Paul P. Wu

Multi-Objective Four-Dimensional Vehicle Motion Planning in Large Dynamic Environments

This paper presents Multi-Step A* (MSA*), a search algorithm based on A* for multi-objective 4-D vehicle motion planning (three spatial and one time dimensions). The research is principally motivated by the need for offline and online motion planning for autonomous unmanned aerial vehicles (UAVs). For UAVs operating in large dynamic uncertain 4-D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and a grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles, and the rules of the air. It is shown that MSA* finds a cost optimal solution using variable length, angle, and velocity trajectory segments. These segments are approximated with a grid-based cell sequence that provides an inherent tolerance to uncertainty. The computational efficiency is achieved by using variable successor operators to create a multiresolution memory-efficient lattice sampling structure. The simulation studies on the UAV flight planning problem show that MSA* meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of a vector neighborhood-based A*.

On-board multi-objective mission planning for Unmanned Aerial Vehicles

A system for automated mission planning is presented with a view to operate Unmanned Aerial Vehicles (UAVs) in the National Airspace System (NAS). This paper describes methods for modelling decision variables, for enroute flight planning under Visual Flight Rules (VFR). For demonstration purposes, the task of delivering a medical package to a remote location was chosen. Decision variables include fuel consumption, flight time, wind and weather conditions, terrain elevation, airspace classification and the flight trajectories of other aircraft. The decision variables are transformed, using a Multi-Criteria Decision Making (MCDM) cost function, into a single cost value for a grid-based search algorithm (e.g. A*). It is shown that the proposed system provides a means for fast, autonomous generation of near-optimal flight plans, which in turn are a key enabler in the operation of UAVs in the NAS.

Fuzzy Multi-Objective Mission Flight Planning in Unmanned Aerial Systems

This paper discusses the development of a multi-objective mission flight planning algorithm for unmanned aerial system (UAS) operations within the National Airspace System (NAS). Existing methods for multi-objective planning are largely confined to two dimensional searches and/or acyclic graphs in deterministic environments; many are computationally infeasible for large state spaces. In this paper, a multi-objective fuzzy logic decision maker is used to augment the D* Lite graph search algorithm in finding a near optimal path. This not only enables evaluation and trade-off between multiple objectives when choosing a path in three dimensional space, but also allows for the modelling of data uncertainty. A case study scenario is developed to illustrate the performance of a number of different algorithms. It is shown that a fuzzy multi-objective mission flight planner provides a viable method for embedding human expert knowledge in a computationally feasible algorithm

Timing anthropogenic stressors to mitigate their impact on marine ecosystem resilience

Better mitigation of anthropogenic stressors on marine ecosystems is urgently needed to address increasing biodiversity losses worldwide. We explore opportunities for stressor mitigation using whole-of-systems modelling of ecological resilience, accounting for complex interactions between stressors, their timing and duration, background environmental conditions and biological processes. We then search for ecological windows, times when stressors minimally impact ecological resilience, defined here as risk, recovery and resistance. We show for 28 globally distributed seagrass meadows that stressor scheduling that exploits ecological windows for dredging campaigns can achieve up to a fourfold reduction in recovery time and 35% reduction in extinction risk. Although the timing and length of windows vary among sites to some degree, global trends indicate favourable windows in autumn and winter. Our results demonstrate that resilience is dynamic with respect to space, time and stressors, varying most strongly with: (i) the life history of the seagrass genus and (ii) the duration and timing of the impacting stress.Stressors such as sediment dredging can harm marine organisms, but this impact could be minimised if targeted within ‘ecological windows’. Here, Wu and colleagues develop a modelling framework to identify ecological windows that maximise seagrass resilience under varying dredging schedules.

Managing seagrass resilience under cumulative dredging affecting light: Predicting risk using dynamic Bayesian networks

Coastal development is contributing to ongoing declines of ecosystems globally. Consequently, understanding the risks posed to these systems, and how they respond to successive disturbances, is paramount for their improved management. We study the cumulative impacts of maintenance dredging on seagrass ecosystems as a canonical example. Maintenance dredging causes disturbances lasting weeks to months, often repeated at yearly intervals. We present a risk-based modelling framework for time varying complex systems centred around a dynamic Bayesian network (DBN). Our approach estimates the impact of a hazard on a systems response in terms of resistance, recovery and persistence, commonly used to characterise the resilience of a system. We consider whole-of-system interactions including light reduction due to dredging (the hazard), the duration, frequency and start time of dredging, and ecosystem characteristics such as the life-history traits expressed by genera and local environmental conditions. The impact on resilience of dredging disturbances is evaluated using a validated seagrass ecosystem DBN for meadows of the genera Amphibolis (Jurien Bay, WA, Australia), Halophila (Hay Point, Qld, Australia) and Zostera (Gladstone, Qld, Australia). Although impacts varied by combinations of dredging parameters and the seagrass meadows being studied, in general, 3 months of duration or more, or repeat dredging every 3 or more years, were key thresholds beyond which resilience can be compromised. Additionally, managing light reduction to less than 50% can significantly decrease one or more of loss, recovery time and risk of local extinction, especially in the presence of cumulative stressors. Synthesis and applications. Our risk-based approach enables managers to develop thresholds by predicting the impact of different configurations of anthropogenic disturbances being managed. Many real-world maintenance dredging requirements fall within these parameters, and our results show that such dredging can be successfully managed to maintain healthy seagrass meadows in the absence of other disturbances. We evaluated opportunities for risk mitigation using time windows; periods during which the impact of dredging stress did not impair resilience.

An Intuitive Dashboard for Bayesian Network Inference

Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.