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Fundamentals of robotic grasping and fixturing
This book provides a fundamental knowledge of robotic grasping and fixturing (RGF) manipulation. For RGF manipulation to become a science rather than an art, the content of the book is uniquely designed for a thorough understanding of the RGF from the multifingered robot hand grasp, basic fixture design principle, and evaluating and planning of robotic grasping/fixturing, and focuses on the modeling and applications of the RGF.
eBook
Adaptive control of robot manipulators
This book introduces an unified function approximation approach to the control of uncertain robot manipulators containing general uncertainties. It works for free space tracking control as well as compliant motion control. It is applicable to the rigid robot and the flexible joint robot. Even with actuator dynamics, the unified approach is still feasible. All these features make the book stand out from other existing publications.
eBook
Oscillation-free point-to-point motions of planar differentially flat under-actuated robots: a Laplace transform method
Differentially flat under-actuated robots are characterized by more degrees of freedom (DOF) than actuators: this makes possible the design of lightweight cheap robots with high dexterity. The main issue of such robots is the control of the passive joint, which requires accurate dynamic modeling of the robot. Friction is usually discarded to simplify the models, especially in the case of low-speed trajectories. However, this simplification leads to oscillations of the end-effector about the final position, which are incompatible with fast and accurate motions. This paper focuses on planar
$n$
-DOF serial robotic arms with
$n-1$
actuated rotational joints plus one final passive rotational joint with stiffness and friction properties. These robots, if properly balanced, are differentially flat. When the non-actuated joint can be considered frictionless, differentially flat robots can be controlled in open loop, calculating the motor torques demanded by point-to-point motions. This paper extends the open-loop control to robots with a passive joint with viscous friction adopting a Laplace transform method. This method can be adopted by exploiting the particular structure of the equations of motion of differentially flat under-actuated robots in which the last equations are linear. Analytical expressions of the motor torques are obtained. The work is enriched by an experimental validation of a
$2$
-DOF under-actuated robot and by numerical simulations of the
$2$
- and
$4$
-DOF robots showing the suppression of unwanted oscillations.
Journal Article
Continuous Path Smoothing for Car-Like Robots Using B-Spline Curves
A practical approach for generating motion paths with continuous steering for car-like mobile robots is presented here. This paper addresses two key issues in robot motion planning; path continuity and maximum curvature constraint for nonholonomic robots. The advantage of this new method is that it allows robots to account for their constraints in an efficient manner that facilitates real-time planning. B-spline curves are leveraged for their robustness and practical synthesis to model the vehicle’s path. Comparative navigational-based analyses are presented to selected appropriate curve and nominate its parameters. Path continuity is achieved by utilizing a single path, to represent the trajectory, with no limitations on path, or orientation. The path parameters are formulated with respect to the robot’s constraints. Maximum curvature is satisfied locally, in every segment using a smoothing algorithm, if needed. It is demonstrated that any local modifications of single sections have minimal effect on the entire path. Rigorous simulations are presented, to highlight the benefits of the proposed method, in comparison to existing approaches with regards to continuity, curvature control, path length and resulting acceleration. Experimental results validate that our approach mimics human steering with high accuracy. Accordingly, efficiently formulated continuous paths ultimately contribute towards passenger comfort improvement. Using presented approach, autonomous vehicles generate and follow paths that humans are accustomed to, with minimum disturbances.
Journal Article
Reinforcement learning-based motion control for snake robots in complex environments
Snake robots can move flexibly due to their special bodies and gaits. However, it is difficult to plan their motion in multi-obstacle environments due to their complex models. To solve this problem, this work investigates a reinforcement learning-based motion planning method. To plan feasible paths, together with a modified deep Q-learning algorithm, a Floyd-moving average algorithm is proposed to ensure smoothness and adaptability of paths for snake robots’ passing. An improved path integral algorithm is used to work out gait parameters to control snake robots to move along the planned paths. To speed up the training of parameters, a strategy combining serial training, parallel training, and experience replaying modules is designed. Moreover, we have designed a motion planning framework consists of path planning, path smoothing, and motion planning. Various simulations are conducted to validate the effectiveness of the proposed algorithms.
Journal Article
A Velocity-Based Dynamic Model and Its Properties for Differential Drive Mobile Robots
An important issue in the field of motion control of wheeled mobile robots is that the design of most controllers is based only on the robot’s kinematics. However, when high-speed movements and/or heavy load transportation are required, it becomes essential to consider the robot dynamics as well. The control signals generated by most dynamic controllers reported in the literature are torques or voltages for the robot motors, while commercial robots usually accept velocity commands. In this context, we present a velocity-based dynamic model for differential drive mobile robots that also includes the dynamics of the robot actuators. Such model has linear and angular velocities as inputs and has been included in Peter Corke’s Robotics Toolbox for MATLAB, therefore it can be easily integrated into simulation systems that have been built for the unicycle kinematics. We demonstrate that the proposed dynamic model has useful mathematical properties. We also present an application of such model on the design of an adaptive dynamic controller and the stability analysis of the complete system, while applying the proposed model properties. Finally, we show some simulation and experimental results and discuss the advantages and limitations of the proposed model.
Journal Article
An operational calibration approach of industrial robots through a motion capture system and an artificial neural network ELM
In the industry, robots’ absolute accuracy is critical. This study provides a new calibration method to increase the robot’s absolute accuracy. This method relies on a motion capture device as a measurement tool that can capture the robot’s continuous motion state in any conceivable domain. Geometric parameters are determined by conducting overall measurements and assessing each joint individually, and the Extreme Learning Machine (ELM) neural network is in charge of compensating for non-geometric error sources that are difficult or impossible to model correctly or completely. The combination of model-based parameter identification and ELM neural network–based compensation approaches is an effective solution for the correction of all robot error sources. To verify the method’s effectiveness and correctness, a six-revolute joint robot GSK RB03 is used across simulation and experiment. After calibration, the robot’s position accuracy ranges from 7.440 to 0.159 mm, and its orientation accuracy ranges from 3.073 to 0.077 degrees. The method’s practical applicability and correctness are determined.
Journal Article
A Terradynamics of Legged Locomotion on Granular Media
The theories of aero- and hydrodynamics predict animal movement and device design in air and water through the computation of lift, drag, and thrust forces. Although models of terrestrial legged locomotion have focused on interactions with solid ground, many animals move on substrates that flow in response to intrusion. However, locomotor-ground interaction models on such flowable ground are often unavailable. We developed a force model for arbitrarily-shaped legs and bodies moving freely in granular media, and used this \"terradynamics\" to predict a small legged robot's locomotion on granular media using various leg shapes and stride frequencies. Our study reveals a complex but generic dependence of stresses in granular media on intruder depth, orientation, and movement direction and gives insight into the effects of leg morphology and kinematics on movement.
Journal Article
Modeling and Control of an Octopus Inspired Soft Arm under Prescribed Spatial Motion Constraints
Precise control of soft robots remains challenging due to their highly compliant nature. Existing kinematic models may not enable accurate control performance as they do not account for actuation forces and dynamics. This paper tackles the problem of precise motion control for a soft robotic arm with longitudinal muscle actuators. We develop an integrated modeling and control framework that incorporates dynamics and actuation forces for improved accuracy. A key contribution is deriving and implementing a mathematical model of the soft muscle actuators using minimum norm optimization. Among, actuator saturation is addressed through a tension limiting function. Based on the whole model, we develop a dynamic surface controller with performance constraint to precisely control the soft arm. This controller makes soft robot subsequent interactions more secure. To assess the approach, numerical simulations and physical experiments are designed to verify the feasibility and rationality.
Journal Article