An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

A Fuzzy Logic Architecture for Rehabilitation Robotic Systems

Robots are highly incorporated in rehabilitation in the last decade to compensate lost functions in disabled individuals. By controlling the rehabilitation robots from far, many benefits are achieved. These benefits include but not restricted to minimum hospital stays, decreasing cost, and increasing the level of care. The main goal of this work is to have an effective solution to take care of patients from far. Tackling the problem of the remote control of rehabilitation robots is undergoing and highly challenging. In this paper, a remote wrist rehabilitation system is presented. The developed system is a sophisticated robot ensuring the two wrist movements (Flexion /extension and abduction/adduction). Additionally, the proposed system provides a software interface enabling the physiotherapists to control the rehabilitation process remotely. The patient’s safety during the therapy is achieved through the integration of a fuzzy controller in the system control architecture. The fuzzy controller is employed to control the robot action according to the pain felt by the patient. By using fuzzy logic approach, the system can adapt effectively according to the patients’ conditions. The Queue Telemetry Transport Protocol (MQTT) is considered to overcome the latency during the human robot interaction. Based on a Kinect camera, the control technique is made gestural. The physiotherapist gestures are detected and transmitted to the software interface to be processed and be sent to the robot. The acquired measurements are recorded in a database that can be used later to monitor patient progress during the treatment protocol. The obtained experimental results show the effectiveness of the developed remote rehabilitation system

Human–Robot Role Arbitration via Differential Game Theory

The industry needs controllers that allow smooth and natural physical Human-Robot Interaction (pHRI) to make production scenarios more flexible and user-friendly. Within this context, particularly interesting is Role Arbitration, which is the mechanism that assigns the role of the leader to either the human or the robot. This paper investigates Game-Theory (GT) to model pHRI, and specifically, Cooperative Game Theory (CGT) and Non-Cooperative Game Theory (NCGT) are considered. This work proposes a possible solution to the Role Arbitration problem and defines a Role Arbitration framework based on differential game theory to allow pHRI. The proposed method can allow trajectory deformation according to human will, avoiding reaching dangerous situations such as collisions with environmental features, robot joints and workspace limits, and possibly safety constraints. Three sets of experiments are proposed to evaluate different situations and compared with two other standard methods for pHRI, the Impedance Control, and the Manual Guidance. Experiments show that with our Role Arbitration method, different situations can be handled safely and smoothly with a low human effort. In particular, the performances of the IMP and MG vary according to the task. In some cases, MG performs well, and IMP does not. In some others, IMP performs excellently, and MG does not. The proposed Role Arbitration controller performs well in all the cases, showing its superiority and generality. The proposed method generally requires less force and ensures better accuracy in performing all tasks than standard controllers. Note to Practitioners—This work presents a method that allows role arbitration for physical Human-Robot Interaction, motivated by the need to adjust the role of leader/follower in a shared task according to the specific phase of the task or the knowledge of one of the two agents. This method suits applications such as object co-transportation, which requires final precise positioning but allows some trajectory deformation on the fly. It can also handle situations where the carried obstacle occludes human sight, and the robot helps the human to avoid possible environmental obstacles and position the objects at the target pose precisely. Currently, this method does not consider external contact, which is likely to arise in many situations. Future studies will investigate the modeling and detection of external contacts to include them in the interaction models this work addresses

Optimization approaches for robot trajectory planning

[EN] The development of optimal trajectory planning algorithms for autonomous robots is a key issue in order to efficiently perform the robot tasks. This problem is hampered by the complex environment regarding the kinematics and dynamics of robots with several arms and/or degrees of freedom (dof), the design of collision-free trajectories and the physical limitations of the robots. This paper presents a review about the existing robot motion planning techniques and discusses their pros and cons regarding completeness, optimality, efficiency, accuracy, smoothness, stability, safety and scalability.Llopis-Albert, C.; Rubio, F.; Valero, F. (2018). Optimization approaches for robot trajectory planning. Multidisciplinary Journal for Education, Social and Technological Sciences. 5(1):1-16. doi:10.4995/muse.2018.9867SWORD1165

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance