Robust Parafoil Terminal Guidance Using Massively Parallel Processing

Terminal guidance of autonomous parafoils is a difficult problem in which wind uncertainty and system underactuation are major challenges. Existing strategies almost exclusively use impact error as the criterion for optimality. Practical airdrop systems, however, must also include other criteria that maybe even more important than impact error for some missions, such as ground speed at impact or constraints imposed by drop zones with restrictions on flight patterns. Furthermore, existing guidance schemes determine terminal trajectories using deterministic wind information and may result in a solution that works in ideal wind but may be sensitive to variations. The work described here develops a guidance strategy that uses massively parallel Monte Carlo simulation performed on a graphics processing unit to rank candidate trajectories in terms of robustness to wind uncertainty. The result is robust guidance, as opposed to optimal guidance. Through simulation results, the proposed path planning scheme proves more robust in realistic dynamic wind environments compared with previous optimal trajectory planners that assume perfect knowledge of a constant wind

Developing strategic information system planning model in Libya organisations

This quantitative research study investigated the impact of organisational context on the process and success of strategic IS planning (SISP) in post-implementation information systems in Libyan organisations. A set of direct and indirect relationships were investigated in the research model. The organisational context presented as a contingent situational variable mediated by SISP process and predicted by SISP success (the criterion variable). The causality of the relationship set was developed from the contingency theory of information systems and supported by fit models in strategic management research. The study deployed multivariate analysis represented in the structural equation modelling (SEM) to develop robust construct measurements and analyse data collected from executives responsible for information systems planning in both public and private Libyan organisations. Multi-dimensional multi-items constructs were used in the path analysis model after they were extensively validated. The path analysis model represented as mediation model, where hypothesise suggest that SISP context has an impact SISP success, through the influence of the SISP process. In the model, four dimensions of the SISP context construct were found to have a significant impact on SISP success directly and indirectly through the SISP process. Two of these dimensions are components of the leadership orientation construct, namely “Creative and Controlling” leadership. The other two dimensions are “Organisation centralisation structure and the Riskiness of organisation strategies”. The environmental uncertainty and planning resource constructs were found to have no impact on SISP success in Libyan organisations. Furthermore, this study validated six out of seven dimensions of SISP process construct measurement; only five exhibited acceptable fit level in the path analysis model and all were affected by the SISP context. However, just three out of five SISP process constructs had an impact on SISP success namely “Comprehensiveness, Focus and Intuition planning process”. Different SISP processes were associated with different levels of SISP success, “Intuition” was the most effective SISP process approach. The second most effective SISP process approach was the “Focus on innovation”, followed by “Limited comprehensiveness”. The SISP success measured by the fulfilment of key objectives that has three measurements constructs namely “Analysis, Alignment, and Cooperation”. The research suggest that under the effect of organisation context the most successful SISP produced by (CIO, CEO, or top executives) who rely less on personal judgment, focus more on innovation rather than control and limit their comprehensiveness of information systems planning process

Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation

In this paper, a concurrent learning framework is developed for source search in an unknown environment using autonomous platforms equipped with onboard sensors. Distinct from the existing solutions that require significant computational power for Bayesian estimation and path planning, the proposed solution is computationally affordable for onboard processors. A new concept of concurrent learning using multiple parallel estimators is proposed to learn the operational environment and quantify estimation uncertainty. The search agent is empowered with dual capability of exploiting current estimated parameters to track the source and probing the environment to reduce the impacts of uncertainty, namely Concurrent Learning based Dual Control for Exploration and Exploitation (CL-DCEE). In this setting, the control action not only minimises the tracking error between future agent's position and estimated source location, but also the uncertainty of predicted estimation. More importantly, the rigorous proven properties such as the convergence of CL-DCEE algorithm are established under mild assumptions on noises, and the impact of noises on the search performance is examined. Simulation results are provided to validate the effectiveness of the proposed CL-DCEE algorithm. Compared with the information-theoretic approach, CL-DCEE not only guarantees convergence, but produces better search performance and consumes much less computational time

Employing back casting principles for the formation of long term built asset management strategies - A theoretical approach

Purpose: Facilities managers have traditionally relied on forecasting approaches using the stock condition survey to predict maintenance and refurbishment needs against changing user requirements. However, the authors have previously shown that such an approach, whilst effective for short term planning, is unable to cope with the uncertainty and complex data sets required to develop long term plans (> 10 years), in particular the impact of future climate change (physical and legislative). This paper will present back casting as an alternative approach to support long term built asset management planning. Background: Back casting has been applied to sustainable transport management, energy planning and community climate change adaptation projects. The process in principle envisions a future state (end-point) set by stakeholders. Alternative ‘paths of approach’ are identified by looking backwards from the future state to the present. Each path is examined in turn to identify interventions (physical and/or operational) required in order for that path to achieve the end-goal. The stakeholder’s review each path and select the most appropriate for achieving the desired (end-point). This path is then integrated into the facilities (built asset) management strategy. Approach: The researchers worked with various stakeholders as part of an action research team to identify climate change adaptations that may be required to ensure the continued performance of the building and integrate these into a 60 year facilities management plan. Results: The paper superimposes back casting theory onto the adaptation process and explains how the theory supported long term facilities management planning. The paper also explains how the approach was used to provide confidence for the building owner to invest in the planned refurbishment of their built asset to improve its future performance and sustainability. Practical implications: The paper demonstrates the application of this approach through a case study example of a newly constructed £75 m educational building. A similar approach could be applied to other building types. Research limitations: This paper presents a theoretical model which needs to be validated using longitudinal data sets. Originality/value: This is the first paper to suggest the potential of back casting to inform long term built asset management strategies

