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Our team developed a riding ballbot (called PURE) that is dynamically stable, omnidirectional, and driven by lean-to-steer control. A hands-free admittance control scheme (HACS) was previously integrated to allow riders with different torso functions to control the robot's movements via torso leaning and twisting. Such an interface requires motor coordination skills and could result in collisions with obstacles due to low proficiency. Hence, a shared controller (SC) that limits the speed of PURE could be helpful to ensure the safety of riders. However, the self-balancing dynamics of PURE could result in a weak control authority of its motion, in which the torso motion of the rider could easily result in poor tracking of the command speed dictated by the shared controller. Thus, we proposed an interactive hands-free admittance control scheme (iHACS), which added two modules to HACS to improve the speed-tracking performance of PURE: control gain personalization module and interaction compensation module. Human riding tests of simple tasks, idle-keeping and speed-limiting, were conducted to compare the performance of HACS and iHACS. Two manual wheelchair users and two able-bodied individuals participated in this study. They were instructed to use "adversarial" torso motions that would tax the SC's ability to keep the ballbot idling or below a set speed. In the idle-keeping tasks, iHACS demonstrated minimal translational motion and low command speed tracking RMSE, even with significant torso lean angles. During the speed-limiting task with command speed saturated at 0.5 m/s, the system achieved an average maximum speed of 1.1 m/s with iHACS, compared with that of over 1.9 m/s with HACS. These results suggest that iHACS can enhance PURE's control authority over the rider, which enables PURE to provide physical interactions back to the rider and results in a collaborative rider-robot synergy.

This study introduces the development of hands-free control schemes for a riding ballbot, designed to allow riders including manual wheelchair users to control its movement through torso leaning and twisting. The hardware platform, Personal Unique Rolling Experience (PURE), utilizes a ballbot drivetrain, a dynamically stable mobile robot that uses a ball as its wheel to provide omnidirectional maneuverability. To accommodate users with varying torso motion functions, the hanads-free control scheme should be adjustable based on the rider's torso function and personal preferences. Therefore, concepts of (a) impedance control and (b) admittance control were integrated into the control scheme. A duo-agent optimization framework was utilized to assess the efficiency of this rider-ballbot system for a safety-critical task: braking from 1.4 m/s. The candidate control schemes were further implemented in the physical robot hardware and validated with two experienced users, demonstrating the efficiency and robustness of the hands-free admittance control scheme (HACS). This interface, which utilized physical human-robot interaction (pHRI) as the input, resulted in lower braking effort and shorter braking distance and time. Subsequently, 12 novice participants (six able-bodied users and six manual wheelchair users) with different levels of torso motion capability were then recruited to benchmark the braking performance with HACS. The indoor navigation capability of PURE was further demonstrated with these participants in courses simulating narrow hallways, tight turns, and navigation through static and dynamic obstacles. By exploiting pHRI, the proposed admittance-style control scheme provided effective control of the ballbot via torso motions. This interface enables PURE to provide a personal unique rolling experience to manual wheelchair users for safe and agile indoor navigation.

Backstepping Control of Linear and Nonlinear Systems (BaCLNS) is a Python package developed to automate the design, simulation, and analysis of backstepping control laws for both linear and nonlinear control-affine systems. By providing a standardized framework, BaCLNS simplifies the process of deriving backstepping controllers, making this powerful control technique more accessible to engineers, researchers, and educators. The package handles complex system dynamics, ensuring robust stabilization even in the presence of significant nonlinearities. BaCLNS's modular design allows users to define custom control systems, simulate their behavior , and visualize the results all within a user-friendly environment. The effectiveness of the package is demonstrated through a series of illustrative examples, ranging from simple linear systems to chaotic nonlinear systems, including the Vaidyanathan Jerk System, the pendulum and the Van der Pol Oscillator., Comment: 9 pages, 13 Figures , Submitting to Journal of SoftwareX

This study introduces a shared-control approach for collision avoidance in a self-balancing riding ballbot, called PURE, marked by its dynamic stability, omnidirectional movement, and hands-free interface. Integrated with a sensor array and a novel Passive Artificial Potential Field (PAPF) method, PURE provides intuitive navigation with deceleration assistance and haptic/audio feedback, effectively mitigating collision risks. This approach addresses the limitations of traditional APF methods, such as control oscillations and unnecessary speed reduction in challenging scenarios. A human-robot interaction experiment, with 20 manual wheelchair users and able-bodied individuals, was conducted to evaluate the performance of indoor navigation and obstacle avoidance with the proposed shared-control algorithm. Results indicated that shared-control significantly reduced collisions and cognitive load without affecting travel speed, offering intuitive and safe operation. These findings highlight the shared-control system's suitability for enhancing collision avoidance in self-balancing mobility devices, a relatively unexplored area in assistive mobility research., Comment: 7 pages and 7 figures, IEEE ICRA format

