FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning

Allaire, François C. J.; Tarbouchi, Mohammed; Labonté, Gilles; Fusina, Giovanni

doi:10.1007/978-1-4020-9137-7_26

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…The cloud center may Because of its rich peripheral interfaces, UAV could also be hardware extendable. Previously, custom hardware design such as Field Programmable Gate Array (FPGA) and Digital Signal Processing (DSP) chip have also been used towards UAV to enhance its computation capability [43][44] [45]. Furthermore, Application-Specific Integrated Circuit (ASIC) approach could also be explored to implement deep learning inference procedures over UAV.…”

Section: Uav Based System Architecturementioning

confidence: 99%

Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV

Wang

Cheng

Liu

et al. 2018

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

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Unmanned Aerial Vehicles (UAVs), have intrigued different people from all walks of life, because of their pervasive computing capabilities. UAV equipped with vision techniques, could be leveraged to establish navigation autonomous control for UAV itself. Also, object detection from UAV could be used to broaden the utilization of drone to provide ubiquitous surveillance and monitoring services towards military operation, urban administration and agriculture management. As the datadriven technologies evolved, machine learning algorithm, especially the deep learning approach has been intensively utilized to solve different traditional computer vision research problems. Modern Convolutional Neural Networks based object detectors could be divided into two major categories: one-stage object detector and two-stage object detector. In this study, we utilize some representative CNN based object detectors to execute the computer vision task over Stanford Drone Dataset (SDD). State-of-the-art performance has been achieved in utilizing focal loss dense detector RetinaNet based approach for object detection from UAV in a fast and accurate manner.

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Section: Uav Based System Architecturementioning

confidence: 99%

Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV

Wang

Cheng

Liu

et al. 2018

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

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“…Finally, we input the R to formula (10), and get a smooth path which can cover all way-points. This path is a valid UAV flying path, which is shown in Fig.…”

Section: B-spline Curves For Smoothing Pathmentioning

confidence: 99%

“…A rapidly-exploring random trees method in [8] is presented to generate near optimal paths for UAVs. Recently, inspired by the well performance of intelligence algorithms, the intelligence approaches have been widely used in path planning, such as evolutionary algorithm (EA) in [9][10][11], which is a search heuristic that mimics the process of natural selection to search optimal path. Particle swarm optimization (PSO) in [12] iteratively improves a candidate solution with regard to a given measure of quality.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Path Planning and Smoothing Algorithm for UAVs in Three Dimensional Environment

Zhao

et al. 2014

2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control

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In three dimensional environments feasible paths for UAVs should be smooth, flyable and safe. To solve the problem of multi-UAVs cooperative path planning with kinds of constraints, a multi-objective optimization approach based on differential evolution is proposed, which uses a group of UAV paths as evolution individuals. In this paper, we adopt a novel combined strategy to enhance the efficiency of difference. To fit UAV constraints and cooperative constraints, a cooperative fitness function is given to measure the pareto optimal solution. And then the control vertexes of a B-spline curve are calculated reversely to get a group of flyable smoothing paths for UAVs. The simulation results show that the proposed approach effectively improves the cooperative accuracy and works well in smoothing paths for UAVs.

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“…Tuncer and Yildirim 25 used a modified mutation operator in a GA-based dynamic path planning of a mobile robot. Allaire et al 26 used GA for motion planning of an unmanned aerial vehicle (UAV) in a complex environment. Hu et al 27 designed an effective vehicle navigation system using GA combined with an A*-based approach.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

Kumar

Parhi

2019

Robotica

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SUMMARYIn the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.

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FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning

Cited by 16 publications

References 22 publications

Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV

Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV

A Cooperative Path Planning and Smoothing Algorithm for UAVs in Three Dimensional Environment

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

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