Kuu- Young

Active and Passive Control of Walk-Assist Robot for Outdoor Guidance

In the currently aging society, walk-assist robots can play an important role in improving the activities of daily living of the elderly. In this paper, we propose a robot walking helper with both passive and active control modes for guidance. From the perspective of human safety, the passive mode adopts a braking control law on the wheels to differentially steer the vehicle. However, if the user walks uphill in the outdoor environment, external forces need to be supplied to the human-walker system. In this paper, we add an active mode to guide the user in situations where the passive control mode alone with user-applied forces is not adequate for guidance. The theory of differential flatness is used to plan the trajectory of control gains within the proposed scheme of the controller. Since the user input force and slope angle of the path are not known a priori , the theory of model predictive control is used to periodically compute the trajectory of these control gains. The simulation and experiment results show that the walk-assist robot, along with the structure of this proposed control scheme, can guide the user to a goal on a slope effectively.

VR-Based Teleoperation for Robot Compliance Control

Robots governed by remote human operators are excellent candidates for work in hazardous or uncertain environments such as nuclear plants or outer space. For successful teleoperation, it is important to let the operator feel physically present at the remote site. When the telerobotic system is used to execute compliance tasks in which simultaneous control of both position and force may be demanded and inevitable contact with environments is encountered, information about the interactions between the robot manipulator and the environment are especially crucial for the operator to make proper decisions. This paper proposes a VR-based telerobotic system for such compliance tasks. The proposed system provides both visual and haptic information. A local intelligence controller, capable of surface tracking and force regulation, is equipped on the robot manipulator to tackle the time-delay problem usually present in teleoperation and to share control load with the operator. The proposed telerobotic system is developed in a virtual environment due to recent gains in the capabilities and popularity of virtual reality to generate realistic telepresence. Experiments based on the surface-tracking and peg-in-hole compliance tasks demonstrate the effectiveness of the proposed system.

Implementation of a Variable D-H Parameter Model for Robot Calibration Using an FCMAC Learning Algorithm

Current robot calibration schemes usually employ calibration models with constant error parameters. Consequently,they are inevitably subject to a certain degree of locality, i.e., the calibrated error parameters (CEPs) will produce the desiredaccuracy only in certain regions of the robot workspace. To deal with the locality phenomenon, CEPs that vary in differentregions of the robot workspace may be more appropriate. Hence, we propose a variable D-H (Denavit and Hartenberg)parameter model to formulate variations of CEPs. An FCMAC (Fuzzy Cerebellar Model Articulation Controller) learningalgorithm is used to implement the proposed variable D-H parameter model. Simulations and experiments that verify theeffectiveness of the proposed calibration scheme based on the variable D-H parameter model are described.

An impact control scheme inspired by human reflex

In this article, we propose a control scheme to deal with unexpected impacts. Impact is inevitable when robot manipulators interact with the environment. Undesirable impacts may induce large interaction forces harmful to robot manipulators and the environment. Impacts may also excite oscillations, and even result in manipulator instability. When unexpected impacts occur, a very limited amount of time is available for control. Thus, a reflex mechanism, which emulates the functioning of human reflexes, is included in the proposed scheme. Human reflex is a kind of human action that requires no conscious effort; consequently, it responds to external stimuli without much delay. Simulations are performed to verify the effectiveness of the proposed scheme under a wide range of environmental variations and impact velocities.

An approach to enlarge learning space coverage for robot learning control

In robot learning control, the learning space for executing general motions of multijoint robot manipulators is quite large. Consequently, for most learning schemes, the learning controllers are used as subordinates to conventional controllers or the learning process needs to be repeated each time a new trajectory is encountered, although learning controllers are considered to be capable of generalization. In this paper, we propose an approach for larger learning space coverage in robot learning control. In this approach, a new structure for learning control is proposed to organize information storage via effective memory management. The proposed structure is motivated by the concept of human motor program and consists mainly of a fuzzy system and a cerebellar model articulation controller (CMAC)-type neural network. The fuzzy system is used for governing a number of sampled motions in a class of motions. The CMAC-type neural network is used to generalize the parameters of the fuzzy system, which are appropriate for the governing of the sampled motions, to deal with the whole class of motions. Under this design, in some sense the qualitative fuzzy rules in the fuzzy system are generalized by the CMAC-type neural network and then a larger learning space can be covered. Therefore, the learning effort is dramatically reduced in dealing with a wide range of robot motions, while the learning process is performed only once. Simulations emulating ball carrying under various conditions are presented to demonstrate the effectiveness of the proposed approach.