The emergence of tethered drones represents a major advancement in unmanned aerial vehicles (UAVs) offering solutions to key limitations faced by traditional drones. This article explores the potential of tethered drones with a particular focus on their ability to tackle issues related to battery life constraints and data latency commonly experienced by battery operated drones. Through their connection to a ground station via a tether, autonomous tethered drones provide continuous power supply and a secure direct data transmission link facilitating prolonged operational durations and real time data transfer. These attributes significantly enhance the effectiveness and dependability of drone missions in scenarios requiring extended surveillance, continuous monitoring and immediate data processing needs. Examining the advancements, operational benefits and potential future progressions associated with tethered drones, this article shows their increasing significance across various sectors and their pivotal role in pushing the boundaries of current UAV capabilities. The emergence of tethered drone technology not only addresses existing obstacles but also paves the way for new innovations within the UAV industry., Comment: 4 pages, 1 figure, 1 table, 28 references

Spannotation is an open source user-friendly tool developed for image annotation for semantic segmentation specifically in autonomous navigation tasks. This study provides an evaluation of Spannotation, demonstrating its effectiveness in generating accurate segmentation masks for various environments like agricultural crop rows, off-road terrains and urban roads. Unlike other popular annotation tools that requires about 40 seconds to annotate an image for semantic segmentation in a typical navigation task, Spannotation achieves similar result in about 6.03 seconds. The tools utility was validated through the utilization of its generated masks to train a U-Net model which achieved a validation accuracy of 98.27% and mean Intersection Over Union (mIOU) of 96.66%. The accessibility, simple annotation process and no-cost features have all contributed to the adoption of Spannotation evident from its download count of 2098 (as of February 25, 2024) since its launch. Future enhancements of Spannotation aim to broaden its application to complex navigation scenarios and incorporate additional automation functionalities. Given its increasing popularity and promising potential, Spannotation stands as a valuable resource in autonomous navigation and semantic segmentation. For detailed information and access to Spannotation, readers are encouraged to visit the project's GitHub repository at https://github.com/sof-danny/spannotation, Comment: 8 pages, 6 figures, 1 table, 1 pseudo code (algorithm), 55 references

Ensuring the robustness of lane detection systems is essential for the reliability of autonomous vehicles, particularly in the face of diverse weather conditions. While numerous algorithms have been proposed, addressing challenges posed by varying weather remains an ongoing issue. Geometric-based lane detection methods, rooted in the inherent properties of road geometry, provide enhanced generalizability. However, these methods often require manual parameter tuning to accommodate it fluctuating illumination and weather conditions. Conversely, learning-based approaches, trained on pre-labeled datasets, excel in localizing intricate and curved lane configurations but grapple with the absence of diverse weather datasets. This paper introduces a promising hybrid approach that merges the strengths of both methodologies. A novel adaptive preprocessing method is proposed in this work. Utilizing a fuzzy inference system (FIS), the algorithm dynamically adjusts parameters in geometric-based image processing functions and enhances adaptability to diverse weather conditions. Notably, this preprocessing algorithm is designed to seamlessly integrate with all learning-based lane detection models. When implemented in conjunction with CNN-based models, the hybrid approach demonstrates commendable generalizability across weather conditions and adaptability to complex lane configurations. Rigorous testing on datasets featuring challenging weather conditions showcases the proposed method's significant improvements over existing models, underscoring its efficacy in addressing the persistent challenges associated with lane detection in adverse weather scenarios., Comment: 10 pages, 6 figures

The pyramid wavefront sensor (PyWFS) has become increasingly popular to use in adaptive optics (AO) systems due to its high sensitivity. The main drawback of the PyWFS is that it is inherently nonlinear, which means that classic linear wavefront reconstruction techniques face a significant reduction in performance at high wavefront errors, particularly when the pyramid is unmodulated. In this paper, we consider the potential use of neural networks (NNs) to replace the widely used matrix vector multiplication (MVM) control. We aim to test the hypothesis that the neural network (NN)'s ability to model nonlinearities will give it a distinct advantage over MVM control. We compare the performance of a MVM linear reconstructor against a dense NN, using daytime data acquired on the Subaru Coronagraphic Extreme Adaptive Optics system (SCExAO) instrument. In a first set of experiments, we produce wavefronts generated from 14 Zernike modes and the PyWFS responses at different modulation radii (25, 50, 75, and 100 mas). We find that the NN allows for a far more precise wavefront reconstruction at all modulations, with differences in performance increasing in the regime where the PyWFS nonlinearity becomes significant. In a second set of experiments, we generate a dataset of atmosphere-like wavefronts, and confirm that the NN outperforms the linear reconstructor. The SCExAO real-time computer software is used as baseline for the latter. These results suggest that NNs are well positioned to improve upon linear reconstructors and stand to bring about a leap forward in AO performance in the near future., Comment: Accepted in Publications of the Astronomical Society of the Pacific, September 2023

