Written by two of Europe's leading robotics experts, this book provides the tools for a unified approach to the modelling of robotic manipulators, whatever their mechanical structure. No other ...publication covers the three fundamental issues of robotics: modelling, identification and control. It covers the development of various mathematical models required for the control and simulation of robots.
· World class authority· Unique range of coverage not available in any other book· Provides a complete course on robotic control at an undergraduate and graduate level
This article presents a holistic approach to the engineering of an artificial robot skin for robots. An example of a multimodal skin cell is given, one that supports multiple human-like sensing ...modalities, and support for skin cell network is also provided; this is essential to form large-area skin patches in order to cover the surfaces of robots. The essential elements of efficiently handling a large amount of tactile data are explained. A general control framework, which supports robots commanded in position, velocity, and torque, is provided and validated. Several applications of this robot skin will be presented, demonstrating the effectiveness and efficiency of our artificial robot skin to support a wide number of robotic platforms as well as its ease of use across different domains.
Unlike most control systems, kinematic uncertainty is present in robot control systems in addition to dynamic uncertainty. The use of different types of external sensors in various configurations ...also results in different sensory transformation or Jacobian matrices and thus leads to different kinematic models. Currently, there is no systematic theoretical framework in developing data-driven neural network (NN) learning and control methods for task-space tracking control of robots with unknown kinematics and dynamics. The existing NN controllers are limited to either dynamic control or kinematic control without considering the interaction between the inner control loop and the outer control loop. In this paper, a NN based data driven offline learning algorithm and an online learning controller are proposed, which are combined in a complementary way. The proposed task-space control algorithms can be implemented on robotic systems with closed control architecture by considering the interaction with the inner control loop. Theoretical analyses are presented to show the stability of the systems and experimental results are presented to illustrate the performance of the proposed learning algorithms.
Abstract
This article describes the controlling of robot arm using different operations with choice. It is a programmable arm to hold and place the objects. Also it can change the position of the ...objects. The addition features are added to robotic arm using a reprogrammable FPGA mechanism. The robotic arm parts are jointed to give flexible move and rotate movements. We have used spartan3 FPGA to implement and test the robot arm conditions. We have used digital controlling inputs to control the robot arm directions.
The article proposes a method for solving the problem of predicting the self-collision of multi-link manipulators with their agreed work. The method is based on the analysis of projections of ...manipulator links on coordinate planes. The proposed approach will make it possible to solve the problem simple and suitable for the online prediction mode of critical positions of manipulators, possible self-collision, with their coordinated work. The developed algorithm was tested when constructing the control of an anthropomorphic robot SAR-400.
Body-machine interfaces (BoMIs)—systems that control assistive devices (e.g., a robotic manipulator) with a person’s movements—offer a robust and non-invasive alternative to brain-machine interfaces ...for individuals with neurological injuries. However, commercially-available assistive devices offer more degrees of freedom (DOFs) than can be efficiently controlled with a user’s residual motor function. Therefore, BoMIs often rely on nonintuitive mappings between body and device movements. Learning these mappings requires considerable practice time in a lab/clinic, which can be challenging. Virtual environments can potentially address this challenge, but there are limited options for high-DOF assistive devices, and it is unclear if learning with a virtual device is similar to learning with its physical counterpart. We developed a novel virtual robotic platform that replicated a commercially-available 6-DOF robotic manipulator. Participants controlled the physical and virtual robots using four wireless inertial measurement units (IMUs) fixed to the upper torso. Forty-three neurologically unimpaired adults practiced a target-matching task using either the physical (sample size n = 25) or virtual device (sample size n = 18) involving pre-, mid-, and post-tests separated by four training blocks. We found that both groups made similar improvements from pre-test in movement time at mid-test (Δvirtual: 9.9 ± 9.5 s; Δphysical: 11.1 ± 9.9 s) and post-test (Δvirtual: 11.1 ± 9.1 s; Δphysical: 11.8 ± 10.5 s) and in path length at mid-test (Δvirtual: 6.1 ± 6.3 m/m; Δphysical: 3.3 ± 3.5 m/m) and post-test (Δvirtual: 6.6 ± 6.2 m/m; Δphysical: 3.5 ± 4.0 m/m). Our results indicate the feasibility of using virtual environments for learning to control assistive devices. Future work should determine how these findings generalize to clinical populations.
•We developed a novel virtual body-machine interface (BoMI) to control assistive robots.•We compared motor learning with the virtual BoMI to a physical replica.•Learning with the virtual platform was similar to the physical platform.•Our virtual BoMI could serve as a potential adjunct for assistive rehabilitation.•The proposed interface could also be a useful tool for motor learning experiments.
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