Oliver M. Cliff

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

The application of autonomous robots to efficiently locate small wildlife species has the potential to provide significant ecological insights not previously possible using traditional landbased survey techniques, and a basis for improved conservation policy and management. We present an approach for autonomously localizing radio-tagged wildlife using a small aerial robot. We present a novel two-point phased array antenna system that yields unambiguous bearing measurements and an associated uncertainty measure. Our estimation and informationbased planning algorithms incorporate this bearing uncertainty to choose observation points that improve confidence in the location estimate. These algorithms run online in real time and we report experimental results that show successful autonomous localization of stationary radio tags and live radio-tagged birds.

Towards Quantifying Interaction Networks in a Football Match

We present several novel methods quantifying dynamic interactions in simulated football games. These interactions are captured in directed networks that represent significant coupled dynamics, detected information-theoretically. The model-free approach measures information dynamics of both pair-wise players’ interactions as well as local tactical contests produced during RoboCup 2D Simulation League games. This analysis involves computation of information transfer and storage, relating the information transfer to responsiveness of the players and the team, and the information storage within the team to the team’s rigidity and lack of tactical flexibility. The resultant directed networks (interaction diagrams) and the measures of responsiveness and rigidity reveal implicit interactions, across teams, that may be delayed and/or long-ranged. The analysis was verified with a number of experiments, identifying the zones of the most intense competition and the extent of interactions.

Quantifying long-range interactions and coherent structure in multi-agent dynamics

We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the teams rigidity and lack of tactical flexibility. The resultant interaction and coherence diagrams reveal implicit interactions, across teams, that may be spatially long-range. The analysis was verified with a statistically significant number of experiments (using simulated football games, produced during RoboCup 2D Simulation League matches), identifying the zones of the most intense competition, the extent and types of interactions, and the correlation between the strength of specific interactions and the results of the matches.

An Information Criterion for Inferring Coupling of Distributed Dynamical Systems

The behaviour of many real systems can be modelled by nonlinear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model as a synchronous graph dynamical systems. Specifically, we study the structure learning problem for dynamical systems coupled via a directed acyclic graph. Unlike established structure learning procedures that find locally maximum posterior probabilities of a network structure containing latent variables, our work shows that we can exploit the properties of certain dynamical systems to compute globally optimal approximations of these distributions. We arrive at this result by use of time delay embedding theorems. Taking an information-theoretic perspective, we show that the log-likelihood has an intuitive interpretation in terms of information transfer.The behaviour of many real-world phenomena can be modelled by nonlinear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model by a set of coupled maps as a synchronous update graph dynamical systems. Specifically, we study the structure learning problem for spatially distributed dynamical systems coupled via a directed acyclic graph. Unlike established structure learning procedures that find locally maximum posterior probabilities of a network structure containing latent variables, our work exploits the properties of dynamical systems to compute globally optimal approximations of these distributions. We arrive at this result by the use of time delay embedding theorems. Taking an information-theoretic perspective, we show that the log-likelihood has an intuitive interpretation in terms of information transfer.

Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems

The Kullback–Leibler (KL) divergence is a fundamental measure of information geometry that is used in a variety of contexts in artificial intelligence. We show that, when system dynamics are given by distributed nonlinear systems, this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. More specifically, these measures are applicable when selecting a candidate model for a distributed system, where individual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this framework due to the hidden variables; however, we can exploit the properties of certain dynamical systems to formulate exact methods based on differential topology. We approach the problem by using reconstruction theorems to derive an analytical expression for the KL divergence of a candidate DAG from the observed dataset. Using this result, we present a scoring function based on transfer entropy to be used as a subroutine in a structure learning algorithm. We then demonstrate its use in recovering the structure of coupled Lorenz and Rössler systems.

Dec-MCTS: Decentralized planning for multi-robot active perception

We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.

Investigating spatiotemporal dynamics and synchrony of influenza epidemics in Australia: An agent-based modelling approach

In this paper we present ACEMod, an agent-based modelling framework for studying influenza epidemics in Australia. The simulator is designed to analyse the spatiotemporal spread of contagion and influenza spatial synchrony across the nation. The individual-based epidemiological model accounts for mobility (worker and student commuting) patterns and human interactions derived from the 2006 Australian census and other national data sources. The high-precision simulation comprises 19.8 million stochastically generated software agents and traces the dynamics of influenza viral infection and transmission at several scales. Using this approach, we are able to synthesise epidemics in Australia with varying outbreak locations and severity. For each scenario, we investigate the spatiotemporal profiles of these epidemics, both qualitatively and quantitatively, via incidence curves, prevalence choropleths, and epidemic synchrony. This analysis exemplifies the nature of influenza pandemics within Australia and facilitates future planning of effective intervention, mitigation and crisis management strategies.

Evaluating techniques for learning a feedback controller for low-cost manipulators

Robust manipulation with tractability in unstructured environments is a prominent hurdle in robotics. Learning algorithms to control robotic arms have introduced elegant solutions to the complexities faced in such systems. A novel method of Reinforcement Learning (RL), Gaussian Process Dynamic Programming (GPDP), yields promising results for closed-loop control of a low-cost manipulator however research surrounding most RL techniques lack breadth of comparable experiments into the viability of particular learning techniques on equivalent environments. We introduce several model-based learning agents as mechanisms to control a noisy, low-cost robotic system. The agents were tested in a simulated domain for learning closed-loop policies of a simple task with no prior information. Then, the fidelity of the simulations is confirmed by application of GPDP to a physical system.

Robotic ecology: Tracking small dynamic animals with an autonomous aerial vehicle

Estimation algorithms for wildlife tracking with an autonomous aerial robot are supported by field validation with wild swift parrots. Understanding animal movements that underpin ecosystem processes is fundamental to ecology. Recent advances in animal tags have increased the ability to remotely locate larger species; however, this technology is not suitable for up to 70% of the world’s bird and mammal species. The most widespread technique for tracking small animals is to manually locate low-power radio transmitters from the ground with handheld equipment. Despite this labor-intensive technique being used for decades, efforts to reduce or automate this process have had limited success. Here, we present an approach for tracking small radio-tagged animals by using an autonomous and lightweight aerial robot. We present experimental results where we used the robot to locate critically endangered swift parrots (Lathamus discolor) within their winter range. The system combines a miniaturized sensor with newly developed estimation algorithms to yield unambiguous bearing- and range-based measurements with associated measures of uncertainty. We incorporated these measurements into Bayesian data fusion and information-based planning algorithms to control the position of the robot as it collected data. We report estimated positions that lie within about 50 meters of the true positions of the birds on average, which are sufficiently accurate for recapture or observation. Further, in comparison with experienced human trackers from locations where the signal was detectable, the robot produced a correct estimate as fast or faster than the human. These results provide validation of robotic systems for wildlife radio telemetry and suggest a way for widespread use as human-assistive or autonomous devices.