Determining the drivable area, or free space segmentation, is critical for mobile robots to navigate indoor environments safely. However, the lack of coherent markings and structures (e.g., lanes, curbs, etc.) in indoor spaces places the burden of traversability estimation heavily on the mobile robot. This paper explores the use of a self-supervised one-shot texture segmentation framework and an RGB-D camera to achieve robust drivable area segmentation. With a fast inference speed and compact size, the developed model, MOSTS is ideal for real-time robot navigation and various embedded applications. A benchmark study was conducted to compare MOSTS's performance with existing one-shot texture segmentation models to evaluate its performance. Additionally, a validation dataset was built to assess MOSTS's ability to perform texture segmentation in the wild, where it effectively identified small low-lying objects that were previously undetectable by depth measurements. Further, the study also compared MOSTS's performance with two State-Of-The-Art (SOTA) indoor semantic segmentation models, both quantitatively and qualitatively. The results showed that MOSTS offers comparable accuracy with up to eight times faster inference speed in indoor drivable area segmentation., Comment: 10 pages, 12 figures

We present the direct-imaging discovery of a substellar companion in orbit around a Sun-like star member of the Hyades open cluster. So far, no other substellar companions have been unambiguously confirmed via direct imaging around main-sequence stars in Hyades. The star HIP 21152 is an accelerating star as identified by the astrometry from the Gaia and Hipparcos satellites. We have detected the companion, HIP 21152 B, in multi-epoch using the high-contrast imaging from SCExAO/CHARIS and Keck/NIRC2. We have also obtained the stellar radial-velocity data from the Okayama 188cm telescope. The CHARIS spectroscopy reveals that HIP 21152 B's spectrum is consistent with the L/T transition, best fit by an early T dwarf. Our orbit modeling determines the semi-major axis and the dynamical mass of HIP 21152 B to be 17.5$^{+7.2}_{-3.8}$ au and 27.8$^{+8.4}_{-5.4}$ $M_{\rm{Jup}}$, respectively. The mass ratio of HIP 21152 B relative to its host is $\approx$2\%, near the planet/brown dwarf boundary suggested from recent surveys. Mass estimates inferred from luminosity evolution models are slightly higher (33--42 $M_{\rm{Jup}}$). With a dynamical mass and a well-constrained age due to the system's Hyades membership, HIP 21152 B will become a critical benchmark in understanding the formation, evolution, and atmosphere of a substellar object as a function of mass and age. Our discovery is yet another key proof-of-concept for using precision astrometry to select direct imaging targets., Comment: 21 pages (11 pages in main body), 8 figures (4 figures in main body). Accepted for Publication in ApJL at July 9, 2022 (UT)

Photonic lanterns allow the decomposition of highly multimodal light into a simplified modal basis such as single-moded and/or few-moded. They are increasingly finding uses in astronomy, optics and telecommunications. Calculating propagation through a photonic lantern using traditional algorithms takes $\sim 1$ hour per simulation on a modern CPU. This paper demonstrates that neural networks can bridge the disparate opto-electronic systems, and when trained can achieve a speed-up of over 5 orders of magnitude. We show that this approach can be used to model photonic lanterns with manufacturing defects as well as successfully generalising to polychromatic data. We demonstrate two uses of these neural network models, propagating seeing through the photonic lantern as well as performing global optimisation for purposes such as photonic lantern funnels and photonic lantern nullers., Comment: 20 pages, 14 figures

Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are used for a wide variety of missions related to exploration and scientific research. Successful navigation by these systems requires a good localization system. Kalman filter based localization techniques have been prevalent since the early 1960s and extensive research has been carried out using them, both in development and in design. It has been found that the use of a dynamic model (instead of a kinematic model) in the Kalman filter can lead to more accurate predictions, as the dynamic model takes the forces acting on the AUV into account. Presented in this paper is a motion-predictive extended Kalman filter (EKF) for AUVs using a simplified dynamic model. The dynamic model is derived first and then it was simplified for a RexROV, a type of submarine vehicle used in simple underwater exploration, inspection of subsea structures, pipelines and shipwrecks. The filter was implemented with a simulated vehicle in an open-source marine vehicle simulator called UUV Simulator and the results were compared with the ground truth. The results show good prediction accuracy for the dynamic filter, though improvements are needed before the EKF can be used on real-time. Some perspective and discussion on practical implementation is presented to show the next steps needed for this concept., Comment: Preprint for ASME Journal for Mechanisms and Robotics, not peer reviewed yet

