Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.

A policy model for access control using building information models

Building information models have created a paradigm shift in how buildings are built and managed by providing a dynamic repository for building data that is useful in many new operational scenarios. This change has also created an opportunity to use building information models as an integral part of security operations and especially as a tool to facilitate fine-grained access control to building spaces in smart buildings and critical infrastructure environments. In this paper, we identify the requirements for a security policy model for such an access control system and discuss why the existing policy models are not suitable for this application. We propose a new policy language extension to XACML, with BIM specific data types and functions based on the IFC specification, which we call BIM-XACML.

Graph theory based representation of building information models for access control applications

Building information models are increasingly being utilised for facility management of large facilities such as critical infrastructures. In such environments, it is valuable to utilise the vast amount of data contained within the building information models to improve access control administration. The use of building information models in access control scenarios can provide 3D visualisation of buildings as well as many other advantages such as automation of essential tasks including path finding, consistency detection, and accessibility verification. However, there is no mathematical model for building information models that can be used to describe and compute these functions. In this paper, we show how graph theory can be utilised as a representation language of building information models and the proposed security related functions. This graph-theoretic representation allows for mathematically representing building information models and performing computations using these functions.

Shared autonomy for close-quarters navigation and control of a VTOL platform

This thesis presents an approach for a vertical infrastructure inspection using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structure such as light and power distribution poles is a difficult task. There are challenges involved with developing such an inspection system, such as flying in close proximity to a target while maintaining a fixed stand-off distance from it. The contributions of this thesis fall into three main areas. Firstly, an approach to vehicle dynamic modeling is evaluated in simulation and experiments. Secondly, EKF-based state estimators are demonstrated, as well as estimator-free approaches such as image based visual servoing (IBVS) validated with motion capture ground truth data. Thirdly, an integrated pole inspection system comprising a VTOL platform with human-in-the-loop control, (shared autonomy) is demonstrated. These contributions are comprehensively explained through a series of published papers.

Trait resilience fosters adaptive coping when control opportunities are high : implications for the motivating potential of active work

Purpose: We examine the interaction between trait resilience and control in predicting coping and performance. Drawing on a person–environment fit perspective, we hypothesized resilient individuals would cope and perform better in demanding work situations when control was high. In contrast, those low in resilience would cope and perform better when control was low. Recognizing the relationship between trait resilience and performance also could be indirect, adaptive coping was examined as a mediating mechanism through which high control enables resilient individuals to demonstrate better performance. Methodology: In Study 1 (N = 78) and Study 2 (N = 94), participants completed a demanding inbox task in which trait resilience was measured and high and low control was manipulated. Study 3 involved surveying 368 employees on their trait resilience, control, and demand at work (at Time 1), and coping and performance 1 month later at Time 2. Findings: For more resilient individuals, high control facilitated problem-focused coping (Study 1, 2, and 3), which was indirectly associated with higher subjective performance (Study 1), mastery (Study 2), adaptive, and proficient performance (Study 3). For more resilient individuals, high control also facilitated positive reappraisal (Study 2 and 3), which was indirectly associated with higher adaptive and proficient performance (Study 3). Implications: Individuals higher in resilience benefit from high control because it enables adaptive coping. Originality/value: This research makes two contributions: (1) an experimental investigation into the interaction of trait resilience and control, and (2) investigation of coping as the mechanism explaining better performance.