Rapid development methodology of agricultural robot navigation system working in GNSS-denied environment

Robotic autonomous operating systems in global n40avigation satellite system(GNSS)-denied agricultural environments(green houses,feeding farms,and under canopy)have recently become a research hotspot.3D light detection and ranging(LiDAR)locates the robot depending on environment and has become a popular perception sensor to navigate agricultural robots.A rapid development methodology of a 3D LiDAR-based navigation system for agricultural robots is proposed in this study,which includes:(i)individual plant clustering and its location estimation method(improved Euclidean clustering algorithm);(ii)robot path planning and tracking control method(Lyapunov direct method);(iii)construction of a robot-LiDAR-plant unified virtual simulation environment(combination use of Gazebo and SolidWorks);and(vi)evaluating the accuracy of the navigation system(triple evaluation:virtual simulation test,physical simulation test,and field test).Applying the proposed methodology,a navigation system for a grape field operation robot has been developed.The virtual simulation test,physical simulation test with GNSS as ground truth,and field test with path tracer showed that the robot could travel along the planned path quickly and smoothly.The maximum and mean absolute errors of path tracking are 2.72 cm,1.02 cm;3.12 cm,1.31 cm,respectively,which meet the accuracy requirements of field operations,establishing the effectiveness of the proposed methodology.The proposed methodology has good scalability and can be implemented in a wide variety of field robot,which is promising to shorten the development cycle of agricultural robot navigation system working in GNSS-denied environment.

Intelligent Autonomous-Robot Control for Medical Applications

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic.This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected patients.The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic.The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic robots.Health officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent robots.We propose an advanced controller for a service robot to be used in hospitals.This type of robot is deployed to deliver food and dispense medications to individual patients.An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction.These criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

Navigation sensors and systems in GNSS degraded and denied environments

Position, velocity, and timing（PVT） signals from the Global Positioning System（GPS）are used throughout the world but the availability and reliability of these signals in all environments has become a subject of concern for both civilian and military applications. This presentation summarizes recent advances in navigation sensor technology, including GPS, inertial, and other navigation aids that address these concerns. Also addressed are developments in sensor integration technology with several examples described, including the Bluefin-21 system mechanization.

Investigate the energy efficiency operation model for bulk carriers based on Simulink/Matlab

The world is facing great challenges regarding the energy crisis and environmental pollution.One of the main challenges is the increasing number of ships with a lot of different types and sizes.In this research,it is essential to analyze the operational energy efficiency of sea-going ships since the previous studies have concentrated on the inland river ships.This working is carried out by analyzing of the energy efficiency operation for large size ships such as bulk carriers,container ships,etc.based on the resistance characteristics of different navigation environment factors.A numerical model of main engine energy efficiency operation was considered using the energy efficiency measure of IMO(International Maritime Organization),in particular using the Energy Efficiency Operational Indicator(EEOI)as a monitoring tool in this research.This EEOI numerical model was established and simulated through Simulink/Matlab and verified by the collected data from a certain vessel in Vietnam.The EEOI model was simulated under the different navigation environment conditions including various engine speed,wind speed,wave height,and water speed.After that,the simulation results of this research will be compared with experimental database of a certain bulk carrier namely M/V NSU JUSTICE 250,000 DWT.The results indicated that it is important to consider the main engine speed appropriately in ship operation in order to save energy onboard and reduce the greenhouse gas emission problem.