One key advantage of single-mode photonic technologies for interferometric use is their ability to easily scale to an ever increasing number of inputs without a major increase in the overall device size, compared to traditional bulk optics. This is particularly important for the upcoming ELT generation of telescopes currently under construction. We demonstrate the fabrication and characterization of a novel hybridized photonic interferometer, with 8 simultaneous inputs, forming 28 baselines, the largest amount to-date. Utilizing different photonic fabrication technologies, we combine a 3D pupil remapper with a planar 8-port ABCD pairwise beam combiner, along with the injection optics necessary for telescope use, into a single integrated monolithic device. We successfully realized a combined device called Dragonfly, which demonstrates a raw instrumental closure-phase stability down to $0.9^{\circ}$ over $8\pi$ phase piston error, relating to a detection contrast of $\sim6.5\times 10^{-4}$ on an Adaptive-Optics corrected 8-m telescope. This prototype successfully demonstrates advanced hybridization and packaging techniques necessary for on-sky use for high-contrast detection at small inner working angles, ideally complementing what can currently be achieved using coronagraphs., Comment: 10 pages, 10 figures, 1 table, submitted to Astrophotonics Special Issue in Applied Optics

There are two puzzles surrounding the Pleiades, or Seven Sisters. First, why are the mythological stories surrounding them, typically involving seven young girls being chased by a man associated with the constellation Orion, so similar in vastly separated cultures, such as the Australian Aboriginal cultures and Greek mythology? Second, why do most cultures call them "Seven Sisters" even though most people with good eyesight see only six stars? Here we show that both these puzzles may be explained by a combination of the great antiquity of the stories combined with the proper motion of the stars, and that these stories may predate the departure of most modern humans out of Africa around 100,000 BC., Comment: Accepted for publication in "Advancing Cultural Astronomy: Studies in Honour of Clive Ruggles", publ. Springer

High-contrast imaging (HCI) observations of exoplanets can be limited by the island effect (IE). On the current generation of telescopes, the IE becomes a severe problem when the ground wind speed is below a few meters per second. This is referred to as the low-wind effect (LWE). The LWE severely distorts the point spread function (PSF), significantly lowering the Strehl ratio and degrading the contrast. In this article, we aim to show that the focal-plane wavefront sensing (FPWFS) algorithm, Fast and Furious (F&F), can be used to measure and correct the IE/LWE. We deployed the algorithm on the SCExAO HCI instrument at the Subaru Telescope using the internal near-infrared camera in H-band. We tested F&F with the internal source, and it was deployed on-sky to test its performance with the full end-to-end system and atmospheric turbulence. The performance of the algorithm was evaluated by two metrics based on the PSF quality: 1) the Strehl ratio approximation ($SRA$), and 2) variance of the normalized first Airy ring ($VAR$). Random LWE phase screens with a peak-to-valley wavefront error between 0.4 $\mu$m and 2 $\mu$m were all corrected to a $SRA$ $>$90\% and an $VAR\lessapprox0.05$. Furthermore, the on-sky results show that F&F is able to improve the PSF quality during very challenging atmospheric conditions (1.3-1.4'' seeing at 500 nm). Closed-loop tests show that F&F is able to improve the $VAR$ from 0.27 to 0.03 and therefore significantly improve the symmetry of the PSF. Simultaneous observations of the PSF in the {optical} ($\lambda = $ 750 nm, $\Delta \lambda =$ 50 nm) show that during these tests we were correcting aberrations common to the optical and NIR paths within SCExAO. Going forward, the algorithm is suitable for incorporation into observing modes, which will enable PSFs of higher quality and stability during science observations., Comment: Accepted for publication in A&A. Abstract shortened for arXiv

Adaptive optics (AO) is critical in astronomy, optical communications and remote sensing to deal with the rapid blurring caused by the Earth's turbulent atmosphere. But current AO systems are limited by their wavefront sensors, which need to be in an optical plane non-common to the science image and are insensitive to certain wavefront-error modes. Here we present a wavefront sensor based on a photonic lantern fibre-mode-converter and deep learning, which can be placed at the same focal plane as the science image, and is optimal for single-mode fibre injection. By measuring the intensities of an array of single-mode outputs, both phase and amplitude information on the incident wavefront can be reconstructed. We demonstrate the concept with simulations and an experimental realisation wherein Zernike wavefront errors are recovered from focal-plane measurements to a precision of $5.1\times10^{-3}\;\pi$ radians root-mean-squared-error.

