Efficient control of an autonomous underwater vehicle while accounting for thruster failure

This paper is concerned with the design and implementation of control strategies onto a test-bed vehicle with six degrees-of-freedom. We design our trajectories to be efficient in time and in power consumption. Moreover, we also consider cases when actuator failure can arise and discuss alternate control strategies in this situation. Our calculations are supplemented by experimental results.

A first extension of geometric control theory to underwater vehicles

This paper serves as a first study on the implementation of control strategies developed using a kinematic reduction onto test bed autonomous underwater vehicles (AUVs). The equations of motion are presented in the framework of differential geometry, including external dissipative forces, as a forced affine connection control system. We show that the hydrodynamic drag forces can be included in the affine connection, resulting in an affine connection control system. The definitions of kinematic reduction and decoupling vector field are thus extended from the ideal fluid scenario. Control strategies are computed using this new extension and are reformulated for implementation onto a test-bed AUV. We compare these geometrically computed controls to time and energy optimal controls for the same trajectory which are computed using a previously developed algorithm. Through this comparison we are able to validate our theoretical results based on the experiments conducted using the time and energy efficient strategies.

Hydrodynamic and thruster model validation for autonomous underwater vehicles

From Pontryagin’s Maximum Principle to the Duke Kahanamoku Aquatic Complex; we develop the theory and generate implementable time efficient trajectories for a test-bed autonomous underwater vehicle (AUV). This paper is the beginning of the journey from theory to implementation. We begin by considering pure motion trajectories and move into a rectangular trajectory which is a concatenation of pure surge and pure sway. These trajectories are tested using our numerical model and demonstrated by our AUV in the pool. In this paper we demonstrate that the above motions are realizable through our method, and we gain confidence in our numerical model. We conclude that using our current techniques, implementation of time efficient trajectories is likely to succeed.

A Taxonomy of image matching techniques for stereo vision

Stereo vision is a method of depth perception, in which depth information is inferred from two (or more) images of a scene, taken from different perspectives. Applications of stereo vision include aerial photogrammetry, autonomous vehicle guidance, robotics, industrial automation and stereomicroscopy. A key issue in stereo vision is that of image matching, or identifying corresponding points in a stereo pair. The difference in the positions of corresponding points in image coordinates is termed the parallax or disparity. When the orientation of the two cameras is known, corresponding points may be projected back to find the location of the original object point in world coordinates. Matching techniques are typically categorised according to the nature of the matching primitives they use and the matching strategy they employ. This report provides a detailed taxonomy of image matching techniques, including area based, transform based, feature based, phase based, hybrid, relaxation based, dynamic programming and object space methods. A number of area based matching metrics as well as the rank and census transforms were implemented, in order to investigate their suitability for a real-time stereo sensor for mining automation applications. The requirements of this sensor were speed, robustness, and the ability to produce a dense depth map. The Sum of Absolute Differences matching metric was the least computationally expensive; however, this metric was the most sensitive to radiometric distortion. Metrics such as the Zero Mean Sum of Absolute Differences and Normalised Cross Correlation were the most robust to this type of distortion but introduced additional computational complexity. The rank and census transforms were found to be robust to radiometric distortion, in addition to having low computational complexity. They are therefore prime candidates for a matching algorithm for a stereo sensor for real-time mining applications. A number of issues came to light during this investigation which may merit further work. These include devising a means to evaluate and compare disparity results of different matching algorithms, and finding a method of assigning a level of confidence to a match. Another issue of interest is the possibility of statistically combining the results of different matching algorithms, in order to improve robustness.

Development of an autonomous helicopter for aerial powerline inspections

Autonomous mini-helicopters have been seen as a viable option for aerial-based powerline inspections, however there are numerous research and engineering challenges in developing a system capable of achieving this task in a dependable manner. We have developed an autonomous helicopter as a research platform which will allow us to demonstrate proof-of-concept capabilities for powerline inspections. Through numerous development cycles and from flight test experience we have gained insights into the key challenges in this area. We discuss these insights, describe the helicopter platform and present our research progress in the area of obstacle avoidance for mini-helicopters.

Empirical evaluation of an autonomous vehicle in an urban environment

Operation in urban environments creates unique challenges for research in autonomous ground vehicles. Due to the presence of tall trees and buildings in close proximity to traversable areas, GPS outage is likely to be frequent and physical hazards pose real threats to autonomous systems. In this paper, we describe a novel autonomous platform developed by the Sydney-Berkeley Driving Team for entry into the 2007 DARPA Urban Challenge competition. We report empirical results analyzing the performance of the vehicle while navigating a 560-meter test loop multiple times in an actual urban setting with severe GPS outage. We show that our system is robust against failure of global position estimates and can reliably traverse standard two-lane road networks using vision for localization. Finally, we discuss ongoing efforts in fusing vision data with other sensing modalities.

Error analysis and attitude observability of a monocular GPS/visual odometry integrated navigation filter

In this paper, we present a method for the recovery of position and absolute attitude (including pitch, roll and yaw) using a novel fusion of monocular Visual Odometry and GPS measurements in a similar manner to a classic loosely-coupled GPS/INS error state navigation filter. The proposed filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. An observability analysis of the proposed filter is performed, showing that the scale factor, position and attitude errors are fully observable under acceleration that is non-parallel to velocity vector in the navigation frame. The observability properties of the proposed filter are demonstrated using numerical simulations. We conclude the article with an implementation of the proposed filter using real flight data collected from a Cessna 172 equipped with a downwards-looking camera and GPS, showing the feasibility of the algorithm in real-world conditions.

