The development and evaluation of computer vision algorithms for the control of an autonomous horticultural vehicle

Economic and environmental pressures have led to a demand for reduced chemical use in crop production. In response to this, precision agriculture techniques have been developed that aim to increase the efficiency of farming operations by more targeted application of chemical treatment. The concept of plant scale husbandry (PSH) has emerged as the logical extreme of precision techniques, where crop and weed plants are treated on an individual basis. To investigate the feasibility of PSH, an autonomous horticultural vehicle has been developed at the Silsoe Research Institute. This thesis describes the development of computer vision algorithms for the experimental vehicle which aim to aid navigation in the field and also allow differential treatment of crop and weed. The algorithm, based upon an extended Kalman filter, exploits the semi-structured nature of the field environment in which the vehicle operates, namely the grid pattern formed by the crop planting. By tracking this grid pattern in the images captured by the vehicles camera as it traverses the field, it is possible to extract information to aid vehicle navigation, such as bearing and offset from the grid of plants. The grid structure can also act as a cue for crop/weed discrimination on the basis of plant position on the ground plane. In addition to tracking the grid pattern, the Kalman filter also estimates the mean distances between the rows of lines and plants in the grid, to cater for variations in the planting procedure. Experiments are described which test the localisation accuracy of the algorithms in offline trials with data captured from the vehicle's camera, and on-line in both a simplified testbed environment and the field. It is found that the algorithms allow safe navigation along the rows of crop. Further experiments demonstrate the crop/weed discrimination performance of the algorithm, both off-line and on-line in a crop treatment experiment performed in the field where all of the crop plants are correctly targeted and no weeds are mistakenly treated

Feasibility study of an Integrated Program for Aerospace vehicle Design (IPAD). Volume 3: Support of the design process

The user requirements for computer support of the IPAD design process are identified. The user-system interface, language, equipment, and computational requirements are considered

Feasibility study of an Integrated Program for Aerospace vehicle Design (IPAD). Volume 2: The design process

The extent to which IPAD is to support the design process is identified. Case studies of representative aerospace products were developed as models to characterize the design process and to provide design requirements for the IPAD computing system

Feasibility study of an Integrated Program for Aerospace vehicle Design (IPAD). Volume 5: Catalog of IPAD technical program elements

The catalog is presented of technical program elements which are required to support the design activities for a subsonic and supersonic commercial transport. Information for each element consists of usage and storage information, ownership, status and an abstract describing the purpose of the element

In-beam internal conversion electron spectroscopy with the SPICE detector

The SPectrometer for Internal Conversion Electrons (SPICE) has been commissioned for use in conjunction with the TIGRESS $\gamma$-ray spectrometer at TRIUMF's ISAC-II facility. SPICE features a permanent rare-earth magnetic lens to collect and direct internal conversion electrons emitted from nuclear reactions to a thick, highly segmented, lithium-drifted silicon detector. This arrangement, combined with TIGRESS, enables in-beam $\gamma$-ray and internal conversion electron spectroscopy to be performed with stable and radioactive ion beams. Technical aspects of the device, capabilities, and initial performance are presented

A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. Our software framework provides the methodology and the tools that enable robots to exhibit deliberative and reactive behaviors in autonomous operation, to be reprogrammed by a human operator at run-time, and to learn and adapt to unstructured, dynamic environments and new tasks, while providing performance guarantees. We demonstrate the algorithms and software on an experimental testbed that involves a team of car-like robots using a single omnidirectional camera as a sensor without explicit use of odometry