Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

Deep Neural Networks for Unsupervised Robotics in Building Constructions: A Priority Area of the Fourth Industrial Revolution (4IR)

Many effective quality systems to maintain the robots’ autonomous task expansion process in construction industries for various applications over the years have yet to be well established. This study, therefore, presents a simple deep/neural network algorithm to diverse robotics tasks on building construction—bricklaying, grasping, cutting materials, and aerial robot obstacle avoidance and highlight the strengths of these algorithms in real-world robotics applications in building sites. Our findings revealed that the amount of tasks robots encountered in real-world environments is extremely challenging for existing robotic control algorithms to handle. Also, our algorithm when evaluated against other conventional learning algorithms can be a more powerful tool with the capacity to learn features directly from data, making it an excellent choice for such robotics applications in building construction. In other words, our algorithm can teach robots the ability to “work,” “think,” “know,” and “understand” their surroundings. It can also improve customer satisfaction, speed up the building process, and improve the productivity of building development teams. This chapter, however, contributes to classifications of autonomous robotics application development in construction literature. Although the problem addressed in this chapter is based on building construction, the algorithms presented are designed to be generalizable to related tasks

Enhanced vision-based localization and control for navigation of non-holonomic omnidirectional mobile robots in GPS-denied environments

New Zealand’s economy relies on primary production to a great extent, where use of the technological advances can have a significant impact on the productivity. Robotics and automation can play a key role in increasing productivity in primary sector, leading to a boost in national economy. This thesis investigates novel methodologies for design, control, and navigation of a mobile robotic platform, aimed for field service applications, specifically in agricultural environments such as orchards to automate the agricultural tasks. The design process of this robotic platform as a non-holonomic omnidirectional mobile robot, includes an innovative integrated application of CAD, CAM, CAE, and RP for development and manufacturing of the platform. Robot Operating System (ROS) is employed for the optimum embedded software system design and development to enable control, sensing, and navigation of the platform. 3D modelling and simulation of the robotic system is performed through interfacing ROS and Gazebo simulator, aiming for off-line programming, optimal control system design, and system performance analysis. Gazebo simulator provides 3D simulation of the robotic system, sensors, and control interfaces. It also enables simulation of the world environment, allowing the simulated robot to operate in a modelled environment. The model based controller for kinematic control of the non-holonomic omnidirectional platform is tested and validated through experimental results obtained from the simulated and the physical robot. The challenges of the kinematic model based controller including the mathematical and kinematic singularities are discussed and the solution to enable an optimal kinematic model based controller is presented. The kinematic singularity associated with the non-holonomic omnidirectional robots is solved using a novel fuzzy logic based approach. The proposed approach is successfully validated and tested through the simulation and experimental results. Development of a reliable localization system is aimed to enable navigation of the platform in GPS-denied environments such as orchards. For this aim, stereo visual odometry (SVO) is considered as the core of the non-GPS localization system. Challenges of SVO are introduced and the SVO accumulative drift is considered as the main challenge to overcome. SVO drift is identified in form of rotational and translational drift. Sensor fusion is employed to improve the SVO rotational drift through the integration of IMU and SVO. A novel machine learning approach is proposed to improve the SVO translational drift using Neural-Fuzzy system and RBF neural network. The machine learning system is formulated as a drift estimator for each image frame, then correction is applied at that frame to avoid the accumulation of the drift over time. The experimental results and analyses are presented to validate the effectiveness of the methodology in improving the SVO accuracy. An enhanced SVO is aimed through combination of sensor fusion and machine learning methods to improve the SVO rotational and translational drifts. Furthermore, to achieve a robust non-GPS localization system for the platform, sensor fusion of the wheel odometry and the enhanced SVO is performed to increase the accuracy of the overall system, as well as the robustness of the non-GPS localization system. The experimental results and analyses are conducted to support the methodology