Thurs 16 July 2020 1p
|Thurs 9 July 2020 5p||Cosimo Della Santina, TUM, DLR||http://www.cosimodellasantina.com/|
|Thurs 2 July 2020 1p||Title: Path Following of Underactuated Mechanical Systems: An Energy Perspective|
|Thurs 25 Jun 2020 1p||Prof Dana Kulic, Monash||https://research.monash.edu/en/persons/dana-kulic|
Title: A Transfer Learning approach to Space Debris Classification through Light Curve Analysis
Overview: In this seminar I will be present progress in my research into space debris classification through the application of transfer learning to light curves extracted from telescope data. The development of a generalised characterisation method for space debris is a significant goal in Space Situational Awareness (SSA) in order to mitigate the risk to both current and future space missions. A dataset of real light curves is being collected and curated using telescope data provided by an industry partner. Due to the difficulties in labelling the real data and obtaining large quantities of observational data, a high-fidelity Blender based simulation environment has also been developed with the ability to generate light curves for a range of space objects. This simulated dataset will be used for pre-training models with the aim of improving classification results on the real dataset.
Bio: James graduated with a Bachelor of Aeronautical Space Engineering from the University of Sydney in 2016 and is currently a PhD student at ACFR.
Title: Intelligent Robotic Non-Chemical Weeding
Weeds compete with crop for nutrients, water and sun light. Controlling weeds and lost revenue is estimated to cost grain growers $3.3 billion a year. Early reliance on herbicides as a complete weed control solution has made contemporary weed management a challenge due to the increasing prevalence of herbicide resistant weeds. An emerging tool in weed management and precision agriculture is site-specific weed management. In site-specific weed management, individual weeds are detected and targeted by a control method. Since this task cannot be solved with a purely mechanical system, it is only feasible at a small scale and requires a large amount of human labour. Robotic systems combined with computer vision and machine learning hold the potential to change the scalability and labour costs of site-specific weed management. Currently commercial robotic site-specific weed management is limited to fallow land where infra-red reflectance can be used to perform ‘green-on-brown’ detection. In this research project, the goal is to extend autonomous weeding capabilities to ‘green-on-green’ detection - that is, detecting weeds amongst crop.
Bio: Dr Asher Bender is a post-doctoral researcher at the Australian Centre for Field Robotics. His research interest is in applying machine learning to solve high-level problems using data collected by autonomous systems. He has worked in marine robotics, intelligent transportation and is currently doing research in agricultural robotics.
|Thurs 28-May-2020 9a|
Asst Prof Matthew O'Toole, CMU Robotics
|Tues 26 May 2020 2p|
Thesis Title: Analysing the Robustness of Semantic Segmentation for Autonomous Vehicles
Abstract: Intelligent systems require the capability to perceive and interact with the surrounding environment. Semantic segmentation, as a pixel-level classification task, is at the frontier of providing a human-like understanding to intelligent systems enabling them to view and understand the world as we do. Deep learning based semantic segmentation algorithms have shown considerable success for certain tasks in recent years. However, in real-world safety critical applications such as autonomous vehicles, there are still many complexities that restrict the use of this technology. My research has been focusing on analysing the generalisation and the robustness of semantic segmentation for intelligent vehicles. A system validation pipeline has been proposed to tackle the challenges of evaluating and quantifying the performance of semantic segmentation before deploying to intelligent platforms. This method can be used in most urban traffic scenarios without the time and expense of using humans to generate labels by hand.
Bio: Wei graduated with Bachelor of Engineering Degrees from the Beijing Institute of Technology and the Australian National University in 2013 and submitted her PhD thesis in March 2020 at ACFR.
|Thurs 21-May-2020 1p|
Title: Long-term map maintenance pipeline for autonomous vehicles
Abstract: One of the requirements for autonomous vehicles to be able to operate persistently in typical urban environments is to maintain high accuracy position information over time, in other words, the capability of their mapping and localisation system to adapt to the changes. The classic definition of localisation based on a single-survey map is not suitable for long-term operation due to its inadequacy to detect and incorporate the variations of the ambience. In this work, we present a process to adjust a featured-based map to the actual environment, this adaptation pipeline seeks to lessen or ward off possible localisation difficulties while taking advantage of the changes of the surroundings. We incorporate different sensor modalities which provide information about the environment and the state of the moving platform.
