Ali Haydar Göktogan

The ANSER Project: Data Fusion Across Multiple Uninhabited Air Vehicles

The objective of the autonomous navigation and sensing experiment research (ANSER) project is to demonstrate decentralized data fusion (DDF) and simultaneous localization and map building (SLAM) across multiple uninhabited air vehicles (UAVs). To achieve this objective, the project specifies the development of four UAVs, where each UAV houses up to two terrain sensors and an INS/GPS navigation system. The terrain sensors include a scanning radar, laser/vision and standard vision system. The DDF concept has to be shown to be effective both on a single UAV and on multiple UAVs. The proof of the concept will lie in the ability of the DDF structure to conduct multi-target tracking problems as well as SLAM. To obtain this goal, a number of subgoals are required, most of which have never been attempted before on a research level. The objective of this paper is to present these goals as an overview of the ANSER project along with some simulated and real-time results.

A Rotary-wing Unmanned Air Vehicle for Aquatic Weed Surveillance and Management

This paper addresses the novel application of an autonomous rotary-wing unmanned air vehicle (RUAV) as a cost-effective tool for the surveillance and management of aquatic weeds. A conservative estimate of the annual loss of agricultural revenue to the Australian economy due to weeds is in the order of A

Coordinated search for a lost target in a Bayesian world

4 billion, hence the reason why weed control is of national significance. The presented system locates and identifies weeds in inaccessible locations. The RUAV is equipped with low-cost sensor suites and various weed detection algorithms. In order to provide the weed control operators with the capability of autonomous or remote control spraying and treatment of the aquatic weeds the RUAV is also fitted with a spray mechanism. The system has been demonstrated over inaccessible weed infested aquatic habitats.

The Demonstration of a Cooperative Control Architecture for UAV Teams

This paper describes a decentralized Bayesian approach to the problem of coordinating multiple autonomous sensor platforms searching for a single non-evading target. In this architecture, each decision maker builds an equivalent representation of the probability density function (PDF) of the target state through a general decentralized Bayesian sensor network, enabling them to coordinate their actions without exchanging any information about their plans. The advantage of the approach is that a high degree of scalability and real-time adaptability can be achieved. The framework is implemented on a real-time high-fidelity multi-vehicle simulator system. The effectiveness of the method is demonstrated in different scenarios for a team of airborne search vehicles looking for both a stationary and a drifting target lost at sea.

Real time Multi-UAV Simulator

This paper presents the implementation and demonstration of a decentralised system architecture for the control of teams of UAVs performing information gathering tasks in large unstructured environments. The focus of the implementation and of the experiments performed are on information gathering tasks. In particular on the estimation/tracking of the locations of ground based features. Our approach to the problem is discussed and the system on which it is implemented is described. Flight demonstration results are shown for multiple vehicles cooperating and planning paths online to most efficiently estimate the location of a number of known features. These results, in conjunction with Hardware-In-the-Loop testing, validate the approach to the problem for a real time system.

Mapping and Tracking

This paper presents the system architecture of a real time multi-UAV simulator (RMUS). The simulator has been implemented as both a testing and validation mechanism for the real demonstration of multiple UAVs conducting both decentralised data fusion and control. These mechanisms include the off-line simulation of complex scenarios, hardware-in-the-loop tests, validation of real test results, and online mission control system demonstrations. The paper also present CommLibX, a novel communication framework for the system which allows simulation modules to communicate over single or multiple virtual channels. This unique communication system is then easily ported onto the real hardware allowing for maximum reuse of software and integrity.

System development and demonstration of a UAV control architecture for information gathering missions

This article presents the implementation of decentralized data fusion (DDF) and cooperative control algorithms on an unmanned aerial system (UAS). We conduct a number of demonstrations with a pair of unmanned aerial vehicles (UAVs) performing an information-gathering mission, and we show that significant benefits can be achieved by enabling cooperation through the sharing of information between members of the team. The objective is to utilize the UAV team to estimate the position and velocity of a number of ground-based features. The UAVs are given some prior knowledge of the feature states and are required to gather further information above a predefined threshold. This situation models a scenario where initial information is made available from an external source (e.g., a high-flying UAV or satellite imagery), which then prompts the start of the feature-localization mission.

System Development and Demonstration of a Cooperative UAV Team for Mapping and Tracking

This paper presents a system architecture for unmanned aerial vehicles (UAVs) performing information gathering missions. Particular focus in this paper is on the development of the architecture from algorithms to simulation, hardware-in-the-loop testing and flight demonstrations. The architecture has been developed for a team of Brumby Mk 3 fixed wing UAVs. Results from recent flight demonstrations are presented in which a single UAV validated a path-following guidance algorithm for feature orbiting, and a utility-based dynamic path planning algorithm applied to a feature tracking problem. The architecture has also been designed to accommodate the use of multiple UAVs, and future work on a team approach to the problem is presented.

A Vision Based Relative Navigation Framework for Formation Flight

We present the implementation and demonstration of a team of two fixed-wing Unmanned Aerial Vehicles whose task is to improve the localization accuracy of a number of ground-based features. The underlying algorithmic paradigm is based on over a decade’s worth of work on Decentralized Data Fusion and Control. We present the components of the architecture, including vehicle localization, feature tracking, path planning and cooperative control. The algorithms described are implemented on a complete Unmanned Aerial System. Three demonstrations were performed, with varying levels of cooperation between team members. We present the results of these demonstrations and compare the performance of the team in completing the mission along with a quantitative understanding of the benefits achieved.

The Development of a Real-Time Modular Architecture for the Control of UAV Teams

Unmanned aerial vehicle (UAV) formation flight can vastly increase operational range and persistence through autonomous aerial refuelling or efficient flight on a wingmans wake vortices. Differencing individual UAV state estimates is not sufficiently accurate for close formation operations and must be augmented with vehicle-to-vehicle observations. To this end, we propose a quaternion based unscented Kalman filter to fuse information from each UAV sensor suite with relative vision observations. The result is a vastly improved relative state estimate that is resilient to brief vision dropouts and degrades gracefully during extended dropouts. Simulated formation flight results validate the approach and provide a numerical analysis of the algorithm performance. Ground based experiments demonstrate the algorithm running in real-time on a dual-UAV system. This represents a significant step towards an airborne implementation.