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Demonstrating autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

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Demonstrating Autonomous 3D Path Planning on a Novel Scalabl...

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Eric Sihite Filip Slezak Ioannis Mandralis Adarsh Salagame Milad Ramezani Arash Kalantari Alireza Ramezani Morteza Gharib The Department of Aerospace Engineering California Institute of Technology Pasadena USA Department of Electrical and Computer Engineering The Silicon Synapse Labs Northeastern University Boston USA The Robotics and Autonomous Systems DATA61 CSIRO Brisbane QLD Australia The Jet Propulsion Laboratory Pasadena USA

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.

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Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling

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Locus: LiDAR-based Place Recognition using Spatiotemporal Hi...

IEEE International Conference on robotics and Automation (ICRA)

作者： Kavisha Vidanapathirana Peyman Moghadam Ben Harwood Muming Zhao Sridha Sridharan Clinton Fookes DATA61 CSIRO Robotics and Autonomous Systems Group Brisbane QLD Australia School of Electrical Engineering and Robotics Queensland University of Technology (QUT) Brisbane Australia

Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper presents Locus, a novel place recognition method using 3D LiDAR point clouds in large-scale environments. We propose a method for extracting and encoding topological and temporal information related to components in a scene and demonstrate how the inclusion of this auxiliary information in place description leads to more robust and discriminative scene representations. Second-order pooling along with a non- linear transform is used to aggregate these multi-level features to generate a fixed-length global descriptor, which is invariant to the permutation of input features. The proposed method outperforms state-of-the-art methods on the KITTI dataset. Furthermore, Locus is demonstrated to be robust across several challenging situations such as occlusions and viewpoint changes in 3D LiDAR point clouds. The open-source implementation is available at: https://***/csiro-robotics/locus.

关键词： Three-dimensional displays Laser radar Simultaneous localization and mapping Estimation Transforms Feature extraction Robustness

AEROS: AdaptivE robust least-squares for graph-based SLAM

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arXiv 2021年

作者： Ramezani, Milad Mattamala, Matias Fallon, Maurice Robotics and Autonomous Systems Group DATA61 CSIRO Brisbane Australia Dynamic Robot Systems Oxford Robotics Institute Department of Engineering Science University of Oxford Oxford United Kingdom

In robot localisation and mapping, outliers are unavoidable when loop-closure measurements are taken into account. A single false-positive loop-closure can have a very negative impact on SLAM problems causing an inferior trajectory to be produced or even for the optimisation to fail entirely. To address this issue, popular existing approaches define a hard switch for each loop-closure constraint. This paper presents AEROS, a novel approach to adaptively solve a robust least-squares minimisation problem by adding just a single extra latent parameter. It can be used in the back-end component of the SLAM problem to enable generalised robust cost minimisation by simultaneously estimating the continuous latent parameter along with the set of sensor poses in a single joint optimisation. This leads to a very closely curve fitting on the distribution of the residuals, thereby reducing the effect of outliers. Additionally, we formulate the robust optimisation problem using standard Gaussian factors so that it can be solved by direct application of popular incremental estimation approaches such as iSAM. Experimental results on publicly available synthetic datasets and real LiDAR-SLAM datasets collected from the 2D and 3D LiDAR systems show the competitiveness of our approach with the state-of-the-art techniques and its superiority on real world scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

Subsentence extraction from text using coverage-based deep learning language models

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arXiv 2021年

作者： Lim, JongYoon Sa, Inkyu Ahn, Ho Seok Gasteiger, Norina Lee, Sanghyub John MacDonald, Bruce CARES Department of Electrical Computer and Software Engineering University of Auckland New Zealand CSIRO Data61 Robotics and Autonomous Systems Group Dynamic Platforms

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience and computer science. This stems from its high-degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper. © 2021, CC BY.

关键词： Sentiment analysis

A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform

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arXiv 2022年

作者： Salagame, Adarsh Manjikian, Shoghair Wang, Chenghao Krishnamurthy, Kaushik Venkatesh Pitroda, Shreyansh Gupta, Bibek Jacob, Tobias Mottis, Benjamin Sihite, Eric Ramezani, Milad Ramezani, Alireza The Department of Electrical and Computer Engineering Northeastern University SiliconSynapse Laboratory BostonMA United States The Department of Aerospace Engineering California Institute of Technology PasadenaCA United States The Robotics and Autonomous Systems Group Data61 CSIRO BrisbaneQLD Australia

Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomotion feat can yield ultra-flexible systems in terms of their ability to negotiate their task spaces. The main objective of this paper is to discuss the challenges in achieving multi-modal locomotion, and to report our progress in developing our quadrupedal robot capable of multi-modal locomotion (legged and aerial locomotion), the Husky Carbon. We report the mechanical and electrical components utilized in our robot, in addition to the simulation and experimentation done to achieve our goal in developing a versatile multi-modal robotic platform. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

Demonstrating autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

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arXiv 2023年

作者： Sihite, Eric Slezak, Filip Mandralis, Ioannis Salagame, Adarsh Ramezani, Milad Kalantari, Arash Ramezani, Alireza Gharib, Morteza The Department of Aerospace Engineering California Institute of Technology Pasadena United States The Silicon Synapse Labs Department of Electrical and Computer Engineering Northeastern University Boston United States The Robotics and Autonomous Systems DATA61 Csiro BrisbaneQLD4069 Australia The Jet Propulsion Laboratory Pasadena United States

关键词： Motion planning

Utilising evolutionary algorithms to design granular materials for industrial applications 18

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Utilising evolutionary algorithms to design granular materia...

