2,522 research outputs found
Exploring relationship between perceived motivation factors and job satisfaction
A number of studies that have focused on perceived motivation factors towards employees’ job satisfaction have been conducted. However, very few relate these concepts from knowledge workers’ perspective. This paper set out to have an empirical look at the relationship between motivation factors and employees’ job satisfaction. The survey had been conducted by distributing questionnaires to employees describes as “knowledge workers” by the manager. 50 respondents participated in this study to yield a response rate of 84.75%. The results showed that only two out three motivator factors (recognition and achievement) have significant relationship between with intrinsic satisfaction and only two out of three hygiene factor (security and working condition) have significant relationship with extrinsic job satisfaction. Thus, this signifies the importance of both motivator and hygiene factors in enhancing job satisfaction among employees especially among knowledge worker
Nonprehensile Dynamic Manipulation: A Survey
Nonprehensile dynamic manipulation can be reason- ably considered as the most complex manipulation task. It might be argued that such a task is still rather far from being fully solved and applied in robotics. This survey tries to collect the results reached so far by the research community about planning and control in the nonprehensile dynamic manipulation domain. A discussion about current open issues is addressed as well
Graphical User Interface (GUI) for Position and Trajectory Tracking Control of the Ball and Plate System Using H-Infinity Controller
In this paper, a graphical user interface (GUI) for position and trajectory tracking of the ball and plate system (BPS) control scheme using the double feedback loop structure i.e. a loop within a loop is proposed. The inner and the outer loop was designed using linear algebraic method by solving a set of Diophantine equations and sensitivity function. The results were simulated in MATLAB 2018a, and the trajectory tracking was displayed on a GUI, which showed that the plate was able to be stabilized at a time of 0.3546 seconds, and also the ball settled at 1.7087 seconds, when a sinusoidal circular reference trajectory of radius 0.4m with an angular frequency of 1.57rad/sec was applied to the BPS, the trajectory tracking error was 0.0095m. This shows that the controllers possess the following properties for the BPS, which are; good adaptability, strong robustness and a high control performance.
Gyroscopic Precession In Motion Modelling Of Ball-Shaped Robots
This study discusses kinematic and dynamic precession models for a rolling ball with a finite contact area and a point contact respectively. In literature, both conventions have been applied. In this paper, we discuss in detail the kinematic and dynamic models to describe the ball precession and the radius of a circular rolling path. The kinematic model can be used if the contact area and friction coefficient are sufficient to prevent slippage. The dynamic precession model has significance in multi-body simulation environments handling rolling balls with ideal point contacts. We have applied both the kinematic and dynamic precession model to evaluate the no-slip condition of the existing GimBall-robot. According to the result, the necessity of an external precession torque may cause slipping at lower velocities than expected if ignoring this torque.Peer reviewe
Two-Dimensional Positioning with Machine Learning in Virtual and Real Environments
In this paper, a ball-on-plate control system driven only by a neural network agent is presented. Apart from reinforcement learning, no other control solution or support was applied. The implemented device, driven by two servo motors, learned by itself through thousands of iterations how to keep the ball in the center of the resistive sensor. We compared the real-world performance of agents trained in both a real-world and in a virtual environment. We also examined the efficacy of a virtually pre-trained agent fine-tuned in the real environment. The obtained results were evaluated and compared to see which approach makes a good basis for the implementation of a control task implemented purely with a neural network
Applying deep reinforcement learning to cable driven parallel robots for balancing unstable loads : a ball case study
The current pandemic has highlighted the need for rapid construction of structures to treat patients and ensure manufacturing of health care products such as vaccines. In order to achieve this, rapid transportation of construction materials from staging area to deposition is needed. In the future, this could be achieved through automated construction sites that make use of robots. Toward this, in this paper a cable driven parallel manipulator (CDPM) is designed and built to balance a highly unstable load, a ball plate system. The system consists of eight cables attached to the end effector plate that can be extended or retracted to actuate movement of the plate. The hardware for the system was designed and built utilizing modern manufacturing processes. A camera system was designed using image recognition to identify the ball pose on the plate. The hardware was used to inform the development of a control system consisting of a reinforcement-learning trained neural network controller that outputs the desired platform response. A nested PID controller for each motor attached to each cable was used to realize the desired response. For the neural network controller, three different model structures were compared to assess the impact of varying model complexity. It was seen that less complex structures resulted in a slower response that was less flexible and more complex structures output a high frequency oscillation of the actuation signal resulting in an unresponsive system. It was concluded that the system showed promise for future development with the potential to improve on the state of the art
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