Nathan Wallace

Nicholas Harrison, Nathan Wallace, Salah Sukkarieh

The efficient collection of samples is an important factor in outdoor information gathering applications on account of high sampling costs such as time, energy, and potential destruction to the environment. Utilization of available a-priori data can be a powerful tool for increasing efficiency. However, the relationships of this data with the quantity of interest are often not known ahead of time, limiting the ability to leverage this knowledge for improved planning efficiency. To this end, this work combines transfer learning and active learning through a Multi-Task Gaussian Process and an information-based objective function. Through this combination it can explore the space of hypothetical inter-quantity relationships and evaluate these hypotheses in real-time, allowing this new knowledge to be immediately exploited for future plans. The performance of the proposed method is evaluated against synthetic data and is shown to evaluate multiple hypotheses correctly. Its effectiveness is also demonstrated on real datasets. The technique is able to identify and leverage hypotheses which show a medium or strong correlation to reduce prediction error by a factor of 1.4--3.4 within the first 7 samples, and poor hypotheses are quickly identified and rejected eventually having no adverse effect.

Stuart Eiffert, Nathan D. Wallace, He Kong et al.

Achieving long-term autonomy for mobile robots operating in real-world unstructured environments such as farms remains a significant challenge. This is made increasingly complex in the presence of moving humans or livestock. These environments require a robot to be adaptive in its immediate plans, accounting for the state of nearby individuals and the response that they might have to the robot's actions. Additionally, in order to achieve longer-term goals, consideration of the limited on-board resources available to the robot is required, especially for extended missions such as weeding an agricultural field. To achieve efficient long-term autonomy, it is thus crucial to understand the impact that online dynamic updates to an energy efficient offline plan might have on resource usage whilst navigating through crowds or herds. To address these challenges, a hierarchical planning framework is proposed, integrating an online local dynamic path planner with an offline longer-term objective-based planner. This framework acts to achieve long-term autonomy through awareness of both dynamic responses of individuals to a robot's motion and the limited resources available. This paper details the hierarchical approach and its integration on a robotic platform, including a comprehensive description of the planning framework and associated perception modules. The approach is evaluated in real-world trials on farms, requiring both consideration of limited battery capacity and the presence of nearby moving individuals. These trials additionally demonstrate the ability of the framework to adapt resource use through variation of the local dynamic planner, allowing adaptive behaviour in changing environments. A summary video is available at https://youtu.be/DGVTrYwJ304.

Stuart Eiffert, Nathan D. Wallace, He Kong et al.

Achieving long term autonomy of robots operating in dynamic environments such as farms remains a significant challenge. Arguably, the most demanding factors to achieve this are the on-board resource constraints such as energy, planning in the presence of moving individuals such as livestock and people, and handling unknown and undulating terrain. These considerations require a robot to be adaptive in its immediate actions in order to successfully achieve long-term, resource-efficient and robust autonomy. To achieve this, we propose a hierarchical framework that integrates a local dynamic path planner with a longer term objective based planner and advanced motion control methods, whilst taking into consideration the dynamic responses of moving individuals within the environment. The framework is motivated by and synthesizes our recent work on energy aware mission planning, path planning in dynamic environments, and receding horizon motion control. In this paper we detail the proposed framework and outline its integration on a robotic platform. We evaluate the strategy in extensive simulated trials, traversing between objective waypoints to complete tasks such as soil sampling, weeding and recharging across a dynamic environment, demonstrating its capability to robustly adapt long term mission plans in the presence of moving individuals and obstacles for real world applications such as large scale farming.

Nathan Wallace, He Kong, Andrew Hill et al.

The control of field robots in varying and uncertain terrain conditions presents a challenge for autonomous navigation. Online estimation of the wheel-terrain slip characteristics is essential for generating the accurate control predictions necessary for tracking trajectories in off-road environments. Receding horizon estimation (RHE) provides a powerful framework for constrained estimation, and when combined with receding horizon control (RHC), yields an adaptive optimisation-based control method. Presently, such methods assume slip to be constant over the estimation horizon, while our proposed structured blocking approach relaxes this assumption, resulting in improved state and parameter estimation. We demonstrate and compare the performance of this method in simulation, and propose an overlapping-block strategy to ameliorate some of the limitations encountered in applying noise-blocking in a receding horizon estimation and control (RHEC) context.