Tactile Sensors for Friction Estimation and Incipient Slip Detection - Toward Dexterous Robotic Manipulation:A Review

Humans can handle and manipulate objects with ease; however, human dexterity has yet to be matched by artificial systems. Receptors in our fingers and hands provide essential tactile information to the motor control system during dexterous manipulation such that the grip force is scaled to the tangential forces according to the coefficient of friction. Likewise, tactile sensing will become essential for robotic and prosthetic gripping performance as applications move toward unstructured environments. However, most existing research ignores the need to sense the frictional properties of the sensor-object interface, which (along with contact forces and torques) is essential for finding the minimum grip force required to securely grasp an object. Here, we review this problem by surveying the field of tactile sensing from the perspective that sensors should: 1) detect gross slip (to adjust the grip force); 2) detect incipient slip (dependent on the frictional properties of the sensor-object interface and the geometries and mechanics of the sensor and the object) as an indication of grip security; or 3) measure friction on contact with an object and/or following a gross or incipient slip event while manipulating an object. Recommendations are made to help focus future sensor design efforts toward a generalizable and practical solution to sense, and hence control grip security. Specifically, we propose that the sensor mechanics should encourage incipient slip, by allowing parts of the sensor to slip while other parts remain stuck, and that instrumentation should measure displacement and deformation to complement conventional force, pressure, and vibration tactile sensing

Adaptive robust interaction control for low-cost robotic grasping

Robotic grasping is a challenging area in the field of robotics. When a gripper starts interacting with an object to perform a grasp, the mechanical properties of the object (stiffness and damping) will play an important role. A gripper which is stable in isolated conditions, can become unstable when coupled to an object. This can lead to the extreme condition where the gripper becomes unstable and generates excessive or insufficient grip force resulting in the grasped object either being crushed, or falling and breaking. In addition to the stability issue, grasp maintenance is one of the most important requirements of any grasp where it guarantees a secure grasp in the presence of any unknown disturbance. The term grasp maintenance refers to the reaction of the controller in the presence of external disturbances, trying to prevent any undesired slippage. To do so, the controller continuously adjusts the grip force. This is a challenging task as it requires an accurate model of the friction and object’s weight to estimate a sufficient grip force to stop the object from slipping while incurring minimum deformation. Unfortunately, in reality, there is no solution which is able to obtain the mechanical properties, frictional coefficient and weight of an object before establishing a mechanical interaction with it. External disturbance forces are also stochastic meaning they are impossible to predict. This thesis addresses both of the problems mentioned above by:Creating a novel variable stiffness gripper, capable of grasping unknown objects, mainly those found in agricultural or food manufacturing companies. In addition to the stabilisation effect of the introduced variable stiffness mechanism, a novel force control algorithm has been designed that passively controls the grip force in variable stiffness grippers. Due to the passive nature of the suggested controller, it completely eliminates the necessity for any force sensor. The combination of both the proposed variable stiffness gripper and the passivity based control provides a unique solution for the stable grasp and force control problem in tendon driven, angular grippers.Introducing a novel active multi input-multi output slip prevention algorithm. The algorithm developed provides a robust control solution to endow direct drive parallel jaw grippers with the capability to stop held objects from slipping while incurring minimum deformation; this can be done without any prior knowledge of the object’s friction and weight. The large number of experiments provided in this thesis demonstrate the robustness of the proposed controller when controlling parallel jaw grippers in order to quickly grip, lift and place a broad range of objects firmly without dropping or crushing them. This is particularly useful for teleoperation and nuclear decommissioning tasks where there is often no accurate information available about the objects to be handled. This can mean that pre-programming of the gripper is required for each different object and for high numbers of objects this is impractical and overly time-consuming. A robust controller, which is able to compensate for any uncertainties regarding the object model and any unknown external disturbances during grasping, is implemented. This work has advanced the state of the art in the following two main areas: Direct impedance modulation for stable grasping in tendon driven, angular grippers. Active MIMO slip prevention grasp control for direct drive parallel jaw grippers

Enhancing Upper Limb Prostheses Through Neuromorphic Sensory Feedback

Upper limb prostheses are rapidly improving in terms of both control and sensory feedback, giving rise to lifelike robotic devices that aim to restore function to amputees. Recent progress in forward control has enabled prosthesis users to make complicated grip patterns with a prosthetic hand and nerve stimulation has enabled sensations of touch in the missing hand of an amputee. A brief overview of the motivation behind the work in this thesis is given in Chapter 1, which is followed by a general overview of the field and state of the art research (Chapter 2). Chapters 3 and 4 look at the use of closed loop tactile feedback for improving prosthesis grasping functionality. This entails development of two algorithms for improving object manipulation (Chapter 3) and the first real-time implementation of neuromorphic tactile signals being used as feedback to a prosthesis controller for improved grasping (Chapter 4). The second half of the thesis (Chatpers 5 - 7) details how sensory information can be conveyed back to an amputee and how the tactile sensations can be utilized for creating a more lifelike prosthesis. Noninvasive electrical nerve stimulation was shown to provide sensations in multiple regions of the phantom hand of amputees both with and without targeted sensory reinnervation surgery (Chapter 5). A multilayered electronic dermis (e-dermis) was developed to mimic the behavior of receptors in the skin to provide, for the first time, sensations of both touch and pain back to an amputee and the prosthesis (Chapter 6). Finally, the first demonstration of sensory feedback as a key component of phantom hand movement for myoelectric pattern recognition shows that enhanced perceptions of the phantom hand can lead to improved prosthesis control (Chapter 7). This work provides the first demonstration of how amputees can perceive multiple tactile sensations through a neuromorphic stimulation paradigm. Furthermore, it describes the unique role that nerve stimulation and phantom hand activation play in the sensorimotor loop of upper limb amputees