Robotic grasping and manipulation controller framework: architecture redevelopment

This paper details the further improvements obtained by redesigning a previously offered Manipulation Controller Framework to provide support to an innovative, friction-based object slippage detection strategy employed by the robotic object manipulator. This upgraded Manipulation Controller Framework includes improved slippage detection functionality and a streamlined architecture designed to improve controller robustness, reliability and speed. Improvements include enhancements to object slippage detection strategy, the removal of the decision making module and integration of its functionality into the Motion Planner, and the stream-lining of the Motion Planner to improve its effectiveness. It is anticipated that this work will be useful to researchers developing integrated robot controller architectures and slippage control.

Optimal sensing requirement for slippage prevention in robotic grasping

This paper presents a new theoretical development and modelling related to the requirement of the minimum number of sensors necessary for slippage prevention in robotic grasping. A fundamental experimental investigation has been conducted to support the newly developed postulate. A series of basic experiments proved that it is possible to evaluate the contributions of various sensors to slippage prevention and control in robotic grasping. The use of three discrete physical sensors, one for each of the three sensing functions (normal, tangential and slippage), has been proven to be the most reliable combination for slippage prevention in robotic grasping. It was also proven that the best performance from a two-sensor combination can be achieved when normal grasp force and tangential force are both monitored in the grasping process.

Robotic object grasping in context of human grasping and manipulation

This paper presents experimental and deductive findings that shed new light on grasp force estimation, which improves robot’s chances to grasp and manipulate the object close to optimum conditions on the first attempt, which in turn improves robot’s object manipulation dexterity.
This paper proposes that object slippage detection in the human hand is not detected based purely on microvibrations sensed by the human skin during incipient slippage but also on load sensing at each finger and movement of fingers relative to each other while holding an object.

Object slippage prevention using different control strategies

This paper presents experimental results obtained using a friction-based slippage and tangential force sensing device that has been developed for the purpose of reliable object slippage prevention during robotic manipulation. The experimental results obtained demonstrate that the developed slippage sensing strategy is rugged and reliable even in its current “rough prototype” state of development. This work has the potential to yield a low cost and highly customisable slippage and tangential force sensing device for a variety of robotic object grasping and manipulation applications. It is envisaged that the work presented here will be beneficial to researchers in the area of object slippage prevention.

An active stereo vision-based learning approach for robotic tracking, fixating and grasping control

In this paper, an active stereo vision-based learning approach is proposed for a robot to track, fixate and grasp an object in unknown environments. First, the functional mapping relationships between the joint angles of the active stereo vision system and the spatial representations of the object are derived and expressed in a three-dimensional workspace frame. Next, the self-adaptive resonance theory-based neural networks and the feedforward neural networks are used to learn the mapping relationships in a self-organized way. Then, the approach is verified by simulation using the models of an active stereo vision system which is installed in the end-effector of a robot. Finally, the simulation results confirm the effectiveness of the present approach.

Multipoint haptic mediator interface for robotic teleoperation

Despite recent advances in artificial intelligence and autonomous robotics, teleoperation can provide distinct benefits in applications requiring real-time human judgement and intuition. However, as robotic systems are increasingly becoming sophisticated and are performing more complex tasks, realizing these benefits requires new approaches to teleoperation. This paper introduces a novel haptic mediator interface for teleoperating mobile robotic platforms that have a variety of manipulators and functions. Identical master-slave bilateral teleoperation of the robotic manipulators is achieved by representing them in virtual reality and by allowing the operator to interact with them using a multipoint haptic device. The operator is also able to command motions to the mobile platform by using a novel haptic interaction metaphor rather than a separate dedicated input device. The presented interaction techniques enable the operator to perform a wide range of control functions and achieve functionality similar to that of conventional teleoperation schemes that use a single haptic interface. The mediator interface is presented, and important considerations such as workspace mapping and scaling are discussed. © 2015 IEEE.

Force measurement capability for robotic assisted minimally invasive surgery systems

An automated laparoscopic instrument capable of non-invasive measurement of tip/tissue interaction forces for direct application in robotic assisted minimally invasive surgery systems_ is introduced in this paper. It has the capability to measure normal grasping forces as well as lateral interaction forces without any sensor mounted on the tip jaws. Further to non-invasive actuation of the tip, the proposed instrument is also able to change the grasping direction during surgical operation. Modular design of the instrument allows conversion between surgical modalities (e.g., grasping, cutting, and dissecting). The main focus of this paper is on evaluation of the grasping force capability of the proposed instrument. The mathematical formulation of fenestrated insert is presented and its non-linear behaviour is studied. In order to measure the stiffness of soft tissues, a device was developed that is also described in this paper. Tissue characterisation experiments were conducted and results are presented and analysed here. The experimental results verify the capability of the proposed instrument in accurately measuring grasping forces and in characterising artificial tissue samples of varying stiffness.

Soft tissue characterisation using a force feedback-enabled instrument for robotic assisted minimally invasive surgery systems

An automated laparoscopic instrument capable of non-invasivemeasurement of tip/tissue interaction forces for direct application in robotic assisted minimally invasive surgery systems is introduced in this chapter. It has the capability to measure normal grasping forces as well as lateral interaction forces without any sensor mounted on the tip jaws. Further to non-invasive actuation of the tip, the proposed instrument is also able to change the grasping direction during surgical operation. Modular design of the instrument allows conversion between surgical modalities (e.g., grasping, cutting, and dissecting). The main focus of thispaper is on evaluation of the grasping force capability of the proposed instrument. The mathematical formulation of fenestrated insert is presented and its non-linear behaviour is studied. In order to measure the stiffness of soft tissues, a device was developed that is also described in this chapter. Tissue characterisation experiments were conducted and results are presented and analysed here. The experimental results verify the capability of the proposed instrument in accurately measuring grasping forces and in characterising artificial tissue samples of varying stiffness.

Object grasping and safe manipulation using friction-based sensing.

This project provides a solution for slippage prevention in industrial robotic grippers for the purpose of safe object manipulation. Slippage sensing is performed using novel friction-based sensors, with customisable slippage sensitivity and complemented by an effective slippage prediction strategy. The outcome is a reliable and affordable slippage prevention technology.

Foot-targeting in reaching and grasping

Foot positioning was investigated when right-handed (Experiment One) and left handed adults (Experiment Two) stopped walking to grasp a stationary 70 mm ball at shoulder height. In both experiments centroid location formed by the toe and heel coordinates relative to the object was highly consistent within a target-location condition, demonstrating a foot-targeting phenomenon. Centroid location in the anterior-posterior direction was uninfluenced by grasping hand but the centroid shifted right for left hand grasps and left for right hand grasps. With the target either centrally located or on the same side as the dominant hand, foot positioning brought the grasping hand closer to the target in the medial-lateral direction. When the target object was aligned with the shoulder opposite the dominant hand both groups adopted foot positions to the left of the target. Thus, neither group adopted optimal foot position when the target was located opposite their dominant hand. Foot orientation angle relative to the target was also influenced by choice of grasping hand. Collectively, the findings demonstrate a close association between grasping hand and foot position when approaching to reach and grasp an object but also suggest that foot-dominance may influence medial-lateral centroid location.