Bio: Stephany graduated with a Bachelor of Mechatronic Engineering from Universidad Autonoma de Occidente (Colombia) and is now a PhD student at ACFR (ITS research group).
|Thurs 7-May-2020 11a|
Title: Efficient validation method for Highly Automated Vehicle to ensure safety
Abstract: The wide-scale deployment of Autonomous Vehicles (AV) seems to be imminent despite many safety challenges that are yet to be resolved. It is well known that there are no universally agreed validation methodologies to guarantee absolute safety, which is crucial for the acceptance of this technology. My research focus is to propose an efficient method that can enable better result than the existing approaches such as test matrix, distance-based validation, and Monte-Carlo simulation.
Bio: Dhanoop completed Bachelor of Computer Science and Engineering from India and Masters in IT from the University of Wollongong. He is now a PhD student at ACFR.
|Thurs 30-Apr-2020 1p|
Title: Creating an AUV - Post-Mortem
|Thurs 23-Apr-2020 1p||Title: Computationally Efficient Dynamic Traffic Optimisation Of Railway Systems|
Abstract: In this seminar, we discuss traffic optimisation for railway systems. These can be seen as multi-agent systems with movement constraints entailing logic conditions (e.g., precedence of utilisation of specific railway tracks) and, as such, the underlying optimisation programs are large NP-hard models. To limit computational complexity, we reduce optimisation horizons. This however makes trains “blind” to the presence of each other beyond the limits of these reduced horizons, with the potential to result in deadlocking. We present an approach to address this shortcoming borrowing notions from control systems theory. We also discuss other complexity reduction mechanisms enabled by this result.
We cover examples illustrating cases where the optimisation models determine traffic patterns that, according to feedback we received form several train controllers, surpass human ability. We also briefly touch upon challenges of deploying these automation techniques in real life in a commercial setting involving a large freight network owned by Rio Tinto.
We also take this opportunity to briefly present current open opportunities for work / collaboration at RTCMA.
Bio: Robin completed his PhD at ETH Zurich, Department of Electrical Engineering and Information Technology, under the supervision of Prof. Manfred Morari. His research focuses on the application of mathematical optimisation techniques, as well as computational methods for handling large scale systems and contexts subject to uncertainty. He has been Research Fellow at the Rio Tinto Centre for Mine Automation since 2016, and became team lead for the project "Pit to Port Optimisation" in 2019.
|Thurs 16-Apr-2020 1p|
Title: Two-Level Hierarchical Planning in a Known Semi-Structured Environment
Abstract: The application of motion planning for autonomous vehicles has been primarily focused either in highly structured or unstructured environments. However, many environments in the real-world share the characteristics of both and can be classified as semi-structured. The adaptation of the strategies from other environments to that of semi-structured, although possible, do not produce trajectories with the required characteristics, especially when the environment is dynamic. In this talk I present a practical two-level hierarchical planning strategy consisting of a discrete lane-network-based global planner and Hybrid A* local planner that (i) generates a smooth, safe and kinematically feasible path in real-time; (ii) considers structural constraints of the environment from an a priori map. I will present preliminary results from the field test at Callan Park and future directions of the project.
Bio: Karan recently joined the ITS research group of ACFR after completing his PhD from UNSW in 2019. He is currently focussing on navigation strategies for autonomous vehicles.
|Thurs 9-Apr-2020 1p|
Title: Tree crop analysis using mobile LiDAR: From light to pruning
Abstract: Commercial fruit growers find it helpful to understand how their crops are growing on an orchard scale and tree scale. This enables them to make decisions regarding actions like pruning. There are a number of tools for measuring tree growth factors which involve the grower manually inspecting each tree, which is prohibitively difficult. Instead, we can use modern LiDAR technology to digitise the trees and perform detailed analyses in silico. In this talk I present my work in using point cloud scans of tree crops from mobile handheld LiDAR to analyse the light interception and tree structure characteristics. I will also discuss the possibility of using these methods to inform automatic pruning decisions, since historically these decisions are made by conventional wisdom rather than tree-specific optimisation.
