Development of an intelligent master-slave system between agricultural vehicles

This paper presents a method to develop an intelligent master-slave system between agricultural vehicles, which will enable a semi-autonomous agricultural vehicle (slave) to follow a leading tractor (master) with a given lateral and longitudinal offset. In our study not only the follow-up motions but also the site-specific control of the apparatus such as rear and front power lift was considered. In the first part of this paper the recent research works in the area autonomous farming were discussed and the restrictions of these research works were illustrated. In the second part an approach to construct a master-slave system between two agricultural vehicles was demonstrated. In the next part the mathematic modelling of this master-slave system and the simulation results about the control algorithm were demonstrated. Afterwards the result of a real field test was presented and the safety considerations about such an intelligent vehicle system were made

Environment mapping enabling safety and usability of an electronic tow bar

Cost efficiency and productivity as well as drivers comfort and usability are significant innovation drivers for agricultural machinery. The proposed electronic tow bar system for tillage processes consists of two vehicles, coupled by wireless data connection. An unmanned slave tractor follows a master tractor with a position dependent lateral and longitudinal offset. Operating two tractors with one driver only, increases productivity and improves the capacity load due to higher flexibility in fleet management. In return, the usability and safety of the tow bar becomes a major concern, which is addressed by an elaborate safety concept enabled by sensor based obstacle detection and mapping. Web-based geo-information, are used to support proactive path planning.This paper presents a solution to achieve both, safety and usability, for a complex platoon system. The interaction of the operator with the local and global obstacle map is designed to meet the requirements of both target functions

Design and Implementation of Fuzzy Logic Controller for Online Computer Controlled Steering System for Navigation of a Teleoperated Agricultural Vehicle

This paper describes design, modeling, simulation, control, and implementation of teleoperated agricultural vehicle using intelligent technique. This vehicle can be used for ploughing, sowing, and soil moisture sensing. Online computer controlled steering system for a vehicle utilizing two independent drive wheels can be used to avoid obstacles and to improve the ability to resist external side forces. To control the steer angles of the nondriven wheels, the mathematical relationships between the drive wheel speeds and the steer angles of the nondriven wheels are used. A fuzzy logic controller is designed to change the drive wheel speeds and to achieve the desired steer angles. Online control of the agricultural vehicle is achieved from a remote place by means of Web Publishing Tool in LabVIEW. IR sensors in the vehicle are used to detect and to avoid the obstacles around. The developed steering angle control algorithm and fuzzy logic controller have been implemented in an agricultural vehicle which depicts that the vehicle performs its operation efficiently and reduces the manpower and becomes advantageous

SMART sensor network: with Bluetooth low energy and CAN-BUS

This paper proposes a system to monitor, through the internet, the data of a logistics distribution truck. For this, it was implemented a reliable and flexible wireless sensor network with low energy consumption. The technology used for the radio system was Bluetooth Low Energy (BLE). Each node in the network contains one type of sensor. The sensors information together with GPS and On-Board Diagnostics (OBD) data collected by the central unit, and later transmitted to the cloud by GSM or Wi-Fi.info:eu-repo/semantics/acceptedVersio

Design Issues and in Field Tests of the New Sustainable Tractor LOCOSTRA

first, in Italy, focusing on the agricultural application of the machine, in natural scenarios with different ground and vegetatio

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir

Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can jeopardize the performance and efficiency of a robot when performing agricultural activity. For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. A test bed equipped with a tracked-driven robot and three types o f terrain i.e. sand, gravel and vegetation has been developed. A small and low power MEMS accelerometer is integrated into the robot for measuring the vertical acceleration. In this experiment, the vibration signals resulted from the interaction between the robot and the different type of terrain were collected. An extensive experimental study was conducted to evaluate the effectiveness of SVM. The results in terms of accuracy of two machine learning techniques based on terrain classification are analyzed and compared. The results show that the robot that is equipped with an SVM can recognize different terrain conditions effectively. Such capability enables the robot to traverse across changing terrain conditions without being trapped in the field. Hence, this research work contributes to develop a self-adaptive agricultural robot in coping with different terrain conditions with minimum human supervision

