectively. Simulation results in the sparse and complex operating environment show that our proposed algorithm can plan a relatively optimal and safe path, improving the success rate of path planning.</p> <p> </p>

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A genetic algorithm for PDA optimal path generation using GPS

Cited by 2 publications

References 6 publications

Impulse Artificial Neural Networks in Internal Transport

Impulse Artificial Neural Networks in Internal Transport

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

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