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

Rowduru, SreeHarsha; Kumar, Niranjan; Kumar, Aditya

doi:10.1177/0959651819847813

Cited by 4 publications

(5 citation statements)

References 69 publications

(72 reference statements)

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“…In order to develop autonomous driving machinery, a cyclic flow of information is required; it is known as the sense-perceive-plan-act (SPPA) cycle [51]. The SPPA cycle connects sensing, perceiving, planning and acting through a closed-loop relation; sensors collect (sense) physical information, the information is received and interpreted (perceive), feasible trajectories for navigation are selected (plan) and the tractor is controlled to follow the selected trajectory (act).…”

Section: Basic Modelingmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation.

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Section: Basic Modelingmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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“…Scholars have performed a lot of research and analysis on the core technology of unmanned LHD vehicles [4][5][6][7][8]. For example, Rowduru [9] reviews the automation of the steering mechanism for LHD vehicles and Gao [10] analyzes the steering characteristics. Altafini [11] proposes a path tracking criterion for an articulated LHD vehicle and Yu [12] compares and optimizes the path tracking control algorithm of the LHD vehicle.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Unmanned Driving System for Load Haul Dump Vehicles in Underground Mines

et al. 2022

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This paper proposes the modeling and simulation of the unmanned driving system for underground load haul dump vehicles based on Gazebo/Ros. Firstly, the kinematics model of the load haul dump vehicle is derived. Then, the model of each part of the load haul dump vehicle is established based on SolidWorks and the model of the load haul dump vehicle is established by connecting the parts through a unified robot description format (URDF) file. Finally, the laneway model is established by using alpha shape to realize the modeling of the operating environment of the load haul dump vehicle. The speed, angular speed, bucket lifting, and bucket flipping of the load haul dump vehicle are controlled using PID. The experimental results show that: The control errors of the speed and angular speed of the load haul dump vehicle are 0.283 m/s and 0.010 rad/s, respectively. The control error of the lifting bucket is 0.025 m and that of the flipping bucket is 0.015 m. The angular velocity control error of the simulation system relative to the actual system is 0.330 and 0.106 m/s, respectively. The error between the SLAM of the simulation system and the actual system and the measured value is 0.917 and 3.44 m, respectively. The control performance of the load haul dump vehicle in the simulation system is good. Therefore, automatic driving algorithms can be studied and tested in this simulation platform.

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“…This solution is also preferred to the skid steering since it does not require a thrusts adjustment between the outside and inside tracks so that the resultant forward thrust can be maintained during a turning maneuver, as shown in [5]. Nowadays, articulated tracked vehicles have been widely adopted in several engineering fields, e.g., planetary exploration, military [6], agriculture [7] and construction [8].…”

Section: Introductionmentioning

confidence: 99%

“…The articulated steering is usually accomplished by installing a hydraulic steering system able to provide the power required to overcome vehicle lateral resistances arising during the relative rotation between the two units [8]. The kineto-dynamic behavior of the hydraulic steering mechanism on articulated vehicles was analyzed in [9,10], thus steering controller for path tracking or path following applications.…”

Section: Introductionmentioning

confidence: 99%

Analytical Study on the Cornering Behavior of an Articulated Tracked Vehicle

2021

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Articulated tracked vehicles have been traditionally studied and appreciated for the extreme maneuverability and mobility flexibility in terms of grade and side slope capabilities. The articulation joint represents an attractive and advantageous solution, if compared to the traditional skid steering operation, by avoiding any trust adjustment between the outside and inside tracks. This paper focuses on the analysis and control of an articulated tracked vehicle characterized by two units connected through a mechanical multiaxial joint that is hydraulically actuated to allow the articulated steering operation. A realistic eight degrees of freedom mathematical model is introduced to include the main nonlinearities involved in the articulated steering behavior. A linearized vehicle model is further proposed to analytically characterize the cornering steady-state and transient behaviors for small lateral accelerations. Finally, a hitch angle controller is designed by proposing a torque-based and a speed-based Proportional Integral Derivative (PID) logics. The controller is also verified by simulating maneuvers typically adopted for handling analysis.

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

Cited by 4 publications

References 69 publications

Computer Vision in Self-Steering Tractors

Computer Vision in Self-Steering Tractors

Modeling and Simulation of Unmanned Driving System for Load Haul Dump Vehicles in Underground Mines

Analytical Study on the Cornering Behavior of an Articulated Tracked Vehicle

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