Kinematic and dynamic modeling of a robotic head with linear motors

Our present research focuses on kinematic and dynamic modeling of a 3-DOF robotic cutting head for the next generation of CNC machines. The robotic cutting head is one kind of parallel manipulator of 3-PUU type, which has a high flexibility of motion in three-dimensional space. The parallel manipulator consists of three linear servomotors, which drive three connecting rods independently according to the cutting strategy. Being a parallel manipulator, the robotic cutting head has higher stiffness and position accuracy; consequently, higher velocities and accelerations can be achieved. A very suitable application of this mechanism is as a cutting head of a precision machine tool for three-dimensional cutting problems.

Solution to robotic landmine detection through use of path planning and motor control

Low cost robotic detectors are a promising new approach to combat the disturbing landmine crisis. In this paper a low-cost robotic solution is proposed, we present several control techniques used to improve the precision of the robotic motion. A P and PD controller is applied, and it is concluded that a cascaded control system provides a more stable and accurate response. Two search patterns for landmine detection are considered, rectangular and spiral, these are used to analyse the effectiveness of the control system.

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.

Automated robotic grinding by low-powered manipulator

A new robotic grinding process has been developed for a low-powered robot system using a spring balancer as a suspension system. To manipulate a robot-arm in the vertical plane, a large actuator torque is required due to the tool weight and enormous gravity effect. But the actuators of the robot system always exhibit a limited torque capacity. This paper presents a cheap and available system for precise grinding tasks by a low-powered robot system using a suspension system. For grinding operations, to achieve position and force-tracking simultaneously, this paper presents an algorithm of the hybrid position/force-tracking scheme with respect to the dynamic behavior of a spring balancer. Material Removal Rate (MRR) is developed for materials SS400 and SUS304. Simulations and experiments have been carried out to demonstrate the feasibility of the proposed system.

Stereo-vision-based moving object tracking via robust linear filtering

Vision-based tracking sensors typically provide nonlinear measurements
of the targets Cartesian position and velocity state components. In this paper we derive linear measurements using an analytical measurement conversion technique which can be used with two (or more) vision sensors. We derive
linear measurements in the target’s Cartesian position and velocity components and we derive a robust version of a linear Kalman filter. We show that our linear robust filter significantly outperforms the extended Kalman Filter. Moreover, we prove that the state estimation error is bounded.

Vision based target tracking using robust linear filtering

The use of perspective projection in tracking a target from a video stream involves nonlinear observations. The target dynamics, however, are modeled in Cartesian coordinates and result in a linear system. In this paper we provide a robust version of a linear Kalman filter and perform a measurement conversion technique on the nonlinear optical measurements. We show that our linear robust filter significantly outperforms the Extended Kalman Filter. Moreover, we prove that the state estimation error is bounded in a probabilistic sense.

OzBot TM - Haptic augmentation of a teleoperated robotic platform for search and rescue operations

A continued increase in computing power, sensor capability, software functionality, immersive interfaces and hardware modularity has given robot designers seemingly endless potential in the area of mobile robotics.  While some mobile robotic system designers are focusing on expensive, full-featured platforms, developers are realising the advantages of emerging technology in providing small, low-cost mobile reconnaissance vehicles as expendable teleoperated robotic systems.  The OzBot^TM mobile reconnaissance platform presents one such system.  The design objectives of the OzBot^TM platform focus on the development of inexpensive, lightweight carry-case sized robots for search and rescue operations, law enforcement scenarios and hazardous environment inspection.  The incorporation of Haptic augmentation provides the teleoperator with improved task immersion for an outdoor search and rescue scenario.  Achieved in cooperation with law enforcement agencies within Australia, this paper discusses the performance of the first four revisions of the OzBot^TM platform.

Depth estimation using multi-wavelet analysis based stereo vision approach

The problem of dimensional defects in aluminum die- casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic, with highly reflective, nature of aluminum die-castings, it is extremely hard to estimate true dimensionality of the die-casting, autonomously. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a stereo cameras pair and a simple fluorescent light. The developed system is capable of estimating surface depths within the tolerance of 1.5 mm. Moreover, the system is invariant to illuminative variations and orientation of the objects in the input image space, which makes the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup.

Simultaneous state and input estimation with application to a two-link robotic system

This paper addresses the problem of estimating simultaneously the state and input of a nonlinear system with application to a two link robotic manipulator - the Pendubot. The system nonlinearity comprises a Lipschitz function with respect to the state, and a nonlinear term which is a function of both the state and input. It is shown that under some conditions, an observer can be designed to estimate simultaneously the system’s state and input. Simulation and experimental results, obtained around the inverted equilibrium position, are presented to demonstrate the validity of the approach.

Observer-based decentralized approach to robotic formation control

Control of a group of mobile robots in a formation requires not only environmental sensing but also communication among vehicles. Enlarging the size of the platoon of vehicles causes difficulties due to communications bandwidth limitations. Decentralized control may be an appropriate approach in those cases when the states of all vehicles cannot be obtained in a centralized manner. This paper presents a solution to the problem of decentralized implementation of a global state-feedback controller for N mobile robots in a formation. The proposed solution is based on the design of functional observers to estimate asymptotically the global state-feedback control signals by using the corresponding local output information and some exogenous global functions. The proposed technique is tested through simulation and experiments for the control of groups of Pinoneer-based non-holonomic mobile robots.

Towards optimization of a real-world robotic-sensor system of systems

The problem of threat detection in an unstructured environment is considered. Three systems, comprising of robots and sensors, are proposed to form a system of systems (SoS) to find a solution to the problem. System interactions are defined to provide a framework for formulation as an SoS optimization problem. Different cost and objective functions are introduced for optimization of local criteria. Using different weights, a linear combination of the local cost and objective functions is obtained to propose a global objective function. An algorithm is suggested to find an optimum value for the global objective function leading towards optimization of the SoS.