David J. Austin

High-level control of a mobile manipulator for door opening

In this paper, off-the-shelf algorithms for force/torque control are used in the context of mobile manipulation, in particular, the task of opening a door is studied. To make the solution robust, as few assumptions as possible are made. By using relaxation of forces as the basic level of control more complex information can be derived from the resulting motion. In our system, the radius and centre of rotation of the door are estimated online. This enables the complete system to have a higher degree of autonomy in an unknown environment. In addition, the redundancy of the robot is exploited in such a way to drive the system towards a desired configuration. The framework of hybrid dynamic systems is used to implement the algorithm which gives a theoretically sound framework for analysing the system with respect to safety and functionality. The integration of the above approaches results in a system which can robustly locate and grasp the handle and then open the door.

Feature based CONDENSATION for mobile robot localization

Much attention has been given to CONDENSATION methods for mobile robot localization. This has resulted in somewhat of a breakthrough in representing uncertainty for mobile robots. In this paper we use CONDENSATION with planned sampling as a tool for doing feature based global localization in a large and semi-structured environment. This paper presents a comparison of four different feature types: sonar based triangulation points and point pairs, as well as lines and doors extracted using a laser scanner. We show experimental results that highlight the information content of the different features, and point to fruitful combinations. Accuracy, computation time and the ability to narrow down the search space are among the measures used to compare the features. From the comparison of the features, some general guidelines are drawn for determining good feature types.

Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localization

A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robot location in the environment. Sensor data is assumed to be provided in the form of a Gaussian distribution over the space of robot poses. A tree of hypotheses is built, representing the possible data association histories for the system. Covariance intersection is used for the fusion of the Gaussians whenever a data association decision is taken. However, such a tree can grow without bound and so rules are introduced for the elimination of the least likely hypotheses from the tree and for the proper re-distribution of their probabilities. This technique is applied to a feature-based mobile robot localization scheme and experimental results are given demonstrating the effectiveness of the scheme.

DCA: a distributed control architecture for robotics

Many control applications are by nature distributed, not only over different processes but also over several processors. Managing such a system with respect to the startup of processes, internal communications and state changes quickly becomes a very complex task. The paper presents a distributed control architecture which supports a formal model of computation as described by Lyons and Arib (1989). The architecture is primarily intended for robot control but has a wide range of potential applications. We motivate the design and implementation of the architecture by discussing the desired properties of a robot system capable of doing real-time tasks like manipulation. This leads to functionality such as a process algebra controlling the life-cycle of the processes, grouping and distribution of processes and internal communication transparent to location. Our implementation does not in itself introduce any bottlenecks due to a tree structure with local control over processes which gives an efficient and scalable architecture. At the end, an example scenario in which a fairly advanced problem like opening a door using a mobile robot with a manipulator arm is demonstrated in the presented framework.

Hybrid topological/metric approach to SLAM

We present a new topological/metric approach to solving the simultaneous localisation and mapping problem. The map is represented as a graph - nodes are local map frames, and edges are transformations between adjacent map frames. The underlying local mapping algorithm is FastSLAM. The local maps and transformations are modelled by sets of particles. There is no global map frame, each maps uncertainties are restricted to its own map frame. The loop dosing is achieved via efficient map matching. We demonstrate our algorithm running in real-time in an indoor environment using a laser range sensor.

Fast sum of absolute differences visual landmark detector

This paper presents various optimisation that can be applied to the sum of absolute differences (SAD) correlation algorithm for automated landmark detection. This has applications in mobile robotic navigation and mapping. We show how some assumptions about the environment and the generic form of strong landmarks selected by the SAD correlation algorithm have led to the development of an algorithm to enable near real tune selection of strong landmarks from visual information. The landmarks that have been selected from a series of frames using our optimisation are shown to be stable through the image sequence, demonstration the scale invariance of the landmarks that are selected by the SAD correlation algorithm.

Geometric constraint identification and mapping for mobile robots

Abstract A new method of map building for mobile robots is presented. Recent developments have focused on grid-based mapping methods which suffer from the drawback of their size, requiring a great deal of memory and prohibiting the use of many path-planning algorithms. In contrast, geometric maps provide a compact alternative which facilitates path-planning. We propose a new method which identifies geometric models of the constraints imposed upon the robot by the environment. A rigorous approach is taken to the process of constraint identification, which is cast as a minimisation problem. A number of primitive geometric objects are used for constraint modelling including line segments, arc segments, cubic segments and, for three degree of freedom systems, polygonal planar patches. A number of operations are also defined which integrate new sensor readings into the existing model. Simulation results are presented for two and three degree of freedom systems, demonstrating the effectiveness of the constraint identification process. A comparative study is also presented which gives guidelines for the proper selection of primitives and operations.

Hybrid dynamic modeling and control of constrained manipulation systems

Discrete event systems are presented as a powerful framework for a large number of robot control tasks. This paper presents a general description of the discrete event modeling and control synthesis for robot manipulation. Additionally, methods for the effective monitoring of the process based on the detection and identification of discrete events are given. The effectiveness and versatility of the approach are demonstrated through a wide variety of experiments. Applications are demonstrated in assembly, online training of robots, advanced perception capabilities, human-robot shared control and the understanding of human manipulation skills.

Mobile robotics in the long term-exploring the fourth dimension

Explores the issues involved in deployment of mobile robots in real-world situations and presents solutions and approaches under development at the Australian National University. For deployment of mobile robots outside of the laboratory, long-term operation is required. Hence, we have developed an automatic recharging system. In addition, a Web-based teleoperation system is used to provide missions to test the long-term reliability of the robot. The final aspect of real-world operation that is explored here is operations in dynamic environments. To date, researchers have assumed static environments for mapping and localisation. We propose methods to avoid this restriction.

A bearing-only control law for stable docking of unicycles

This paper proposes a new control method for stabilising control design for docking unicycle-like vehicles based on bearing-only information. An omni-directional panoramic camera is used to detect visual targets around the docking station and provides bearing (or heading) data for each observed landmark. The convergence of the controlled system is fully analysed and simulations are provided to demonstrate the ideal behaviour of the system. A robust and computationally cheap blob detection algorithm is proposed and results are provided to demonstrate its performance in extracting targets from cluttered scenes. Experimental results are presented demonstrating the performance of the algorithm on the ANU Nomadic Technologies Nomad XR4000 robot.