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.

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.

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.

Haptic control of multi-axis robotic systems

Control of tele-operated remote robot’s is nothing new; the public was introduced to this 'new' field in 1986 when the Chernobyl cleanup began. Pictures of weird and wonderful robotic workers pouring concrete or moving rubble flooded the world. Integration of force feedback or 'haptics' to remote robot's is a new development and one that is likely to make a big difference in man-machine interaction. Development of haptic capable tele-operation schema is a challenge. Often platform specific software is developed for one off tasks. This research focussed on the development of an open software platform for haptic control of multiple remote robotic platforms. The software utilises efficient server/client architecture for low data latency, while efficiently performing required kinematic transforms and data manipulation in real time. A description of the algorithm, software interface and hardware is presented in this paper. Preliminary results are encouraging as haptic control has been shown to greatly enhances remote positioning tasks.

Next generation robotic counter IED technology : A step change in capability

Improvised Explosive Devices (IEDs) are reported as the number one cause of injury and death for allied troops in the current theater of operation. Deakin University’s Centre for Intelligent Systems Research (CISR) is working on next-generation technology to combat the threat. In 2006 CISR was awarded funding through the Capability and Technology Demonstrator (CTD) Program managed by the Australian Defence Force. The objective was to investigate the use of haptics or force feedback technology for Counter-IED (CIED) tasks. Over the past six years, engineers from CISR have worked alongside Defence stakeholders to develop a series of robotic platforms designed to immerse a soldier in the remote environment. Utilising a natural user interface, haptic force feedback and stereovision, the technology has undergone initial trials in Sydney, Canberra, Woomera and at the CISR testing facility in Geelong, Australia. The technology has proved popular among operators allowing them increased fidelity and manipulation speed while significantly reducing required training. CISR has a history of rapidly delivering technology to meet the needs of police and law enforcement in Australia. The OzBot™ series of robots developed in conjunction with the Victorian Police is currently in service and used extensively for hostage negotiation and first responder roles. The CISR robotics group works on technologies that reduce operator fatigue, minimise training liability and maintenance. Over 55 engineers develop simulation environments for increased training availability and continuous improvement to the current range of mobile platforms, including communications range, payload, manipulator reach and capability. This paper describes a number of the technologies, methods and systems developed by CISR for IED neutralisation, with the aim to increasing military awareness of Australian capability.

Effects of realistic force feedback in a robotic assisted minimally invasive surgery system

Robotic assisted minimally invasive surgery systems not only have the advantages of traditional laparoscopic procedures but also restore the surgeon's hand-eye coordination and improve the surgeon's precision by filtering hand tremors. Unfortunately, these benefits have come at the expense of the surgeon's ability to feel. Several research efforts have already attempted to restore this feature and study the effects of force feedback in robotic systems. The proposed methods and studies have some shortcomings. The main focus of this research is to overcome some of these limitations and to study the effects of force feedback in palpation in a more realistic fashion.

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.

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.

Multi-agent model for robotic assembly system

In this paper, a multi-agent based model for a robotic assembly system is presented. Firstly, an organization model is used to construct the multi-agent model. Secondly, a dynamic self-organizing method is then put forward for the multi-agent robotic system to bid and contract the operations. Thirdly, a real multi-agent robotic system is built and assembly experiments are carried out. Finally, the experimental results confirm that the present multi-agent robotic system has flexibility, adaptation and stability.

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.

Habitat manipulation and rodent damage control: reducing rodent damage in Australian macadamia orchards

This paper examines the relationship between adjacent non-crop vegetation and rodent (Rattus rattus) damage in Australian macadamia (Macadamia integrifolia) orchard systems. Orchards adjacent to structurally diverse, non-crop vegetation dominated by woody weeds exhibited significantly higher damage when compared to orchards adjacent to managed grasslands. This relationship formed the basis for a rodent damage reduction strategy utilising habitat manipulation. Structurally diverse, non-crop habitats were modified to grasslands leading to a reduction in rodent damage of 65%. This strategy was cost-effective and has the potential to be long-term with minimal effort needed to maintain sites in a modified state. Habitat manipulation is a process whereby the resource load in a system is reduced and hence rodent densities cannot reach levels where they cause significant crop damage. This paper provides empirical evidence to support habitat manipulation as a practical, cost-effective control strategy for rodent pests.