Planning Under Uncertainty for Unmanned Aerial Vehicles

Unmanned aerial vehicle (UAV) technology has grown out of traditional research and military applications and has captivated the commercial and consumer markets, showing the ability to perform a spectrum of autonomous functions. This technology has the capability of saving lives in search and rescue, fighting wildfires in environmental monitoring, and delivering time dependent medicine in package delivery. These examples demonstrate the potential impact this technology will have on our society. However, it is evident how sensitive UAVs are to the uncertainty of the physical world. In order to properly achieve the full potential of UAVs in these markets, robust and efficient planning algorithms are needed. This thesis addresses the challenge of planning under uncertainty for UAVs. We develop a suite of algorithms that are robust to changes in the environment and build on the key areas of research needed for utilizing UAVs in a commercial setting. Throughout this research three main components emerged: monitoring targets in dynamic environments, exploration with unreliable communication, and risk-aware path planning. We use a realistic fire simulation to test persistent monitoring in an uncertain environment. The fire is generated using the standard program for modeling wildfire, FARSITE. This model was used to validate a weighted-greedy approach to monitoring clustered points of interest (POIs) over traditional methods of tracking a fire front. We implemented the algorithm on a commercial UAV to demonstrate the deployment capability. Dynamic monitoring has limited potential if if coordinated planning is fallible to uncertainty in the world. Uncertain communication can cause critical failures in coordinated planning algorithms. We develop a method for coordinated exploration of a multi-UAV team with unreliable communication and limited battery life. Our results show that the proposed algorithm, which leverages meeting, sacrificing, and relaying behavior, increases the percentage of the environment explored over a frontier-based exploration strategy by up to 18%. We test on teams of up to 8 simulated UAVs and 2 real UAVs able to cope with communication loss and still report improved gains. We demonstrate this work with a pair of custom UAVs in an indoor office environment. We introduce a novel approach to incorporating and addressing uncertainty in planning problems. The proposed Risk-Aware Graph Search (RAGS) algorithm combines traditional deterministic search techniques with risk-aware planning. RAGS is able to trade off the number of future path options, as well as the mean and variance of the associated path cost distributions to make online edge traversal decisions that minimize the risk of executing a high-cost path. The algorithm is compared against existing graphsearch techniques on a set of graphs with randomly assigned edge costs, as well as over a set of graphs with transition costs generated from satellite imagery data. In all cases, RAGS is shown to reduce the probability of executing high-cost paths over A*, D* and a greedy planning approach. High level planning algorithms can be brittle in dynamic conditions where the environment is not modeled perfectly. In developing planners for uncertainty we ensure UAVs will be able to operate in conditions outside the scope of prior techniques. We address the need for robustness in robotic monitoring, coordination, and path planning tasks. Each of the three methods introduced were tested in simulated and real environments, and the results show improvement over traditional algorithms

Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones

Autonomous drones (also known as unmanned aerial vehicles) are increasingly popular for diverse applications of light-weight delivery and as substitutions of manned operations in remote locations. The computing systems for drones are becoming a new venue for research in cyber-physical systems. Autonomous drones require integrated intelligent decision systems to control and manage their flight missions in the absence of human operators. One of the most crucial aspects of drone mission control and management is related to the optimization of battery lifetime. Typical drones are powered by on-board batteries, with limited capacity. But drones are expected to carry out long missions. Thus, a fully automated management system that can optimize the operations of battery-operated autonomous drones to extend their operation time is highly desirable. This paper presents several contributions to automated management systems for battery-operated drones: (1) We conduct empirical studies to model the battery performance of drones, considering various flight scenarios. (2) We study a joint problem of flight mission planning and recharging optimization for drones with an objective to complete a tour mission for a set of sites of interest in the shortest time. This problem captures diverse applications of delivery and remote operations by drones. (3) We present algorithms for solving the problem of flight mission planning and recharging optimization. We implemented our algorithms in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. We evaluated the results of our algorithms using data from empirical studies. (4) To allow fully autonomous recharging of drones, we also develop a robotic charging system prototype that can recharge drones autonomously by our drone management system

A Bayesian framework for optimal motion planning with uncertainty

Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to path- planning in the extended space of poses x covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state