Automatic Navigation for Rat-Robots with Modeling of the Human Guidance

A bio-robot system refers to an animal equipped with Brain-Computer Interface （BCI）, through which the outer stimulation is delivered directly into the animal＇s brain to control its behaviors. The development ofbio-robots suffers from the dependency on real-time guidance by human operators. Because of its inherent difficulties, there is no feasible method for automatic con- trolling of bio-robots yet. In this paper, we propose a new method to realize the automatic navigation for bio-robots. A General Regression Neural Network （GRNN） is adopted to analyze and model the controlling procedure of human operations. Com- paring to the traditional approaches with explicit controlling rules, our algorithm learns the controlling process and imitates the decision-making of human-beings to steer the rat-robot automatically. In real-time navigation experiments, our method suc- cessfully controls bio-robots to follow given paths automatically and precisely. This work would be significant for future ap- plications of bio-robots and provide a new way to realize hybrid intelligent systems with artificial intelligence and natural biological intelligence combined together.

Development of a low-cost GPS/INS integrated system for tractor automatic navigation

The use of low-cost single GPS receivers and inertial sensors for auto-guidance applications has been limited by their reduced accuracy and signal drift over time compared to real-time kinematic(RTK)differential GPS units and fiber-optic gyroscope(FOG)sensors.In this study,a prototype low-cost GPS/INS integrated system consisting of a triangle-shaped array of three Garmin 19x GPS receivers and an Xsens inertial measurement unit(IMU)to improve the accuracy of position and heading angle measured with a single GPS receiver was developed.A triangular algorithm that uses data collected from the three single GPSs mounted on the angular points of a triangular frame was designed.A sensor fusion algorithm based on the Kalman filter combining the GPS and IMU data was developed by integrating position data and heading angles of a triangular array of GPS receivers.The optimized values of two noise covariance matrixes(Q and R)for the Kalman filtering were determined using the Central Composite Design(CCD)method.As compared to the use of a single Garmin GPS receiver,use of the developed GPS/INS system showed improved accuracy performance in terms of both position and heading angle,with reductions in root mean square errors(RMSEs)from 2.7 m to 0.64 m for position and from 8.9ºto 2.1ºfor heading angle.The accuracy improvements show new potential for agricultural auto-guidance applications.

Development of the automatic navigation system for combine harvester based on GNSS

An automatic navigation system was developed to realize automatic driving for combine harvester,including the mechanical design,control method and software design.First of all,for the harvester modified with the automatic navigation system,a dynamic calibration method of the rear wheel center position was proposed.The control part included the navigation controller and the steering controller.A variable universe fuzzy controller was designed to the navigation controller,which used fuzzy control to change the fuzzy universe of input and output dynamically,that means,under the condition that the fuzzy rules remain unchanged,the fuzzy universe changes with the change of input,which is an adaptive fuzzy control method and can modify the control strategy in time.To realize the automatic navigation of the harvester,the decision result of the navigation controller based on the variable universe fuzzy control was input into the steering controller,and then the electric steering wheel was controlled to rotate.To test the performance of the designed automatic navigation system,the field experiment was carried out.When the combine harvester was navigating linearly at a speed of 0.8 m/s,the overall root mean square error(RMSE)of the lateral deviation was 5.87 cm.The test results showed that the system was designed could make the combine track the preset path smoothly and stably,and the tracking accuracy was at the centimeter level.

Development of the electric automatic steering system for agricultural vehicles

Automatic guidance of agricultural vehicles requires automatic execution of operation commands received from the navigation controller by using electronically controlled mechanisms for wheel steering,speed changing and work implementing.Automatic steering contributes as a prerequisite technique in automatic and semi-automatic agricultural navigation.This research aimed to develop an electric automatic steering system that was compact in its structure and integrated into original steering mechanism in a simply and convenient way for aftermarket modification.A brushless motor and reducer assembly was utilized to provide an adequate steering torque instead of manual maneuver.A rapid assembling approach was proposed by passing the steering shaft through the hollow output shaft.A digital proportional-integral-differential(PID)algorithm was implemented to calculate the rotation speeds and directions by comparing the desired angle and the actual angle,which was implemented in a printed circuit board with a microcontroller unit(MCU)and interface chips.An unmanned wheeled tractor was applied as test platform to integrate the newly developed electric automatic steering system.Tests were conducted to evaluate its performance in terms of stability and responsiveness.An autonomous navigation system guided the tractor along target paths in the field by sending steering commands to the electric automatic steering system.The results show that the steering angle error was less than 0.81°when desired steering angle was less than 10°.The lateral error difference was no more than 4.76 cm when repeating following the same target path,which indicated that the electric automatic steering system responded accurately and robustly to steering commands.

Development of a tomato harvesting robot used in greenhouse

A tomato harvesting robot was developed in this study,which consisted of a four-wheel independent steering system,a 5-DOF harvesting system,a navigation system,and a binocular stereo vision system.The four-wheel independent steering system was capable of providing a low-speed steering control of the robot based on Ackerman steering geometry.The proportional-integral-derivative(PID)algorithm was used in the laser navigation control system.The Otsu algorithm and the elliptic template method were used for the automatic recognition of ripe tomatoes,and obstacle avoidance strategies were proposed based on the C-space method.The maximum average absolute error between the set angle and the actual angle was about 0.14°,and the maximum standard deviation was about 0.04°.The laser navigation system was able to rapidly and accurately track the path,with the deviation being less than 8 cm.The load bearing capacity of the mechanical arm was about 1.5 kg.The success rate of the binocular vision system in the recognition of ripe tomatoes was 99.3%.When the distance was less than 600 mm,the positioning error was less than 10 mm.The time needed for recognition of ripe tomatoes and pitching was about 15 s per tomato,with a success rate of about 86%.This study provides some insights into the development and application of tomato harvesting robot used in the greenhouse.