Survey of research in the design and control of automated guided vehicle systems

This paper presents a human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation. These signals are recorded with electrodes attached to the skin on top of selected muscles and reflect the activation of the observed muscle. They are evaluated by a sophisticated but simplified biomechanical model of the human body to derive the desired action of the operator. A support action is computed in accordance to the desired action and is executed by the exoskeleton. The biomechanical model fuses results from different biomechanical and biomedical research groups and performs a sensible simplification considering the intended application. Some of the model parameters reflect properties of the individual human operator and his or her current body state. A calibration algorithm for these parameters is presented that relies exclusively on sensors mounted on the exoskeleton. An exoskeleton for knee joint support was designed and constructed to verify the model and to investigate the interaction between operator and machine in experiments with force support during everyday movements.

A Human--Exoskeleton Interface Utilizing Electromyography

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

https://iopscience.iop.org/article/10.1088/1741-2560/11/5/051001/pdf

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

An on-body personal lift augmentation device (PLAD) reduces EMG amplitude of erector spinae during lifting tasks

Development of a 3DOF mobile exoskeleton robot for human upper-limb motion assist

This paper proposes two methods that give intelligence to automatically guided vehicles (AGVs). In order to drive AGVs autonomously, two types of problems need to be overcome. They are the AGV navigation problem and collision avoidance problem. The first problem has been well known since 1980s. A new method based on the feature scene recognition and acquisition is proposed. The sparse distributed memory neural network (SDM) is employed for the scene recognition and acquisition. The navigation route for the AGV is learnt by use of Q-learning depending on the recognized and acquired scenes. The second problem is described as mutual understanding of behaviors between AGVs. The method of mutual understanding is proposed by the use of Q-learning. Those two methods are combined together for driving plural AGVs autonomously to deliver raw materials between machine tools in a factory. They are incorporated into the AGVs as the machine intelligence. In experimental simulations, it is verified that the first proposed method can guide the AGV to the suitable navigation and that the second method can acquire knowledge of mutual understanding of the AGVs’ behaviors.

Intelligent AGV driving toward an autonomous decentralized manufacturing system

A power assisting device for lower back flexion and extension, when carrying a heavy load is presented in this paper. When people lift up something heavy, they use both hands. So the development of a controller without using hands is necessary. We attempt to control the device by a voluntary human motion. First, lifting up and putting down motions are analyzed using a human model. Results show that torques in a hip joint and a knee joint are characteristic for control the device. It means that EMG signals in front and back thigh muscles can become feature values because the muscles located there connect the hip joint and the knee joint. After that, a controller of the device using the EMG signals is developed. Finally, artificial neural networks (ANN) are introduced as a solution of the problem of individual differences.

Yukinori Kakazu

Papers

Intelligent AGV driving toward an autonomous decentralized manufacturing system

Study for control of a power assist device. Development of an EMG based controller considering a human model

Design of Wearable Power-Assist Device for Lower Back Support

An Analysis of Human Motion for Control of a Wearable Power Assist System

Loose Robot Communication over the Internet