An autonomous machine is a machine that can navigate through its environment without human interactions. These machines use sensors to sense the environment and have computing abilities for receiving and interpreting the sensory data as well as for controlling their displacement. At the University of Saskatchewan (Saskatoon, Canada), a low level autonomous machine was developed. This low level machine was the sensor system for an autonomous machine. The machine was capable of sensing the environment and carrying out actions based on commands sent to it. This machine provided a sensing and control layer, but the path planning (decision making) part of the autonomous machine was not developed.<p>This autonomous machine was developed on a Case IH DX 34H tractor with the purpose of providing a machine for testing software and sensors in a true agricultural environment. The tractor was equipped with sensors capable of sensing the speed and heading of the tractor. A control architecture was developed that received input commands from a human or computer in the form of a target heading and speed. The control architecture then adjusted controls on the tractor to make the tractor reach and maintain the target heading and speed until a new command was provided. The tractor was capable of being used in all kinds of weather, although some minor issues arose when testing in rain and snow. The sensor platform developed was found to be insufficient for proper control. The control structure appeared to work correctly, but was hindered by the poor sensor platform performance.

An autonomous machine is a machine that can navigate through its environment without human interactions. These machines use sensors to sense the environment and have computing abilities for receiving and interpreting the sensory data as well as for controlling their displacement. At the University of Saskatchewan (Saskatoon, Canada), a low level autonomous machine was developed. This low level machine was the sensor system for an autonomous machine. The machine was capable of sensing the environment and carrying out actions based on commands sent to it. This machine provided a sensing and control layer, but the path planning (decision making) part of the autonomous machine was not developed.<p>This autonomous machine was developed on a Case IH DX 34H tractor with the purpose of providing a machine for testing software and sensors in a true agricultural environment. The tractor was equipped with sensors capable of sensing the speed and heading of the tractor. A control architecture was developed that received input commands from a human or computer in the form of a target heading and speed. The control architecture then adjusted controls on the tractor to make the tractor reach and maintain the target heading and speed until a new command was provided. The tractor was capable of being used in all kinds of weather, although some minor issues arose when testing in rain and snow. The sensor platform developed was found to be insufficient for proper control. The control structure appeared to work correctly, but was hindered by the poor sensor platform performance.

A method of estimating the relative position and orientation of a known target satellite is presented, using only passive imagery. Such a method is intended as a prelude to a system required in future autonomous satellite docking missions. Using a single monocular image, and utilising knowledge of the target spacecraft, estimation of the target's six relative rotation and translation parameters with respect to the camera are found. Pose estimation is divided into modular sections. Each frame is processed to detect the major lines in the image, and correspondence information between detected lines and a-priori target information is estimated, resulting in a list of line-to-model correspondences. This correspondence information is used to estimate the pose of the target required to produce such a correspondence list. Multiple possible pose estimates are generated and tested, where each estimate contains the three rotation and translation parameters. The best estimates go through to the least-squares minimisation phase, which reduces estimation error and provides statistical information for multi-frame filtering. The final estimate vector and covariance matrix is the end result for each frame. Estimates of the target location over time allow the relative orbit parameters of the target to be estimated. Location estimates are filtered to fit an orbit model based on Hill's Equations, and statistical information gathered with each estimate is including in the filter process when estimating the orbit parameters. These orbit parameters allow prediction of the target location with time, which will enable mission planning and safety analysis of potential orbit manoeuvres in close proximity to the target. Testing is carried out by a detailed simulation system, which renders accurate images of the target satellite given the true pose of the target with respect to the inertial reference frame. The rendering software used takes into account lighting conditions, reflections, shadowing, specularity, and other considerations, and further post-processing is involved to produce a realistic image. Target position over time is modelled on orbit dynamics with respect to a defined inertial frame. Transformation between inertial, target, and camera frames of reference are dealt with, to transform a rotating target in the inertial frame to the apparent rotation in the camera frame.

Limited water exchange shrimp culture technology is commonly used today in many shrimp farms in Vietnam to reduce water usage, input of diseases and discharge of nutrient-rich effluents into environment as well as to increase the production per unit area. However, a remaining problem in this technology is that the water quality in shrimp ponds will be reduced due to limitation of water exchange for a long period. The accumulation of inorganic components such as waste feed, bacterial deposits or other biological debris at the pond bottom will lead to low dissolved oxygen, high ammonia-nitrogen level, high fecal coliform bacteria and high turbidity which cause a severe degradation of water quality and detriment to shrimp growth and survival. To solve this remaining problem, an autonomous water-cleaning machine for shrimp ponds was designed to control the waste accumulation in the pond. This is an effective solution to replace manual cleaning methods for water quality management in shrimp farming in the coastal area of the Mekong delta of Vietnam. Especially, this technique can be used for biosecure shrimp production systems according to GMP standards to meet the objectives for sustainable development of shrimp aquaculture in Vietnam. / Kỹ thuật nuôi tôm không thay nước đang được sử dụng rộng rãi ở các trại nuôi tôm Việt Nam vì giúp giảm lượng nước sử dụng, hạn chế thải nước vào môi trường và giúp tăng diện tích nuôi trồng tôm. Tuy nhiên, bản thân kỹ thuật này cũng tạo nên một sản phẩm chất thải là phân tôm, thức ăn và chế phẩm sinh học xử lý nước dư thừa. Chất thải này dần dần tích tụ dưới đáy ao tạo thành lớp bùn độc, rất thiếu ôxy và chứa nhiều chất gây hại như ammonia, nitrite, hydrogen sulfide. Để tránh làm giảm diện tích ao nuôi do chất thải tích tụ làm tôm lảng tránh và tăng mật độ tôm nuôi trồng, thiết bị tự hành thu gom chất thải làm sạch nước trong vuông nuôi tôm đã được nghiên cứu, thiết kế và chế tạo thành công. Thiết bị đã thay thế các hoạt động làm sạch chất thải thủ công của con người, không sử dụng các nguồn năng lượng gây ô nhiễm, giảm nhu cầu về điện góp phần phát triển sản xuất tôm sạch đạt chuẩn GMP và phát triển bền vững ở các tỉnh ven biển miền Tây Việt Nam.

Limited water exchange shrimp culture technology is commonly used today in many shrimp farms in Vietnam to reduce water usage, input of diseases and discharge of nutrient-rich effluents into environment as well as to increase the production per unit area. However, a remaining problem in this technology is that the water quality in shrimp ponds will be reduced due to limitation of water exchange for a long period. The accumulation of inorganic components such as waste feed, bacterial deposits or other biological debris at the pond bottom will lead to low dissolved oxygen, high ammonia-nitrogen level, high fecal coliform bacteria and high turbidity which cause a severe degradation of water quality and detriment to shrimp growth and survival. To solve this remaining problem, an autonomous water-cleaning machine for shrimp ponds was designed to control the waste accumulation in the pond. This is an effective solution to replace manual cleaning methods for water quality management in shrimp farming in the coastal area of the Mekong delta of Vietnam. Especially, this technique can be used for biosecure shrimp production systems according to GMP standards to meet the objectives for sustainable development of shrimp aquaculture in Vietnam. / Kỹ thuật nuôi tôm không thay nước đang được sử dụng rộng rãi ở các trại nuôi tôm Việt Nam vì giúp giảm lượng nước sử dụng, hạn chế thải nước vào môi trường và giúp tăng diện tích nuôi trồng tôm. Tuy nhiên, bản thân kỹ thuật này cũng tạo nên một sản phẩm chất thải là phân tôm, thức ăn và chế phẩm sinh học xử lý nước dư thừa. Chất thải này dần dần tích tụ dưới đáy ao tạo thành lớp bùn độc, rất thiếu ôxy và chứa nhiều chất gây hại như ammonia, nitrite, hydrogen sulfide. Để tránh làm giảm diện tích ao nuôi do chất thải tích tụ làm tôm lảng tránh và tăng mật độ tôm nuôi trồng, thiết bị tự hành thu gom chất thải làm sạch nước trong vuông nuôi tôm đã được nghiên cứu, thiết kế và chế tạo thành công. Thiết bị đã thay thế các hoạt động làm sạch chất thải thủ công của con người, không sử dụng các nguồn năng lượng gây ô nhiễm, giảm nhu cầu về điện góp phần phát triển sản xuất tôm sạch đạt chuẩn GMP và phát triển bền vững ở các tỉnh ven biển miền Tây Việt Nam.