Bio: Fred graduated with a Bachelor of Mechatronic Engineering and Computer Science from UNSW in 2016 and is now a PhD student at ACFR focusing on LiDAR applications in tree crops.
|Thurs 2-Apr-2020 1p|
|Thurs 26-Mar-2020 1p|
|Thurs 6-Feb-2020 1p||Chihyung Jeon|
Title: Talking over the robot: A field study of strained collaboration in a dementia-prevention robot class in South Korea
Abstract: I will present a case study of Silbot – a “dementia-prevention robot” – at a regional health center in South Korea, which I conducted with my graduate students at KAIST. From our on-site observation of the Silbot classes, we claim that the efficacy of the robot class relies heavily on the “strained collaboration” between the human instructor and the robot. “Strained collaboration” refers to the ways in which the instructor works with the robot, attempting to compensate for the robot’s functional limitation and social awkwardness. In bringing Silbot into the classroom setting, the instructor employs characteristic verbal tones, bodily movements, and other pedagogical tactics. The instructor even talks over the robot, downplaying its interactional capacity. We conclude that any success of such robot programs requires a deeper understanding of the spatial and human context of robot use, including the role of human operators or mediators and also that this understanding should be reflected in the design, implementation, and evaluation of robot programs.
Bio: Chihyung Jeon is an associate professor of science, technology, and policy at KAIST (Korea Advanced Institute of Science and Technology). He received his PhD degree in STS (Science, Technology & Society) at the Massachusetts Institute of Technology and has conducted research at the Max Planck Institute for the History of Science in Berlin and the Rachel Carson Center for Environment and Society in Munich. His research focuses on the sociocultural relationship between humans and technologies. He is currently working on cultures of AI and robotics in South Korea and participating in LIFEBOTS Exchange, an EU-funded research network on social robots for welfare and healthcare. He is also interested in the technologies and cultures of simulation, remoteness, and humanlessness. Within KAIST, he is also affiliated with the Center for Anthropocene Studies, where he is looking at scientific and public practices about aerial conditions.
|Tue Jan-14-2020 2:00p||Suda Bharadwaj|
Title: Assured Autonomy for Complex Systems
Abstract: Over the last decade, there has been an explosion in the use of autonomous systems and artificial intelligence in our daily lives. As human reliance on autonomy grows, so do the consequences of autonomous agents failing to achieve their mission. One area poised to make a fundamental impact is urban air mobility (UAM). UAM refers to on-demand air transportation services within an urban area. With projections indicating high-volume use of autonomous aircraft in urban air spaces, it is clear that advances in decision-making for autonomous systems with assured performance will play a key role in the advancement and acceptance of UAM. In this talk I explore the use of techniques from the field of formal methods in order to provide theoretical guarantees of performance and safety in multi-agent systems such as UAM. While formal methods provides powerful tools to formally specify and guarantee complex high- level requirements, it suffers from a lack of scalability restricting its applicability for systems with multiple agents. We explore the use of runtime enforcement or shielding in order to guarantee safety of complex systems at runtime without knowledge of the underlying systems’ design or goals. I will present our work in decentralizing the synthesis procedure in order to allow for use in systems with large numbers of agents and demonstrate its effectiveness with some UAM-based examples.
Bio: Suda Bharadwaj is currently a PhD student in the U-T-Autonomous systems lab at the University of Texas at Austin, supervised by Dr. Ufuk Topcu. His research interests involve assured autonomy using formal methods, reinforcement learning, and control. He completed his undergraduate studies at the University of Sydney with a BE/BSc double degree majoring in Aeronautical (Space) engineering and Physics. He received his MS in Aerospace Engineering at the University of Texas at Austin.