Artificial Intelligence in the Path Planning Optimization of Mobile Agent Navigation

Brassai, Sándor Tihamér; Iantovics, László Barna; Enăchescu, Călin

doi:10.1016/s2212-5671(12)00147-5

Cited by 20 publications

(4 citation statements)

References 13 publications

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“…One of the key aspects of AI is efficient Computational Intelligence (CI) algorithms (Zhao et al, 2018), such as neural networks and evolutionary algorithms. There are several AI-based methods for multi-rotor UAVs path planning, including ACO (Deb, 2011), ANN (ANN, 2020), brute force search (Brassai et al, 2012), GA (Pham et al, 2017), heuristic search (Zhao et al, 2018), Variable Neighbourhood Search (VNS) (Hansen et al, 2010) and data-driven planners (Tamar et al, 2016).…”

Section: Ai-based Techniquesmentioning

confidence: 99%

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

et al. 2022

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Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landing (VTOL) capabilities and the capacity to carry out tasks with complete autonomy, they are now a standard platform for both research and industrial uses. However, while the flight control architecture is well established in the literature, there are still many challenges in designing autonomous guidance and navigation systems to make the UAV able to work in constrained and cluttered environments or also indoors. Therefore, the main motivation of this work is to provide a comprehensive and exhaustive literature review on the numerous methods and approaches to address path-planning problems for multi-rotor UAVs. In particular, the inclusion of a review of the related research in the context of Precision Agriculture (PA) provides a unified and accessible presentation for researchers who are initiating their endeavours in this subject.

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Section: Ai-based Techniquesmentioning

confidence: 99%

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

et al. 2022

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“…Some works on robotic neural path planner and decentralized neural learning for cooperative robots tasks 22 have been reported. The connectivity into competitive neural networks has been studied 36 to understand the multi-stability and multi-periodicity of high-order competitive neural networks with a general class of activation functions. 37 The general class of activation functions consist of general bounded smooth sigmoidal functions (e.g.…”

Section: Neural Task Allocationmentioning

confidence: 99%

Neural control and coordination of decentralized transportation robots

Martínez‐García

Torres-Cordoba

Carrillo-Saucedo

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This work presents the modeling, control architecture and simulation of a decentralized multi-robot system for transporting material in a warehouse. Each robot has a task scheduler comprising two different neural networks for task allocation and fault tolerance. The path planner consists of a first-order dynamical state equation to control the robot's four-wheel asynchronous driving and steering, as well as a partial differential equation to coordinate speeds and arrival times. The task allocation and motion coordination combine the robot's kinematic control law with a one-layer artificial neural network that classifies five-dimensional symbolic logical equations that define the state transitions between asynchronous events. These events include carry and fetch, material supply, robots stop, obstacle avoidance and battery state. Another multilayer artificial neural network reads the same state inputs for fault detection and recovery. The two neural systems feed forward a navigation planner, which uses a partial differential equation to coordinate the robot's speed and its relaxation time with respect to the robot in front of it. The energy cost is measured by a Lagrangian function. The proposed planning control scheme was computationally validated through parallel computing simulations. The system is shown to be consistent, reliable and feasible, and it allows for fast navigational tasks.

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“…Critical parts of the real time localization process, for which calculations must be performed in a short period of time, such as triangulation, pre-filtering and the Kalman Filter, are modeled and implemented in hardware. For the future, the aim is to integrate into a single system the indoor localization methods, UAV systems [13] [14] and mobile robots [15] [16] [17], enabling mobile robots and UAV systems to be used for special indoors or even outdoors applications.…”

Section: Introductionmentioning

confidence: 99%

Radio Frequency (RF)-based, Real-Time, Indoor Localization System for Unmanned Aerial Vehicles and Mobile Robot Applications

Brassai¹,

Szekely²

2020

ACTA POLYTECH HUNG

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The goal of this paper is to describe the indoor localization of a moving object, such as, an UAV or mobile robot, in real time. For localization, the triangulation method is used, based on a multiple fixed points anchors. The distance measurement between the moving object and the anchors is realized with DWM1000 UWB compliant, wireless transceiver modules. For distance measurement, the double-sided, two-way ranging method is used. The position is revealed after the triangulation is corrected, based on the Kalman filter. This computationally intensive task is running on FPGA and provides a realtime operation. One of the anchors is connected to FPGA with a serial communication interface. For the triangulation and Kalman filter, hardware module generation high level synthesis, is used. In the paper, in addition to the implementation, measurement results are also presented for real-time localization.

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Artificial Intelligence in the Path Planning Optimization of Mobile Agent Navigation

Cited by 20 publications

References 13 publications

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

Neural control and coordination of decentralized transportation robots

Radio Frequency (RF)-based, Real-Time, Indoor Localization System for Unmanned Aerial Vehicles and Mobile Robot Applications

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