Development of a mobile robotic phenotyping system for growth chamber-based studies of genotype x environment interactions

In order to fully understand the interaction between phenotype and genotype x environment to improve crop performance, a large amount of phenotypic data is needed. Studying plants of a given strain under multiple environments can greatly help to reveal their interactions. This thesis presents two key portions of the development of the Enviratron rover, a robotic system that aims to autonomously collect the labor-intensive data required to perform experiments in this area. The rover is part of a larger project which will track plant growth in multiple environments. The first aspects of the robot discussed in this thesis is the system hardware and main, or whole-chamber, imaging system. Semi-autonomous behavior is currently achieved, and the system performance in probing leaves is quantified and discussed. In contrast to existing systems, the rover can follow magnetic tape along all four directions (front, left, back, right), and uses a Microsoft Kinect V2 mounted on the end-effector of a robotic arm to position a threaded rod, simulating future sensors such as fluorimeter and Raman Spectrometer, at a desired position and orientation. Advantages of the tape following include being able to reliably move both between chambers and within a chamber regardless of dust and lighting conditions. The robot arm and Kinect system is unique in its speed at reconstructing an (filtered) environment when combined with its accuracy at positioning sensors. A comparison of using raw camera coordinates data and using KinectFusion data is presented. The results suggest that the KinectFusion pose estimation is fairly accurate, only decreasing accuracy by a few millimeters at distances of roughly 0.8 meter. The system can consistently position sensors to within 4 cm of the goal, and often within 3 cm. The system is shown to be accurate enough to position sensors to ñ 9 degrees of a desired orientation, although currently this accuracy requires human input to fully utilize the Kinect’s feedback. The second aspect of the robot presented in this thesis is a framework for generating collision-free robot arm motion within the chamber. This framework uses feedback from the Kinect sensor and is based on the Probabilistic Roadmaps (PRM) technique, which involves creating a graph of collision-free nodes and edges, and then searching for an acceptable path. The variant presented uses a dilated, down-sampled, KinectFusion as input for rapid collision checking, effectively representing the environment as a discretized grid and representing the robot arm as a collection of spheres. The approach combines many desirable characteristics of previous PRM methods and other collision-avoidance schemes, and is aimed at providing a reliable, rapidly-constructed, highly-connected roadmap which can be queried multiple times in a static environment, such as a growth chamber or a greenhouse. In a sample plant configuration with several of the most challenging practical goal poses, it is shown to create a roadmap in an average time of 32.5 seconds. One key feature is that nodes are added near the goal during each query, in order to increase accuracy at the expense of increased query time. A completed graph is searched for an optimal path connecting nodes near the starting pose and the desired end pose. The fastest graph search studied was an implementation of the A* algorithm. Queries using this framework took an average time of 0.46 seconds. The average distance between the attained pose and the desired location was 2.7 cm. Average distance C-space between the attained pose and the desired location was 3.65 degrees. The research suggests that the robotic framework presented has the potential to fulfill the main hardware and motion requirements of an autonomous indoor phenotyping robot, and can generate desired collision-free robot arm motion

Design of a Multiple-User Intelligent Feeding Robot for Elderly and Disabled

The number of elderly people around the world is growing rapidly. This has led to an increase in the number of people who are seeking assistance and adequate service either at home or in long-term- care institutions to successfully accomplish their daily activities. Responding to these needs has been a burden to the health care system in terms of labour and associated costs and has motivated research in developing alternative services using new technologies. Various intelligent, and non-intelligent, machines and robots have been developed to meet the needs of elderly and people with upper limb disabilities or dysfunctions in gaining independence in eating, which is one of the most frequent and time-consuming everyday tasks. However, in almost all cases, the proposed systems are designed only for the personal use of one individual and little effort to design a multiple-user feeding robot has been previously made. The feeding requirements of elderly in environments such as senior homes, where many elderly residents dine together at least three times per day, have not been extensively researched before. The aim of this research was to develop a machine to feed multiple elderly people based on their characteristics and feeding needs, as determined through observations at a nursing home. Observations of the elderly during meal times have revealed that almost 40% of the population was totally dependent on nurses or caregivers to be fed. Most of those remaining, suffered from hand tremors, joint pain or lack of hand muscle strength, which made utensil manipulation and coordination very difficult and the eating process both messy and lengthy. In addition, more than 43% of the elderly were very slow in eating because of chewing and swallowing problems and most of the rest were slow in scooping and directing utensils toward their mouths. Consequently, one nurse could only respond to a maximum of two diners simultaneously. In order to manage the needs of all elderly diners, they required the assistance of additional staff members. The limited time allocated for each meal and the daily progression of the seniors’ disabilities also made mealtime very challenging. Based on the caregivers’ opinion, many of the elderly in such environments can benefit from a machine capable of feeding multiple users simultaneously. Since eating is a slow procedure, the idle state of the robot during one user’s chewing and swallowing time can be allotted for feeding another person who is sitting at the same table. The observations and studies have resulted in the design of a food tray, and selection of an appropriate robot and applicable user interface. The proposed system uses a 6-DOF serial articulated robot in the center of a four-seat table along with a specifically designed food tray to feed one to four people. It employs a vision interface for food detection and recognition. Building the dynamic equations of the robotic system and simulation of the system were used to verify its dynamic behaviour before any prototyping and real-time testing

Enhanced online programming for industrial robots

The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal