In this paper we illustrate the architecture and the main features of a bio-inspired control system employed to govern an anthropomorphic artificial Arm. The manipulation system we developed was designed starting from...
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ISBN:
(纸本)9789728865825
In this paper we illustrate the architecture and the main features of a bio-inspired control system employed to govern an anthropomorphic artificial Arm. The manipulation system we developed was designed starting from a deeply study of the human limb from the anatomical, physiological and neurological point of view. In accordance with the general view of the Biorobotics field we try to replicate the structure and the functionalities of the natural limb. Thanks to this biomimetic approach we obtained a system that can perform movements similar to those of the natural limb. The control system is organized in a hierarchical way. The low level controller emulates the neural circuits located in the human spinal cord and is charged to reproduce the reflexes behaviors and to control the arm stiffness. The high level control system generates the arm trajectory performing the inverse kinematics and furnishing the instantaneous muscles reference position. In particular we implemented the Inverse kinematic using a gradient based algorithm;at each step the actuators movements are arranged in order to decrease the distance between the wrist and the target position. Simulation and experimental results shows the ability of the control system in governing the arm to follow a predefined trajectory and to perform human like reflexes behaviors.
In the paper we recover the static characteristic of Wiener-Hammerstein (sandwich) system from inputoutput data. The system is excited and disturbed by random processes with arbitrary distribution. Two kernel-based es...
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Engineering efforts are one of the major cost factors in today's industrial automation systems. We present a configuration system, which grants a reduced obligation of engineering effort. Through self-learning the...
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ISBN:
(纸本)9789897585227
Engineering efforts are one of the major cost factors in today's industrial automation systems. We present a configuration system, which grants a reduced obligation of engineering effort. Through self-learning the configuration system can adapt to various tasks by actively learning about its environment. We validate our configuration system using a robotic perception system, specifically a picking application. Perception systems for robotic applications become increasingly essential in industrial environments. Today, such systems often require tedious configuration and design from a well trained technician. These processes have to be carried out for each application and each change in the environment. Our robotic perception system is evaluated on the BOP benchmark and consists of two elements. First, we design building blocks, which are algorithms and datasets available for our configuration algorithm. Second, we implement agents (configuration algorithms) which are designed to intelligently interact with our building blocks. On an examplary industrial robotic picking problem we show, that our autonomous engineering system can reduce engineering efforts.
An approach for nonholonomic two-wheeled mobile robot trajectory tracking and obstacle avoiding is presented in this paper. If the desired trajectory is provided by high level planner, trajectory tracking problem can ...
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ISBN:
(纸本)9789898565211
An approach for nonholonomic two-wheeled mobile robot trajectory tracking and obstacle avoiding is presented in this paper. If the desired trajectory is provided by high level planner, trajectory tracking problem can be solved in various ways. In this paper, tracking is provided using proportional-integral (PI) or fuzzy logic controller (FLC). Unfortunately, tracking is never perfect, due to uncertainties and obstacles can change their positions in time. In order to overcome these difficulties, additional correction controller must be used. Here is proposed fuzzy controller, which slightly changes control action of the tracking controller in order to prevent collision with obstacles. This approach is proved to be efficient even in dynamic environments. Simulation results are presented as illustration of the proposed approach.
The majority of the kinematics analysis carried out on the human body are usually available only for use in the sagittal plane. Limited studies were interested in this analysis in all three planes (sagittal, transvers...
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ISBN:
(纸本)9789897583049
The majority of the kinematics analysis carried out on the human body are usually available only for use in the sagittal plane. Limited studies were interested in this analysis in all three planes (sagittal, transverse, and frontal) where motions of all joints occur. The aim of this paper is to develop a new optimal kinematic analysis of human lower limbs in three-dimensional space for a rehabilitation end. The proposed approach is focused on optimizing the manipulability and the human performance of the human leg, as being a physiologically constrained three-link arm. The obtained forward kinematic model leads to define the feasible workspace of the human leg in the considered configuration. Using an effective optimization-based human performance measure that incorporates a new objective function of musculoskeletal discomfort, the optimal inverse kinematic (IK) model is obtained.
This paper presents an approach to the formal verification of safety properties of the behaviour-based control network of the mobile outdoor robot RAYON. In particular, we consider behaviours that are used for the com...
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This paper presents an approach to the formal verification of safety properties of the behaviour-based control network of the mobile outdoor robot RAYON. In particular, we consider behaviours that are used for the computation of the projected vehicle's velocity from obstacle proximity sensor data and inclination information. We describe how this group of behaviours is implemented in the synchronous language Quartz in order to be formally verified using model checking techniques of the Averest verification framework. Moreover, by integrating the automatically generated and verified code into the behaviour network, it can be guaranteed that the robot slows down and stops as required by the given safety specifications.
In this paper, we present the design and fabrication of a bio-inspired caudal fin actuator for propulsion and maneuvering purposes in a fish-like robot. Shape memory alloy (SMA) composite actuator is customized to pro...
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ISBN:
(纸本)9789897581236
In this paper, we present the design and fabrication of a bio-inspired caudal fin actuator for propulsion and maneuvering purposes in a fish-like robot. Shape memory alloy (SMA) composite actuator is customized to provide the necessary work out for the caudal fin. The pocket holes guide, electrical wiring and attachment pads for SMA actuators are all embedded in a single layer of cellulose acetate film, sandwiched between two layers of silicone rubber. Instead of using joints, four SMAs are fixed along the soft structure of the caudal fin and bend this to a certain mode shape. The caudal fin actuator was inspired by Largemouth Bass, which uses sub-carangiform mode swimming and the caudal fin during steady swimming and maneuvering.
In recent years, advanced model-based and data-driven control methods are unlocking the potential of complex robotics systems, and we can expect this trend to continue at an exponential rate in the near future. Howeve...
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ISBN:
(纸本)9798350384581;9798350384574
In recent years, advanced model-based and data-driven control methods are unlocking the potential of complex robotics systems, and we can expect this trend to continue at an exponential rate in the near future. However, ensuring safety with these advanced control methods remains a challenge. A well-known tool to make controllers (either Model Predictive controllers or Reinforcement Learning policies) safe, is the so-called control-invariant set (a.k.a. safe set). Unfortunately, for nonlinear systems, such a set cannot be exactly computed in general. Numerical algorithms exist for computing approximate control-invariant sets, but classic theoretic control methods break down if the set is not exact. This paper presents our recent efforts to address this issue. We present a novel Model Predictive control scheme that can guarantee recursive feasibility and/or safety under weaker assumptions than classic methods. In particular, recursive feasibility is guaranteed by making the safe-set constraint move backward over the horizon, and assuming that such set satisfies a condition that is weaker than control invariance. Safety is instead guaranteed under an even weaker assumption on the safe set, triggering a safe task-abortion strategy whenever a risk of constraint violation is detected. We evaluated our approach on a simulated robot manipulator, empirically demonstrating that it leads to less constraint violations than state-of-the-art approaches, while retaining reasonable performance in terms of tracking cost, number of completed tasks, and computation time.
Refined algorithms for solving continuous-time algebraic Riccati equations using Newton's method with or without line search are discussed. Their main properties are briefly presented. Algorithmic details incorpor...
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ISBN:
(纸本)9789897580390
Refined algorithms for solving continuous-time algebraic Riccati equations using Newton's method with or without line search are discussed. Their main properties are briefly presented. Algorithmic details incorporated in the developed solver are described. The results of an extensive performance investigation on a large collection of examples are summarized. Several numerical difficulties and observed unexpected behavior are reported. These algorithms are strongly recommended for improving the solutions computed by other solvers.
Minimum-time trajectories for applications where a geometric path is followed by a kinematically redundant robot's end-effector may yield economical improvements in many cases compared to conventional manipulators...
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ISBN:
(纸本)9789897581984
Minimum-time trajectories for applications where a geometric path is followed by a kinematically redundant robot's end-effector may yield economical improvements in many cases compared to conventional manipulators. While for non-redundant robots the problem of finding such trajectories has been solved, the redundant case has not been treated exhaustively. In this contribution, the problem is split into two interlaced parts: inverse kinematics and trajectory optimization. In a direct optimization approach, the inverse kinematics problem is solved numerically at each time point. Therein, the manupulator's kinematic redundancy is exploited by introducing scaled nullspace basis vectors of the Jacobian of differential velocities. The scaling factors for each time point are decision variables, thus the inverse kinematics is solved optimally w.r.t. the trajectory optimization goal, i.e. minimizing end time. The effectiveness of the presented method is shown by means of the example of a planar 4R manipulator with two redundant degrees of freedom.
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