Neural Networks Based Path Planning and Navigation of Mobile Robots

Kroumov, Valeri; Yu, Junzhi

doi:10.5772/26889

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…Simulated annealing method has been developed to avoid such type of problem. 52 Kroumov and Yu 53 addressed the problems related to generating optimal paths with the help of neural networks. Modified pulse-coupled neural network (MPCNN) is a real-time system which constructs a grid-based map by processing images captured using vision camera sensors.…”

Section: Development Stages For Automatic Steering System Of the Lhd mentioning

confidence: 99%

A critical review on automation of steering mechanism of load haul dump machine

Rowduru

Kumar

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a brief note on the evolution of steering mechanisms and more emphasized on articulated steering system of the load haul dump machine. In this respect, pictorial representation of the evolution of steering mechanisms for on-road and articulated steering mechanisms of the load haul dump machine is made from the available literature. Critical review on basic elements required for the complete automation of the load haul dump vehicle steering system is presented. Different types of controllers for path tracking error minimization of the scale-modeled or simulated model of the load haul dump steering system are tabulated. Few case studies stimulating the complete automation of the load haul dump steering system employed on the field are also discussed. Challenges and some research gaps in making fully automated steering system of the load haul dump machine are identified in this review article. At the end, based on the critical review, some novel methods for making the fully automated steering system of the load haul dump machine is provided, which is the potential future work for the design and development of feasible automatic steering system.

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Section: Development Stages For Automatic Steering System Of the Lhd mentioning

confidence: 99%

A critical review on automation of steering mechanism of load haul dump machine

Rowduru

Kumar

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Using a topologically‐ordered map, [LYS09] plan paths in a 2D multi‐agent environment, later extended to a cooperative hunting task by [NY11]. [KY11] introduces a neural network that has an obstacle description of a stationary 2D environment in its hidden layer and outputs a repulsive penalty function to solve the local minima problem of the artificial potential field method in navigating mobile robots. [DH14] uses ant colony optimization to plan a global path with least repulsive penalty function produced by a neural network that has obstacle information in its hidden layers.…”

Section: Related Workmentioning

confidence: 99%

Learning Physically Based Humanoid Climbing Movements

Naderi

Babadi

Hämäläinen

2018

Computer Graphics Forum

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We propose a novel learning‐based solution for motion planning of physically‐based humanoid climbing that allows for fast and robust planning of complex climbing strategies and movements, including extreme movements such as jumping. Similar to recent previous work, we combine a high‐level graph‐based path planner with low‐level sampling‐based optimization of climbing moves. We contribute through showing that neural network models of move success probability, effortfulness, and control policy can make both the high‐level and low‐level components more efficient and robust. The models can be trained through random simulation practice without any data. The models also eliminate the need for laboriously hand‐tuned heuristics for graph search. As a result, we are able to efficiently synthesize climbing sequences involving dynamic leaps and one‐hand swings, i.e. there are no limits to the movement complexity or the number of limbs allowed to move simultaneously. Our supplemental video also provides some comparisons between our AI climber and a real human climber.

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“…The spanning tree method combines every four grids into a unit and realizes traversal by connecting all units. This method requires a lot of storage space, and the path turns into the biological excitation neural network algorithm [5]. The neuron uses the shunt equation to communicate with the adjacent neurons.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithms for Coverage Path Planning using Mobile Robots

Yu¹

2023

HSET

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Using the grid method to establish the environmental model, the map is divided into several sub-regions according to the environmental characteristics, and the connection between the sub-regions is established. Next, the Genetic Algorithm encodes all sub-regions, and the base point information between and within each region is established to obtain the optimal coverage sequence. Partial coverage is achieved in each sub-region by means of reciprocating motion, which turns the complete coverage problem into a traveling salesman problem (TSP). Finally, the Genetic Algorithm is used to conduct an experimental simulation to obtain the correlation between the fitness and the number of iterations, and obtain the optimal traversal sequence, which proves that the method is effective and feasible.

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Neural Networks Based Path Planning and Navigation of Mobile Robots

Cited by 11 publications

References 22 publications

A critical review on automation of steering mechanism of load haul dump machine

A critical review on automation of steering mechanism of load haul dump machine

Learning Physically Based Humanoid Climbing Movements

Genetic Algorithms for Coverage Path Planning using Mobile Robots

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