Wi-Fi and Satellite-Based Location Techniques for Intelligent Agricultural Machinery Controlled by a Human Operator

In the new agricultural scenarios, the interaction between autonomous tractors and a human operator is important when they jointly perform a task. Obtaining and exchanging accurate localization information between autonomous tractors and the human operator, working as a team, is a critical to maintaining safety, synchronization, and efficiency during the execution of a mission. An advanced localization system for both entities involved in the joint work, i.e., the autonomous tractors and the human operator, provides a basis for meeting the task requirements. In this paper, different localization techniques for a human operator and an autonomous tractor in a field environment were tested. First, we compared the localization performances of two global navigation satellite systems’ (GNSS) receivers carried by the human operator: (1) an internal GNSS receiver built into a handheld device; and (2) an external DGNSS receiver with centimeter-level accuracy. To investigate autonomous tractor localization, a real-time kinematic (RTK)-based localization system installed on autonomous tractor developed for agricultural applications was evaluated. Finally, a hybrid localization approach, which combines distance estimates obtained using a wireless scheme with the position of an autonomous tractor obtained using an RTK-GNSS system, is proposed. The hybrid solution is intended for user localization in unstructured environments in which the GNSS signal is obstructed. The hybrid localization approach has two components: (1) a localization algorithm based on the received signal strength indication (RSSI) from the wireless environment; and (2) the acquisition of the tractor RTK coordinates when the human operator is near the tractor. In five RSSI tests, the best result achieved was an average localization error of 4 m. In tests of real-time position correction between rows, RMS error of 2.4 cm demonstrated that the passes were straight, as was desired for the autonomous tractor. From these preliminary results, future work will address the use of autonomous tractor localization in the hybrid localization approach

Innovative airplane ground handling system for green operations

The aim of this work is to develop a new concept of taxiing, in order to reduce the pollution in terms of noise and gas emission and to introduce a higher level of safety during ground operations. In the area close to the airport gates, the airplane ground handlings are currently performed through the airplane engines, which have the task of providing the trust necessary to move the airplane to the runway. Pollutant emissions and the noise level near the gates, however, could be drastically reduced by introducing an innovative autonomous tractor called CHAT (Clean Hydrogen Autonomous Tractor), developed from the standard pushback tractor. The ground operations could be basically modified by extending the time in which the airplane engines are idle and the airplane is towed by the tractors powered by renewable energy

Field Obstacle Identification for Autonomous Tractor Applications

New technologies are being developed to meet the growing demand for agricultural products. Autonomous tractors are one of the many solutions to address this demand. Obstacle detection and avoidance is an important consideration for safe operation of any autonomous machine. Three field obstacles were chosen to be identified in this thesis work: tractors, round bales, and center pivots. Limited research work was found on the identification of center pivot detection. Feasibility of using low cost LIDARs was considered for the detection of tractors, bales, and agricultural center pivots. Performance of LIDARs in different lighting conditions, different colors of obstacles, accuracy and angular resolution was evaluated. It was found that low cost LIDARs do not have a small enough angular resolution to detect pivots at a distance to avoid the obstacle. Formulas were derived to help find the distance between steps of the LIDAR. Obstacle identification is also important so that proper corrective actions can be taken to avoid the obstacle. RGB cameras were used to aid in the detection of center pivots. SURF Feature Extraction and Matching, Viola-Jones algorithm and edge detection with a shape identification algorithm were tried but none of the algorithms could adapt to more than one orientation or class of object. Obstacle identification using Convolutional Neural Networks (CNNs) for obstacle detection was pursued. Each obstacle was individually trained first and then all classes were combined to create one object detector. Faster Region based CNN (R-CNN) was used with GoogLeNet to give high mean Average Precision (mAP). Advisor: Santosh Pitl

Innovative airplane ground handling system for green operations

The aim of this work is to develop a new concept of taxiing, in order to reduce the pollution in terms of noise and gas emission and to introduce a higher level of safety during ground operations. In the area close to the airport gates, the airplane ground handlings are currently performed through the airplane engines, which have the task of providing the trust necessary to move the airplane to the runway. Pollutant emissions and the noise level near the gates, however, could be drastically reduced by introducing an innovative autonomous tractor called CHAT (Clean Hydrogen Autonomous Tractor), developed from the standard pushback tractor. The ground operations could be basically modified by extending the time in which the airplane engines are idle and the airplane is towed by the tractors powered by renewable energy

Do Engineering Students Learn Ethics From an Ethics Course?

