A literature survey of the robotic technologies during the COVID-19 pandemic

Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies’ perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies’ perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work’s technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.     

Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

一种基于阵列式触觉传感器的主动触觉搜索方法及仿真

本文在一种二指节手指模型的基础上，提出了一种新的主动触觉搜索策略和方法，综合利用阵列式触觉传感铭的输出图象来不断改变机械手的姿态，从而依次有序地搜索到三维物体的特征点，并据此生成物体的三维图形。此外还讨论了上述方法的计算机仿真实现。     

柔性冗余度机器人残余振动主动控制

研究柔性冗余度机器人的残余振动主动控制问题，设计了具有压电作动器与应变传感器的机敏杆件，建立了受控系统的状态空间表达式，采用独立模态空间控制理论设计LQR状态反馈控制器，并基于对偶原理设计了具有指定收敛特性的Luenberger全维状态观测器，最后，以平面3R柔性冗余度机器人为例进行了计算机仿真，结果表明，采用这种主动控制方法可以显著改善柔性冗余度机器人的动力学品质。     

Rule-based supervisory control of a two-link flexible manipulator

In this paper, we propose a two level hierarchical control strategy to achieve accurate end-point position of a planar two-link flexible manipulator. The upper level consists of a feedforward rule-based supervisory controller that incorporates fuzzy logic, whereas the lower level consists of conventional controllers that combine shaft position-endpoint acceleration feedback for disturbance rejection properties and shaping of the (joint) actuator inputs to minimize the energy transferred to the flexible modes during commanded movements. The effectiveness of this hierarchical control strategy is verified by experimental results for various movements of the links, in various configurations. In particular, we illustrate how the hierarchical intelligent control strategy performs better than conventional control techniques for endpoint position control in the presence of flexure effects.     

Theoretical Investigation of Time Suboptimal Control of Industrial Manipulators Along Specified Paths

A method for the time suboptimal control of an industrial manipulator from an initial position and orientation to a final position and orientation as it moves along a specified path is proposed. Nonlinear system equations that describe the manipulator motion are linearized at each time step along the path. A method which gives the control inputs (joint angular velocities) for time suboptimal control of the manipulator is developed. In the formulation, joint angular velocity and acceleration limitations are also taken into consideration. A six degree of freedom elbow type manipulator is used in numerical examples to verify the method developed.     

Safe and effective navigation of autonomous robots in hazardous environments

The development of autonomous mobile machines to perform useful tasks in real work environments is currently being impeded by concerns over effectiveness, commercial viability and, above all, safety. This paper introduces a case study of a robotic excavator to explore a series of issues around system development, navigation in unstructured environments, autonomous decision making and changing the behaviour of autonomous machines to suit the prevailing demands of users. The adoption of the Real-Time Control Systems (RCS) architecture (Albus, 1991) is proposed as a universal framework for the development of intelligent systems. In addition it is explained how the use of Partially Observable Markov Decision Processes (POMDP) (Kaelbling et al., 1998) can form the basis of decision making in the face of uncertainty and how the technique can be effectively incorporated into the RCS architecture. Particular emphasis is placed on ensuring that the resulting behaviour is both task effective and adequately safe, and it is recognised that these two objectives may be in opposition and that the desired relative balance between them may change. The concept of an autonomous system having “values” is introduced through the use of utility theory. Limited simulation results of experiments are reported which demonstrate that these techniques can create intelligent systems capable of modifying their behaviour to exhibit either ‘safety conscious’ or ‘task achieving’ personalities.     

A visual tool for computer supported learning: The robot motion planning example

We introduce an effective computer aided learning visual tool (CALVT) to teach graph-based applications. We present the robot motion planning problem as an example of such applications. The proposed tool can be used to simulate and/or further to implement practical systems in different areas of computer science such as graphics, computational geometry, robotics and networking. In the robot motion planning example, CALVT enables users to setup the working environment by creating obstacles and a robot of different shapes, specifying starting and goal positions, and setting other path or environment parameters from a user-friendly interface. The path planning system involves several phases. Each of these modules is complex and therefore we provide the possibility of visualizing graphically the output of each phase. Based on our experience, this tool has been an effective one in classroom teaching. It not only cuts down, significantly, on the instructor’s time and effort but also motivates senior/graduate students to pursue work in this specific area of research.