International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The aim of the paper was to test of the concept of the navigation system for the autonomous robot for sowing and wide row planting. Autonomous work of the robot in the field of traction and agronomic processes is implemented based on data from many sensors (cameras, position sensors, distance sensors, and others). The robot is intended for ecologic cultivation requiring mechanical removal of weeds or in crops with application of selective liquid agrochemicals limited to the minimum. The use of a vision system, based on the map coordinates of the position of the sown seeds, allows for their care on an early stage of plant development. Main sensor system is based on a specialized GPS receiver and inertial navigation providing position information with an accuracy of around 10 mm. To determine the angular acceleration the IMU (Inertial Measurement Unit) is used. Additionally, information from the acceleration sensors and wheel encoders is used for navigation purposes. This system is used to: control the speed of the robot, keep the robot on the designated path, and detect the precise position of the seeds. The exact information of the seeds position is used to build maps of seeds, which will be used as supporting information for precision weeding, and to control the position of and operation of key components. The front camera view is used to increase positioning accuracy of the robot. It will allow corrections of the robot path regarding the rows of plants. The vision system is also used for detection of non-moving objects. A structure of requirements for the SQL database has been developed, which is used to store plant and weed geo-data, as well as store data about plants and weeds, based on images recorded by the vision system.

In Industrial Institute of Agriculture Engineering (Poznań, Poland), together with the Institute of Vehicle of Warsaw Technical University and firm Promar Poznań action have been taken to design autonomous field robot for sowing and cultivation of crops. Designed robot is to be autonomous device. It will automatically perform the sowing, weeding and selective spraying plant crops such as sugar beet or maize. The robot will work in various terrain and weather conditions. It will move on the dirt roads and on cultivated fields, and therefore it will have to overcome mud, sand, puddles and avoid obstacles such as ruts, bumps in the road and stones. The achievement of this objective required the development of a suitable chassis, system of autonomous control in terms of driving and realization of agronomic processes.

The aim of the paper was to test of the concept of the navigation system for the autonomous robot for sowing and wide row planting. Autonomous work of the robot in the field of traction and agronomic processes is implemented based on data from many sensors (cameras, position sensors, distance sensors, and others). The robot is intended for ecologic cultivation requiring mechanical removal of weeds or in crops with application of selective liquid agrochemicals limited to the minimum. The use of a vision system, based on the map coordinates of the position of the sown seeds, allows for their care on an early stage of plant development. Main sensor system is based on a specialized GPS receiver and inertial navigation providing position information with an accuracy of around 10 mm. To determine the angular acceleration the IMU (Inertial Measurement Unit) is used. Additionally, information from the acceleration sensors and wheel encoders is used for navigation purposes. This system is used to: control the speed of the robot, keep the robot on the designated path, and detect the precise position of the seeds. The exact information of the seeds position is used to build maps of seeds, which will be used as supporting information for precision weeding, and to control the position of and operation of key components. The front camera view is used to increase positioning accuracy of the robot. It will allow corrections of the robot path regarding the rows of plants. The vision system is also used for detection of non-moving objects. A structure of requirements for the SQL database has been developed, which is used to store plant and weed geo-data, as well as store data about plants and weeds, based on images recorded by the vision system.

The paper presents possibility of fault detection and isolation in rotation machinery using analytical redundancy. It outlines the most important techniques of model-based residual generation using parameter identification and state estimation methods with emphasis the problems of reliability. A solution to the fundamental problem of fault detection providing the maximum achievable effectiveness by using condition-based maintenance system, reducing downtime, decreasing maintenance cost, and increasing machine availability is given. With the aim of synthesizing and providing the information of researcher`s community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of remain useful life prognosis.

The results are very important for robust instrument fault detection, component fault detection and actuator fault detection. Finally we discuss the approach of fault diagnosis using a combination of analytical and knowledge-based redundancy.

The aim of the paper was to propose conception of the navigation system for the autonomous robot for sowing and wide row planting. Autonomous work of the robot in range of traction and agronomic processes will be implemented on the basis of data from a many sensors (cameras, sensors position, sensors distance, and others). Positive test results will allow for the use of the robot in organic crops requiring mechanical removal of weeds or in crops with application of selective liquid agrochemicals limited to the minimum The use of a vision system, based on the map coordinates of the position of the sown seeds , will allow for their care on an early stage of plant development. Main sensor system is based on a specialized GPS receiver providing position information with an accuracy of less than 100 mm. This system will be used to: control speed of the robot, guidance and maintenance robot on the designated path, precision seeding – the exact information on where sowing the seeds will be used to build maps of seeds, which will be used as supporting information for precision weeding, and to control the position of and operation of key components. The front camera view will be used to increase positioning accuracy of the robot. It will allow corrections of the robot path regarding the rows of plants. The vision system will also be used for detection of non-moving objects. Additionally information from the acceleration sensors and encoders built-in wheels will be used in navigation purposes. To determine the angular acceleration the IMU (Inertial Measurement Unit) will be required. During the preliminary phase of the project Authors are planning to test possibility of usage of several low cost sensors for collision avoidance system (moving objects detection).

The aim of the paper was to propose conception of autonomous robot for sowing and wide row planting. Autonomous work of the robot in range of traction and agronomic processes will be implemented on the basis of data from a many sensors (cameras, sensors position, sensors distance, and others). Positive test results will allow for the use of the robot in organic crops requiring mechanical removal of weeds or in crops with application of selective liquid agrochemicals limited to the minimum The use of a vision system, based on the map coordinates of the position of the sown seeds , will allow for their care on an early stage of plant development. The applicability of the robot to onerous work in organic farming may encourage farmers to discontinue the use of herbicides in crops include sugar beet, corn, etc.