Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Preface. 1. Introduction. 2. Rigid Motions and Homogeneous Transformations. 3. Forward and Inverse Kinematics. 4. Velocity Kinematics-The Jacobian. 5. Path and Trajectory Planning. 6. Independent Joint Control. 7. Dynamics. 8. Multivariable Control. 9. Force Control. 10. Geometric Nonlinear Control. 11. Computer Vision. 12. Vision-Based Control. Appendix A: Trigonometry. Appendix B: Linear Algebra. Appendix C: Dynamical Systems. Appendix D: Lyapunov Stability. Index.

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Robot Modeling and Control

Paper: The internal model principle of control theory

Human skin is a remarkable organ. It consists of an integrated, stretchable network of sensors that relay information about tactile and thermal stimuli to the brain, allowing us to maneuver within our environment safely and effectively. Interest in large-area networks of electronic devices inspired by human skin is motivated by the promise of creating autonomous intelligent robots and biomimetic prosthetics, among other applications. The development of electronic networks comprised of flexible, stretchable, and robust devices that are compatible with large-area implementation and integrated with multiple functionalities is a testament to the progress in developing an electronic skin (e-skin) akin to human skin. E-skins are already capable of providing augmented performance over their organic counterpart, both in superior spatial resolution and thermal sensitivity. They could be further improved through the incorporation of additional functionalities (e.g., chemical and biological sensing) and desired properties (e.g., biodegradability and self-powering). Continued rapid progress in this area is promising for the development of a fully integrated e-skin in the near future.

25th Anniversary Article: The Evolution of Electronic Skin (E-Skin): A Brief History, Design Considerations, and Recent Progress

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Micro-electro-mechanical system (MEMS) is one of the few commercial platforms for building optical switches 2D MEMS L-switching matrix has been introduced recently to double sizes of 2D MEMS crossbar switches The sizes of the switches are mainly limited by the Gaussian signal loss associated path difference Though the design of L-switching matrix improves system scalability but it suffers internal blocking problem In this paper, a rearrangeably nonblocking algorithm will be presented Moreover, two enhanced designs are proposed to improve the overall system performance of the L-switching matrix They are the staircase switching mechanism and redundant switching system With the improved internal blocking probability can be minimized Consequently, the L-switching probability can be minimized Consequently, the L-switching matrix performs similar to an optical switching fabric with wide-sense nonblocking property

Enhanced designs on MEMS L-switching matrix

flexible laboratory automation architectures, robotic-based laboratory automation, track-based laboratory automation,

https://journals.sagepub.com/doi/pdf/10.1016/j.jala.2006.05.014

Hardware Flexibility of Laboratory Automation Systems: Analysis and New Flexible Automation Architectures

The basic characteristic of kinematically redundant robots is that non-unique joint solutions may exist for a specified end effector location. Thus, trajectory planning for a kinematically redundant robot requires an optimization procedure to determine the joint displacements when solving the inverse kinematics relations. In this paper an analytical solution is developed for the trajectory optimization problem of redundant robots based on the classical Lagrange’s method. A detailed formulation is provided for seven degrees of freedom robots, which minimizes the Euclidean norm of joint dislacements for point-to-point motion trajectory planning.

Analytical Trajectory Optimization of Seven Degrees of Freedom Redundant Robots

A robot was used to make fiber-optic reflectance measurements from 400 to 700 nm in 10-nm increments at six sites, 10 cm apart, along the length of 48 pork loins. Meat quality was assessed in the longissimus dorsi near the thoracolumbar junction using 1) a bag-drip method for fluid loss, 2) a subjective evaluation of wetness, 3) a colorimeter measurement of paleness (CIE L), and 4) a subjective evaluation for Japanese pork color scores (JPCS). Sorting of the loins in the commercial plant from which they originated was correlated (P .05) at 700 nm and R =.58 (P >.05) adding 430 nm; with wetness score, r =.25 (P>.05); with CIE L * , r =.58 (P < .025) at 700 nm; and with JPCS, r = -.71 (P <.01) at 700 nm. Thus, as well as detecting obvious PSE loins, the robotic probe also had a limited capability to sort loins all categorized as normal at the plant.

Meat quality variation in the robotic sorting of pork loins.

This paper describes a step by step validation procedure of a mobile manipulator simulation. The procedure includes performing basic model simulations, a sensitivity analysis, and a stochastic output analysis. Static, quasi-static, and dynamic experiments performed on a real system and simulated using DADS/sup TM/ are also presented. An algorithm to time the model parameters, to match the output of the simulations to the output of the real system, was successfully implemented. Finally, an optimization procedure on the design parameters of the system is discussed.

Andrew A. Goldenberg

Papers

Enhanced designs on MEMS L-switching matrix

Hardware Flexibility of Laboratory Automation Systems: Analysis and New Flexible Automation Architectures

Analytical Trajectory Optimization of Seven Degrees of Freedom Redundant Robots

Meat quality variation in the robotic sorting of pork loins.

A simulation study of a mobile manipulator