18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

作者： Delaney, Gary W. Howard, David De Napoli, Krystal Computational Modelling Group Csiro Data61 Melbourne Australia Robotics and Autonomous Systems Group Csiro Data61 Brisbane Australia

ISBN: (纸本)9781728145495

Granular materials, such as sands, soils, grains and powders, are ubiquitous in both natural and artificial systems. They are core to many industrial systems from mining and food production to pharmaceuticals and construction. Granular media display unique properties, including their ability to flow like a liquid at low densities and jam in to a solid state at high densities. Granular materials are used functionally in a number of industrial systems, where for example their insulating, energy absorption, filtration or vibration damping properties are variously exploited. A recent emerging industrial application is to utilise the jamming transition of granular matter (transition from a sold to a liquid) to create functional jammed systems such as universal grippers or soft robotic devices with potential broad impact across many industrial sectors. However, controlling the microscopic properties of such systems to elicit bespoke functional granular systems remains challenging due to the complex relationship between the individual particle morphologies and the related emergent behaviour of the bulk state. Here, we investigate the use of evolution to explore the functional landscapes of granular systems. We employ a superellipsoid representation of the particle shape which allows us to smoothly transition between a large variety of particle aspect ratios and angularities, and investigate the use of multi-component systems alongside homogenous granular arrangements. Results show the ability to successfully characterise a sample design space, and represents an important step towards the creation of bespoke jammed systems with a range of practical applications across broad swathes of industry. © 2019 IEEE.

关键词： Evolutionary algorithms

Non-Linearity Measure for POMDP-based Motion Planning

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arXiv 2020年

作者： Hoerger, Marcus Kurniawati, Hanna Elfes, Alberto Research School of Computer Science Robotics and Autonomous Systems Group Data61 CSIRO

Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with complex dynamics. Most state-of-the-art methods perform search that relies on a large number of forward simulations. For systems with complex dynamics, this generally require costly numerical integrations which significantly slows down the planning process. Linearization-based methods have been proposed that can alleviate the above problem. However, it is not clear how linearization affects the quality of the generated motion strategy, and when such simplifications are admissible. We propose a non-linearity measure, called Statistical-distance-based Non-linearity Measure (SNM), that can identify where linearization is beneficial and where it should be avoided. We show that when the problem is framed as the Partially Observable Markov Decision Process, the value difference between the optimal strategy for the original model and the linearized model can be upper bounded by a function linear in SNM. Comparisons with an existing measure on various scenarios indicate that SNM is more suitable in estimating the effectiveness of linearization-based solvers. To test the applicability of SNM in motion planning, we propose a simple on-line planner that uses SNM as a heuristic to switch between a general and a linearization-based solver. Results on a car-like robot with second order dynamics and 4-DOFs and 7-DOFs torque-controlled manipulators indicate that SNM can appropriately decide if and when a linearization-based solver should be used. Copyright © 2020, The Authors. All rights reserved.

关键词： Linearization

Multi-Agent 3D Map Reconstruction and Change Detection in Microgravity with Free-Flying Robots

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arXiv 2023年

作者： Dinkel, Holly Di, Julia Santos, Jamie Albee, Keenan Borges, Paulo Moreira, Marina Alexandrov, Oleg Coltin, Brian Smith, Trey Department of Aerospace Engineering The University of Illinois Urbana-Champaign UrbanaIL United States Department of Mechanical Engineering Stanford University StanfordCA United States Department of Physics Chalmers University of Technology Gothenburg Sweden Maritime and Multi-Agent Autonomy Group The Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Robotics and Autonomous Systems Group CSIRO Data61 BrisbaneQLD Australia Intelligent Robotics Group The NASA Ames Research Center Moffett FieldCA United States

Assistive free-flyer robots autonomously caring for future crewed outposts—such as NASA’s Astrobee robots on the International Space Station (ISS)—must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. This work presents a framework for multi-agent cooperative mapping and change detection to enable robotic maintenance of space outposts. One agent is used to reconstruct a 3D model of the environment from sequences of images and corresponding depth information. Another agent is used to periodically scan the environment for inconsistencies against the 3D model. Change detection is validated after completing the surveys using real image and pose data collected by Astrobee robots in a ground testing environment and from microgravity aboard the ISS. This work outlines the objectives, requirements, and algorithmic modules for the multi-agent reconstruction system, including recommendations for its use by assistive free-flyers aboard future microgravity outposts. © 2023, CC BY.

关键词： Space stations