Study on Predictive Control for Trajectory Tracking of Robotic Manipulator

Model predictive control (MPC) differs from other control methods mainly in implementation of the control actions. In this paper, the tracking error performance index of rolling optimization of predictive control is designed for a class of nonlinear systems by the relative degree, then employed to design controller for robot manipulator. In other words, the robot manipulator’s reference trajectory is based on Taylor expansion, and the actual trajectory is substituted into the performance cost function which is derivated for minimum to obtain the controller. Then the performance analysis for the closed system is made. Simulation results demonstrate the effectiveness of the method based on Taylor expansion

Discussion on Different Controllers Used for the Navigation of Mobile Robot

Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR\u27s can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Dynamics-Guided Diffusion Model for Robot Manipulator Design

We present Dynamics-Guided Diffusion Model, a data-driven framework for generating manipulator geometry designs for a given manipulation task. Instead of training different design models for each task, our approach employs a learned dynamics network shared across tasks. For a new manipulation task, we first decompose it into a collection of individual motion targets which we call target interaction profile, where each individual motion can be modeled by the shared dynamics network. The design objective constructed from the target and predicted interaction profiles provides a gradient to guide the refinement of finger geometry for the task. This refinement process is executed as a classifier-guided diffusion process, where the design objective acts as the classifier guidance. We evaluate our framework on various manipulation tasks, under the sensor-less setting using only an open-loop parallel jaw motion. Our generated designs outperform optimization-based and unguided diffusion baselines relatively by 31.5% and 45.3% on average manipulation success rate. With the ability to generate a design within 0.8 seconds, our framework could facilitate rapid design iteration and enhance the adoption of data-driven approaches for robotic mechanism design

Fuzzy Model-Reference Adaptive Control Method For An Underwater Robotic Manipulator

Pengendali robotik dalam air (URM) adalah berbeza jika dibandingkan dengan pengendali robotik biasa atau yg berada di permukaan. Dinamiknya mempunyai ketidakpastian yang besar bergantung kepada daya apungan, daya yang dihasilkan oleh jisim tambahan/momen luas kedua dan daya geseran. Tambahan lagi, ia juga dipengaruhi oleh gangguan luaran yang penting seperti arus dan ombak. The underwater robotic manipulators (URMs) are different with the ordinary or landbased robotic manipulators. Its dynamics have large uncertainties owing to the buoyancy, force induced by the added mass/moment of inertia and the drag force. Moreover, they are also affected by the crucial external disturbances such as currents and waves

Resource-efficient path-following control for a self-driving car in a networked control system

[EN] In recent years, in-vehicle networks are increasingly being incorporated to self-driving cars in order to interconnect spatially distributed devices such as sensors, actuators, and controllers, leading to networked control systems (NCS). The main aim of this work is to reduce the use of resources in a NCS (bandwidth, device batteries) while maintaining an accurate path following for a self-driving car. Some typical network-induced drawbacks such as time-varying delays, packet dropouts and packet disorder will also be coped with. In order to reach the goals, a systematic integration of periodic event-triggered sampling techniques, packet-based control strategies, and state estimation methods is proposed. A novel non-uniform dual-rate extended Kalman filter (NUDREKF) is formulated to estimate the system state at fast, control rate from scarce slow-rate measurements. Due to its mathematical simplicity and low computational cost, the dynamic control law is designed from an inverse kinematic bicycle model and a proportional feedforward controller. Interestingly, optimal parameters for the event-triggered conditions are reached, leading to a satisfactory trade-off between resource savings and control performance. Simulation results for a real trajectory considering actual limitations for the actuators reveal the benefits of the control proposal compared to a conventional control approach.Alite, G.; Cuenca, Á.; Salt Llobregat, JJ.; Tomizuka, M. (2023). Resource-efficient path-following control for a self-driving car in a networked control system. IEEE Access. 11:108011-108023. https://doi.org/10.1109/ACCESS.2023.33212691080111080231

\u3cem\u3eGRASP News\u3c/em\u3e: Volume 9, Number 1

The past year at the GRASP Lab has been an exciting and productive period. As always, innovation and technical advancement arising from past research has lead to unexpected questions and fertile areas for new research. New robots, new mobile platforms, new sensors and cameras, and new personnel have all contributed to the breathtaking pace of the change. Perhaps the most significant change is the trend towards multi-disciplinary projects, most notable the multi-agent project (see inside for details on this, and all the other new and on-going projects). This issue of GRASP News covers the developments for the year 1992 and the first quarter of 1993

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area