arXiv subjects

Samuel Burns

Publications and source records attributed to Samuel Burns.

3 recordsLinked to original sources

arXiv2024

Optimized Kalman Filter based State Estimation and Height Control in Hopping Robots

Rotor-based hopping locomotion significantly improves efficiency and operation time as compared to purely flying systems; where most hopping robots use the liftoff states and an assumed ballistic trajectory to determine the hopping height. However, significant aerial phase force (e.g., thrust and drag) can invalidate this assumption and lead to poor estimation performance. To combat this issue, a group has implemented multiple sensors (active and passive optical, inertial, and contact) and significant computational power to achieve full state estimation. This, however, poses a significant challenge to the development of light-weight, high-performance, low observable, jamming and electronic interference resistant hopping systems; especially in perceptually degraded environments (e.g., dust, smoke). Here we show a training procedure for a coupled hopping phase and Kalman filter-based vertical state estimator, requiring only inertial measurements, which is able to learn the characteristics of the target system, sensors, locomotion behaviors, environment, and acceleration measurement aliasing conditions. The resulting estimator, given hop heights up to 4 m and velocities up to $\pm7$ m/s, achieves a mean absolute percent error in the hop apex height of 12.5% with an aerial trajectory average normalized mean absolute error in position and velocity of 19% and 16.5%, respectively; while operating at 840 Hz, on a dual-core 240 MHz processor, with a total robot mass of 672 g. Due to the low mass and computational power, the presented estimator could also be used as a degraded operational mode in cases of sensor damage, malfunction, or occlusion in more complex robots.

cs.RO↗

arXiv2023

Design and Control of a High-Performance Hopping Robot

Jumping and hopping locomotion are efficient means of traversing unstructured rugged terrain with the former being the focus of roboticists; a focus that has recently been changing. This focus has led to significant performance and understanding in jumping robots but with limited practical applications as they require significant time between jumps to store energy, thus relegating jumping to a secondary role in locomotion. Hopping locomotion, however, can preserve and transfer energy to subsequent hops without long energy storage periods. However, incorporating the performance observed in jumping systems into their hopping counterparts is an ongoing challenge. To date, hopping robots typically operate around 1 meter with a maximum of 1.63 m whereas jumping robots have reached heights of 30 m. This is due to the added design and control complexity inherent in developing a system able to input and store the necessary energy while withstanding the forces involved and managing the system's state. Here we report hopping robot design principles for efficient, robust, high-specific energy, and high-energy input systems through analytical, simulation, and experimental results. The resulting robot (MultiMo-MHR) can hop over 4 meters ($\sim$2.4x the current state-of-the-art) and is designed to withstand impact at terminal velocity ($\geq 30.7$ m).

cs.RO↗

arXiv2020

Interactive Multi-User 3D Visual Analytics in Augmented Reality

This publication reports on a research project in which we set out to explore the advantages and disadvantages augmented reality (AR) technology has for visual data analytics. We developed a prototype of an AR data analytics application, which provides users with an interactive 3D interface, hand gesture-based controls and multi-user support for a shared experience, enabling multiple people to collaboratively visualize, analyze and manipulate data with high dimensional features in 3D space. Our software prototype, called DataCube, runs on the Microsoft HoloLens - one of the first true stand-alone AR headsets, through which users can see computer-generated images overlaid onto real-world objects in the user's physical environment. Using hand gestures, the users can select menu options, control the 3D data visualization with various filtering and visualization functions, and freely arrange the various menus and virtual displays in their environment. The shared multi-user experience allows all participating users to see and interact with the virtual environment, changes one user makes will become visible to the other users instantly. As users engage together they are not restricted from observing the physical world simultaneously and therefore they can also see non-verbal cues such as gesturing or facial reactions of other users in the physical environment. The main objective of this research project was to find out if AR interfaces and collaborative analysis can provide an effective solution for data analysis tasks, and our experience with our prototype system confirms this.

cs.HC↗