Steven Scheding

An experiment in autonomous navigation of an underground mining vehicle

Describes the theoretical development and experimental evaluation of a navigation system for an autonomous load, haul, and dump truck based on the results obtained during extensive in-situ field trials. The particular contributions of the theoretical work are in designing the navigation system to be able to cope with vehicle slip in rough uneven terrain using information from inertial sensors, odometry, and a bearing only laser. Results are presented using data obtained during the field trials.

Development and application of millimeter wave radar sensors for underground mining

This paper defines the issues that are required for the development of a successful underground range measurement sensor. It considers various options, including laser and sonar implementations, before focusing on a millimeter-wave frequency modulated continuous wave radar. The implementations of radar sensors for simple ranging and three-dimensional cavity profiling are then discussed before some data obtained in underground mines is presented to verify the radar performance through thick dust and vapor.

Dynamic Modelling of Wheel-Terrain Interaction of a UGV

Understanding the vehicle-terrain interaction is essential for autonomous and safe operations of skid-steering unmanned ground vehicles (UGVs). This paper presents a comprehensive analysis of the dynamic processes involved in this interaction, using the vehicle kinetics and the theory of terramechanics to derive systematically shear displacement, reaction force, and load distribution for a wheel. The new model is then summarized in the form of an algorithm to allow for computation of characteristic performance of the interaction such as slip ratios, rolling resistance, and moment of turning resistance for a number of terrain types. Given the current state of the vehicle and terrain parameters, the model can be used to estimate its next states and to predict the vehicle running path. The development is illustrated by simulation and verified with experimental data.

Control Issues of an Autonomous Vehicle

This paper addresses some control issues of a robotic amphibious vehicle that can serve as a general framework for automation of tractors used in construction. These include the vehicles low-level dynamic equations, the development of its braking control system, kinematics in interactions with ground and the slip problem. Simulation and real-time results to date are presented. Index Terms—Unmanned Ground Vehicle, dynamic and kinematic modelling, skid-steering, sliding mode control, slip

Frequency domain modeling of aided GPS for vehicle navigation systems

Position information P obtained from standard global positioning system (GPS) receivers is known to be corrupted with colored (time-correlated) noise. To make effective use of GPS information in a navigation system it is essential to model this colored noise and to incorporate additional sensing to de-correlate and eliminate its effect. In this paper frequency domain techniques are employed to generate a model for GPS noise sources. This model shows clearly what type and combination of additional sensor information is necessary to de-correlate GPS errors and to make best use of position information in navigation tasks. The frequency-domain methodology proposed has wider application in the design of sensor suites for high-performance navigation systems. Experimental results are presented demonstrating the method in fusing standard GPS latitude and longitude information with information from a velocity sensor.

Kalman filtering design techniques for aided GPS land navigation applications

Position information obtained from standard GPS receivers is known to be corrupted with coloured (time-correlated) noise. To make effective use of GPS information in a navigation system it is essential to model this coloured noise and to incorporate additional sensing to de-correlate and eliminate its effect. In this paper frequency domain techniques are employed to generate a model for GPS noise sources. This model shows clearly what type and combination of additional sensor information is necessary to de-correlate GPS errors and to make best use of position information in navigation tasks. The frequency-domain methodology proposed has wider application in the design of sensor suites for high-performance navigation systems. Experimental results are presented demonstrating the method in fusing standard GPS latitude and longitude information with information from a velocity sensor.

A heuristic rule-based switching and adaptive PID controller for a large autonomous tracked vehicle: from development to implementation

An innovative and effective algorithm to control the speed and heading of a large tracked vehicle is presented. This is a part of a larger control system that converts a manual driving vehicle into a computer controlled platform to perform autonomous functions in unstructured jungle-like terrains. Heuristic rule-based switching and adaptive PID control methods are used in this algorithm. The control system has been physically implemented and extensive field trials proved that the algorithm is robust and effective with excellent performance under various terrain conditions.

Stochastic collection and replenishment (SCAR): Objective functions

This paper introduces two objective functions for computing the expected cost in the Stochastic Collection and Replenishment (SCAR) scenario. In the SCAR scenario, multiple user agents have a limited supply of a resource that they either use or collect, depending on the scenario. To enable persistent autonomy, dedicated replenishment agents travel to the user agents and replenish or collect their supply of the resource, thus allowing them to operate indefinitely in the field. Of the two objective functions, one uses a Monte Carlo method, while the other uses a significantly faster analytical method. Approximations to multiplication, division and inversion of Gaussian distributed variables are used to facilitate propagation of probability distributions in the analytical method when Gaussian distributed parameters are used. The analytical objective function is shown to have greater than 99% comparison accuracy when compared with the Monte Carlo objective function while achieving speed gains of several orders of magnitude.

Fault Detection in the Frequency Domain: Designing Reliable Navigation Systems

This paper provides an analysis of Kaiman filter based systems with respect to fault detection. By using frequency domain techniques, a metric is developed that describes the detectability of a fault by showing how a sensor fault is transmitted to the filter innovations (if at all). To increase detectability, it is shown that redundancy must be employed, and that unlike sensors should be used. Examples are provided throughout to illustrate the various concepts.

Applying Gaussian distributed constraints to Gaussian distributed variables

This paper develops an analytical method of truncating inequality constrained Gaussian distributed variables where the constraints are themselves described by Gaussian distributions. Existing truncation methods either assume hard constraints, or use numerical methods to handle uncertain constraints. The proposed approach introduces moment-based Gaussian approximations of the truncated distribution. This method can be applied to numerous problems, with the motivating problem being Kalman filtering with uncertain constraints. In a simulation example, the developed method is shown to outperform unconstrained Kalman filtering by over 40% and hard-constrained Kalman filtering by over 17%.