Magneto-Rheological Actuators for Human-Safe Robots: Modeling, Control, and Implementation

In recent years, research on physical human-robot interaction has received considerable attention. Research on this subject has led to the study of new control and actuation mechanisms for robots in order to achieve intrinsic safety. Naturally, intrinsic safety is only achievable in kinematic structures that exhibit low output impedance. Existing solutions for reducing impedance are commonly obtained at the expense of reduced performance, or significant increase in mechanical complexity. Achieving high performance while guaranteeing safety seems to be a challenging goal that necessitates new actuation technologies in future generations of human-safe robots. In this study, a novel two degrees-of-freedom safe manipulator is presented. The manipulator uses magneto-rheological fluid-based actuators. Magneto-rheological actuators offer low inertia-to-torque and mass-to-torque ratios which support their applications in human-friendly actuation. As a key element in the design of the manipulator, bi-directional actuation is attained by antagonistically coupling MR actuators at the joints. Antagonistically coupled MR actuators at the joints allow using a single motor to drive multiple joints. The motor is located at the base of the manipulator in order to further reduce the overall weight of the robot. Due to the unique characteristic of MR actuators, intrinsically safe actuation is achieved without compromising high quality actuation. Despite these advantages, modeling and control of MR actuators present some challenges. The antagonistic configuration of MR actuators may result in limit cycles in some cases when the actuator operates in the position control loop. To study the possibility of limit cycles, describing function method is employed to obtain the conditions under which limit cycles may occur in the operation of the system. Moreover, a connection between the amplitude and the frequency of the potential limit cycles and the system parameters is established to provide an insight into the design of the actuator as well as the controller. MR actuators require magnetic fields to control their output torques. The application of magnetic field however introduces hysteresis in the behaviors of MR actuators. To this effect, an adaptive model is developed to estimate the hysteretic behavior of the actuator. The effectiveness of the model is evaluated by comparing its results with those obtained using the Preisach model. These results are then extended to an adaptive control scheme in order to compensate for the effect of hysteresis. In both modeling and control, stability of proposed schemes are evaluated using Lyapunov method, and the effectiveness of the proposed methods are validated with experimental results

Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining

This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern

A spatial impedance controller for robotic manipulation

Mechanical impedance is the dynamic generalization of stiffness, and determines interactive behavior by definition. Although the argument for explicitly controlling impedance is strong, impedance control has had only a modest impact on robotic manipulator control practice. This is due in part to the fact that it is difficult to select suitable impedances given tasks. A spatial impedance controller is presented that simplifies impedance selection. Impedance is characterized using ¿spatially affine¿ families of compliance and damping, which are characterized by nonspatial and spatial parameters. Nonspatial parameters are selected independently of configuration of the object with which the robot must interact. Spatial parameters depend on object configurations, but transform in an intuitive, well-defined way. Control laws corresponding to these compliance and damping families are derived assuming a commonly used robot model. While the compliance control law was implemented in simulation and on a real robot, this paper emphasizes the underlying theor

Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators

In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Trajectory generation of space telerobots

The purpose is to review a variety of trajectory generation techniques which may be applied to space telerobots and to identify problems which need to be addressed in future telerobot motion control systems. As a starting point for the development of motion generation systems for space telerobots, the operation and limitations of traditional path-oriented trajectory generation approaches are discussed. This discussion leads to a description of more advanced techniques which have been demonstrated in research laboratories, and their potential applicability to space telerobots. Examples of this work include systems that incorporate sensory-interactive motion capability and optimal motion planning. Additional considerations which need to be addressed for motion control of a space telerobot are described, such as redundancy resolution and the description and generation of constrained and multi-armed cooperative motions. A task decomposition module for a hierarchical telerobot control system which will serve as a testbed for trajectory generation approaches which address these issues is also discussed briefly