Development of an autonomous unmanned aerial system to collect time-stamped samples from the atmosphere and localize potential pathogen sources

This paper presents the hardware development and testing of a new concept for air sampling via the integration of a prototype spore trap onboard an unmanned aerial system (UAS).We propose the integration of a prototype spore trap onboard a UAS to allow multiple capture of spores of pathogens in single remote locations at high or low altitude, otherwise not possible with stationary sampling devices.We also demonstrate the capability of this system for the capture of multiple time-stamped samples during a single mission.Wind tunnel testing was followed by simulation, and flight testing was conducted to measure and quantify the spread during simulated airborne air sampling operations. During autonomous operations, the onboard autopilot commands the servo to rotate the sampling device to a new indexed location once the UAS vehicle reaches the predefined waypoint or set of waypoints (which represents the region of interest). Time-stamped UAS data are continuously logged during the flight to assist with analysis of the particles collected. Testing and validation of the autopilot and spore trap integration, functionality, and performance is described. These tools may enhance the ability to detect new incursions of spores

Which way from here? The spatiality of maps & navigation in first-person single-player video games

Player experience of spatiality in first-person, single-player games is informed by the maps and navigational aids provided by the game. This project uses textual analysis to examine the way these maps and navigational aids inform the experience of spatiality in Fallout 3, BioShock and BioShock 2. Spatiality is understood as trialectic, incorporating perceived, conceived and lived space, drawing on the work of Henri Lefebvre and Edward Soja. The most prominent elements of the games’ maps and navigational aids are analysed in terms of how they inform players’ experience of the games’ spaces. In particular this project examines the in-game maps these games incorporate, the waypoint navigation and fast-travel systems in Fallout 3, and the guide arrow and environmental cues in the BioShock games.

Implementable efficient time and energy consumption trajectories design for an autonomous underwater vehicle

In this paper we consider the implementation of time and energy efficient trajectories onto a test-bed autonomous underwater vehicle. The trajectories are losely connected to the results of the application of the maximum principle to the controlled mechanical system. We use a numerical algorithm to compute efficient trajectories designed using geometric control theory to optimize a given cost function. Experimental results are shown for the time minimization problem.

The RatSLAM project : robot spatial navigation

Rats are superior to the most advanced robots when it comes to creating and exploiting spatial representations. A wild rat can have a foraging range of hundreds of meters, possibly kilometers, and yet the rodent can unerringly return to its home after each foraging mission, and return to profitable foraging locations at a later date (Davis, et al., 1948). The rat runs through undergrowth and pipes with few distal landmarks, along paths where the visual, textural, and olfactory appearance constantly change (Hardy and Taylor, 1980; Recht, 1988). Despite these challenges the rat builds, maintains, and exploits internal representations of large areas of the real world throughout its two to three year lifetime. While algorithms exist that allow robots to build maps, the questions of how to maintain those maps and how to handle change in appearance over time remain open. The robotic approach to map building has been dominated by algorithms that optimise the geometry of the map based on measurements of distances to features. In a robotic approach, measurements of distance to features are taken with range-measuring devices such as laser range finders or ultrasound sensors, and in some cases estimates of depth from visual information. The features are incorporated into the map based on previous readings of other features in view and estimates of self-motion. The algorithms explicitly model the uncertainty in measurements of range and the measurement of self-motion, and use probability theory to find optimal solutions for the geometric configuration of the map features (Dissanayake, et al., 2001; Thrun and Leonard, 2008). Some of the results from the application of these algorithms have been impressive, ranging from three-dimensional maps of large urban strucutures (Thrun and Montemerlo, 2006) to natural environments (Montemerlo, et al., 2003).

Towards lifelong navigation and mapping in an office environment

This paper addresses the challenge of developing robots that map and navigate autonomously in real world, dynamic environments throughout the robot’s entire lifetime – the problem of lifelong navigation. Static mapping algorithms can produce highly accurate maps, but have found few applications in real environments that are in constant flux. Environments change in many ways: both rapidly and gradually, transiently and permanently, geometrically and in appearance. This paper demonstrates a biologically inspired navigation algorithm, RatSLAM, that uses principles found in rodent neural circuits. The algorithm is demonstrated in an office delivery challenge where the robot was required to perform mock deliveries to goal locations in two different buildings. The robot successfully completed 1177 out of 1178 navigation trials over 37 hours of around the clock operation spread over 11 days.

Using incomplete online metric maps for topological exploration with the Gap Navigation Tree

This paper presents a general, global approach to the problem of robot exploration, utilizing a topological data structure to guide an underlying Simultaneous Localization and Mapping (SLAM) process. A Gap Navigation Tree (GNT) is used to motivate global target selection and occluded regions of the environment (called “gaps”) are tracked probabilistically. The process of map construction and the motion of the vehicle alters both the shape and location of these regions. The use of online mapping is shown to reduce the difficulties in implementing the GNT.