Trolls: a novel low-cost controlling system platform for walk-behind tractor

A novel low-cost controlling system platform for walk-behind hand tractors (Quick G3000 and G1000) was designed and developed to solve the fatigue problem faced by farmers when ploughing the rice field. This platform is dedicated to designing and manufacturing mechanical, electrical, and software components. The tractor was modified and added with an embedded control system that functioned as the slave, while the direction of the tractor movement was controlled remotely by humans through Bluetooth communication with the smartphone application as the master. Several servos and direct currents (DCs) were used as the actuator to move some levers and clutches instead of the tractor to make it remotely controllable. This system has been directly tested in the paddy farming land through two tractors: Quick G3000 and G1000. The testing results showed that this system could be used within more or less six hours; there is a cost-efficiency of 21.74% and 84.62% battery usage efficiency. More efficient mechanics caused this cost efficiency, and the reduction in electronic devices affects battery efficiency. A low-cost platform for controlling walk-behind tractors has been successfully developed; this platform assists farmers in ploughing their fields

USING THE VEHICLE ROUTING PROBLEM (VRP) TO PROVIDE LOGISTICS SOLUTIONS IN AGRICULTURE

Agricultural producers consider utilizing multiple machines to reduce field completion times for improving effective field capacity. Using a number of smaller machines rather than a single big machine also has benefits such as sustainability via less compaction risk, redundancy in the event of an equipment failure, and more flexibility in machinery management. However, machinery management is complicated due to logistics issues. In this work, the allocation and ordering of field paths among a number of available machines have been transformed into a solvable Vehicle Routing Problem (VRP). A basic heuristic algorithm (a modified form of the Clarke-Wright algorithm) and a meta-heuristic algorithm, Tabu Search, were employed to solve the VRP. The solution considered optimization of field completion time as well as improving the field efficiency. Both techniques were evaluated through computer simulations with 2, 3, 5, or 10 vehicles working simultaneously to complete the same operation. Furthermore, the parameters of the VRP were changed into a dynamic, multi-depot representation to enable the re-route of vehicles while the operation is ongoing. The results proved both the Clarke-Wright and Tabu Search algorithms always generated feasible solutions. The Tabu Search solutions outperformed the solutions provided by the Clarke-Wright algorithm. As the number of the vehicles increased, or the field shape became more complex, the Tabu Search generated better results in terms of reducing the field completion times. With 10 vehicles working together in a real-world field, the benefit provided by the Tabu Search over the Modified Clarke-Wright solution was 32% reduction in completion time. In addition, changes in the parameters of the VRP resulted in a Dynamic, Multi-Depot VRP (DMDVRP) to reset the routes allocated to each vehicle even as the operation was in progress. In all the scenarios tested, the DMDVRP was able to produce new optimized routes, but the impact of these routes varied for each scenario. The ability of this optimization procedure to reduce field work times were verified through real-world experiments using three tractors during a rotary mowing operation. The time to complete the field work was reduced by 17.3% and the total operating time for all tractors was reduced by 11.5%. The task of a single large machine was also simulated as a task for 2 or 3 smaller machines through computer simulations. Results revealed up to 11% reduction in completion time using three smaller machines. This time reduction improved the effective field capacity

Modeling and design of low cost customizable household robot

Just as the growth of Personal computer, Mobile phones and Automobiles took place in last 3 decades, the personal robotics industry still in its nascent stage, is heading in the same direction. This thesis explores the concept of customizable household robots (CHR) in the robotics community. An attempt has been made to design a customizable robot by extending the 2 wheel differential drive kinematic model to 4 wheel independent differential drive kinematic model. A framework for CHR is developed which will be able to do various household repetitive tasks. Just as we can assemble a PC by buying its individual components, in the same way in near future we should be able to assemble a robot at home to do specific/multiple tasks. This thesis presents the kinematic modeling and 3D design of CHR agBot, agBot is a 4 wheel independently driven solar powered robot. It weighs approximately 25 lbs. It is equipped with various sensors like compass, ultrasonic, GPS, and vision. To validate the concept of customizable household robot, lawn maintenance module and security module have been implemented