The goal of the present research is to develop machine-assisted methods that can assist in the analysis of students’ written compositions in ethics courses. As part of this research, we analyzed Social Impact Assessment (SIA) papers submitted by engineering undergraduates in a course on engineering ethics. The SIA papers required students to identify and discuss a contemporary engineering technology (e.g., autonomous tractor trailers) and to explicitly discuss the ethical issues involved in that technology. Here we describe the ability of three machine tools to discriminate differences in the technical compared to ethical portions of the SIA papers. First, using LIWC (Language Inquiry and Word Count) we quantified differences in analytical thinking, expertise and self-confidence, disclosure, and affect, in the technical and ethical portions of the papers. Next, we applied MEH (Meaning Extraction Helper) to examine differences in critical concepts in the technical and ethical portions of the papers. Finally, we used LDA (Latent Dirichlet Allocation) to examine differences in the topics in the technical and ethical portions of the papers. The results of these three tests demonstrate the ability of machine-based tools to discriminate conceptual, affective, and motivational differences in the texts that students compose that relate to engineering technology and to engineering ethics. We discuss the utility and future directions for this research.Cockrell School of Engineerin

Fuzzy logic control for energy saving in autonomous electric vehicles

Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested

Comparison of positional accuracy between RTK and RTX GNSS gased on the autonomous agricultural vehicles under field conditions

Currently, many systems (machine vision, high resolution remote sensing, global positioning systems, and odometry techniques) have been integrated into agricultural e quipment to increase the efficiency, productivity, and safety of the individual in all field activities. This study focused upon assessing a satellite-based localization solution used in straight path guidance of an autonomou s vehicle developed for ag ricultural applica tions. The autonom ous agricultural vehicle was designed and constructed under RHEA (Robot fleets for highly effective agriculture and forestry management) project and is part of a three-unit fleet of similar vehicles. Static tests showed that 99% of all positions are placed within a circle with a 2.9 cm radius centered at the geo-position usi ng real-time satellite corrections (RTX). Dynamic tests between rows demonstrated a mean (N=610) of the standard deviation for real-time base station corrections (RTK) of 1.43 cm and for real-time satellite corrections (RTX) of 2.55 cm. These re sults demonstrate that the tractor was able to track each straight line with high degree of accuracy. The integration of a Global Navigation Satellite System (GNSS) with sensors (e.g., inertial sensor, altimeters, odomet ers, etc.) within the vehicle showed th e potential of autonomous tractors for expanding agricultural applications utilizing this technology.European Union FP7/2007-201

EXPERIMENT STUDY OF AUTONOMOUS SYSTEM FOR INTRA-ROW WEED CONTROL

The study is used to propose and test by experiment a procedure for autonomous hoeing system for intra-row weed control. The proposed system utilizes the RTK-GPS navigation system. The system consists of autonomous vehicle equipped with side-shifting frame and cycloid hoe. The navigation of the system is controlled using a pre-specified plan and implemented in the system internal computer. The internal computer is also attached to a field station using the wireless local area network (WLAN). The performance of the system was measured through an experiment consisted of making rows having small plants and soil conditions similar to the actual field. The results based on chi-square shows that transverse deviation had normal distribution indicating the performance of the side-shift control. The results related to the longitudinal deviation distance between plants and the nearest line trajectories showed good chi-square fit which is an indication of performance of the cycloid hoe control. The result shows that the system is promising and can be used at larger level with suitable adjustment