Walk-Assist Robot: A Novel Approach to Gain Selection of a Braking Controller Using Differential Flatness

With the growth of elderly population in our society, technology will play an important role in providing functional mobility to humans. From the perspective of human safety, it is desirable that controllers for walk-assist robots be dissipative, i.e., the energy is supplied by the human to the walker, while the controller modulates this energy, also the motion of the walker, while dissipating this energy. The simplest form of a dissipating controller is a brake, where resistive torques are applied to the wheels proportional to their speeds. The fundamental question that we ask in this paper is how to modulate these proportionality gains over time for the two wheels so that the walker can perform point-to-point motions in the state space. The unique contribution of this paper is a novel way in which the theory of differential flatness is used to plan the trajectory of these braking gains. Since the user input force is not known prior, the theory of model predictive control is used to periodically compute the trajectory of these braking gains. The simulation results show that the walking assist robot, along with the structure of this proposed control scheme, can guide the user to a goal accurately.

Challenges in VR-based robot teleoperation

Teleoperation techniques unite the human operator as the supervisor and the machine as the manipulator. Because both the human operator and machine are involved in the control loop and they are connected via the network instead of a direct link, the development of a telerobotic system poses challenges different from systems involving machines alone. And these challenges become more severe when the telerobotic system is used for compliance task, in which simultaneous control of both position and force are demanded and inevitable contact with the environment is encountered. In this paper, we discuss the challenges we may face in designing a telerobotic system. We then describe the networked VR-based telerobotic system developed in our laboratory and report the results by using the developed telerobotic system to execute several different kinds of compliance tasks.

An intelligent radar predictor for high-speed moving-target tracking

Due to rapid the increase in missile speed, the air-defense radar system faces severe challenge in tracking these high-speed missiles. During tracking, the radar data are read into the system in a real-time manner sequentially, and thus only few data are available for trajectory estimation in every short time period. Therefore, in this paper, we propose an intelligent radar predictor, including a self-organizing map (SOM), to achieve accurate trajectory estimation under the strict time constraint. By knowing the dynamic model of the moving target, the SOM, an unsupervised neural network, learns to predict the target trajectory using a limited number of data. The performance of the SOM is compared with that of the Kalman filtering. Simulation results based on both the generated and real radar data demonstrate the effectiveness of the proposed intelligent radar predictor.

Reinforcement Learning and Robust Control for Robot Compliance Tasks

The complexity in planning and control of robot compliance tasks mainly results from simultaneous control of both position and force and inevitable contact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. In addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we propose a reinforcement learning and robust control scheme for robot compliance tasks. A reinforcement learning mechanism is used to tackle variations among compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the reinforcement learning mechanism. Simulations based on deburring compliance tasks demonstrate the effectiveness of the proposed scheme.

Upper-Limb EMG-Based Robot Motion Governing Using Empirical Mode Decomposition and Adaptive Neural Fuzzy Inference System

To improve the quality of life for the disabled and elderly, this paper develops an upper-limb, EMG-based robot control system to provide natural, intuitive manipulation for robot arm motions. Considering the non-stationary and nonlinear characteristics of the Electromyography (EMG) signals, especially when multi-DOF movements are involved, an empirical mode decomposition method is introduced to break down the EMG signals into a set of intrinsic mode functions, each of which represents different physical characteristics of muscular movement. We then integrate this new system with an initial point detection method previously proposed to establish the mapping between the EMG signals and corresponding robot arm movements in real-time. Meanwhile, as the selection of critical values in the initial point detection method is user-dependent, we employ the adaptive neuro-fuzzy inference system to find proper parameters that are better suited for individual users. Experiments are performed to demonstrate the effectiveness of the proposed upper-limb EMG-based robot control system.