The characterisation of exoplanets is critical to understanding planet diversity and formation, their atmospheric composition and the potential for life. This endeavour is greatly enhanced when light from the planet can be spatially separated from that of the host star. One potential method is nulling interferometry, where the contaminating starlight is removed via destructive interference. The GLINT instrument is a photonic nulling interferometer with novel capabilities that has now been demonstrated in on-sky testing. The instrument fragments the telescope pupil into sub-apertures that are injected into waveguides within a single-mode photonic chip. Here, all requisite beam splitting, routing and recombination is performed using integrated photonic components. We describe the design, construction and laboratory testing of our GLINT pathfinder instrument. We then demonstrate the efficacy of this method on sky at the Subaru Telescope, achieving a null-depth precision on sky of $\sim10^{-4}$ and successfully determining the angular diameter of stars (via their null-depth measurements) to milli-arcsecond accuracy. A statistical method for analysing such data is described, along with an outline of the next steps required to deploy this technique for cutting-edge science.

This concept paper outlines some recent efforts toward the design and development of user-integrated semi-autonomous home-sized lawn mowing systems from a systems engineering perspective. This is an important and emerging field of study within the robotics and systems engineering communities. The work presented includes a review of current progress on this problem, a discussion of the problem from a systems engineering perspective, a general system architecture developed by the authors, and a preliminary set of design requirements. This work is meant to provide a baseline and motivation for the further development and refinement of these systems within the systems engineering and robotics communities and is relevant to both academic and commercial research., Comment: 11 pages, 8 figures, and 32 references

We present the first-ever images of the circumstellar environment of the carbon-rich AGB star II Lup in the infrared and sub-mm wavelengths, and the discovery of the envelope's non-spherical morphology with the use of high-angular resolution imaging techniques with the sparse aperture masking mode on NACO/VLT (that enables diffraction limited resolution from a single telescope) and with ALMA. We have successfully recovered images in $Ks$ (2.18$\mu$m), $L'$ (3.80$\mu$m) and $M'$ (4.78$\mu$m), that revealed the non-spherical morphology of the circumstellar envelope around II Lup. The stellar surface of the AGB star is unresolved (i.e. $\leq30$ mas in $Ks$) however the detected structure extends up to 110 mas from the star in all filters. Clumps have been found in the $Ks$ maps, while at lower emission levels a hook-like structure appears to extend counter-clockwise from the south. At larger spatial scales, the circumstellar envelope extends up to approximately 23 arcsec, while its shape suggests a spiral at four different molecules, namely CO, SiO, CS and HC$_3$N, with an average arm spacing of 1.7 arcsec which would imply an orbital period of 128 years for a distance of 590pc., Comment: 17 pages, 14 figures, accepted by MNRAS

The circumstellar environment of L2 Pup, an oxygen-rich semiregular variable, was observed to understand the evolution of mass loss and the shaping of ejecta in the late stages of stellar evolution. High-angular resolution observations from a single 8 m telescope were obtained using aperture masking in the near-infrared (1.64, 2.30 and 3.74 $\rm\mu m$) on the NACO/VLT, both in imaging and polarimetric modes. The aperture-masking images of L2 Pup at 2.30 $\rm\mu m$ show a resolved structure that resembles a toroidal structure with a major axis of ~140 milliarcseconds (mas) and an east-west orientation. Two clumps can be seen on either side of the star, ~65 mas from the star, beyond the edge of the circumstellar envelope (estimated diameter is ~27 mas), while a faint, hook-like structure appear toward the northeast. The patterns are visible both in the imaging and polarimetric mode, although the latter was only used to measure the total intensity (Stokes I). The overall shape of the structure is similar at the 3.74 $\rm\mu m$ pseudo-continuum (dust emission), where the clumps appear to be embedded within a dark, dusty lane. The faint, hook-like patterns are also seen at this wavelength, extending northeast and southwest with the central, dark lane being an apparent axis of symmetry. We interpret the structure as a circumstellar torus with inner radius of 4.2 au. With a rotation velocity of 10 km s$^{-1}$ as suggested by the SiO maser profile, we estimate a stellar mass of 0.7 M$_\odot$.}, Comment: 9 pages, 7 figures, to be published in A&A