arXiv subjects

Michael Bosello

Publications and source records attributed to Michael Bosello.

3 recordsLinked to original sources

arXiv2025

On Your Own: Pro-level Autonomous Drone Racing in Uninstrumented Arenas

Drone technology is proliferating in many industries, including agriculture, logistics, defense, infrastructure, and environmental monitoring. Vision-based autonomy is one of its key enablers, particularly for real-world applications. This is essential for operating in novel, unstructured environments where traditional navigation methods may be unavailable. Autonomous drone racing has become the de facto benchmark for such systems. State-of-the-art research has shown that autonomous systems can surpass human-level performance in racing arenas. However, the direct applicability to commercial and field operations is still limited, as current systems are often trained and evaluated in highly controlled environments. In our contribution, the system's capabilities are analyzed within a controlled environment -- where external tracking is available for ground-truth comparison -- but also demonstrated in a challenging, uninstrumented environment -- where ground-truth measurements were never available. We show that our approach can match the performance of professional human pilots in both scenarios.

cs.RO↗

arXiv2023

Race Against the Machine: a Fully-annotated, Open-design Dataset of Autonomous and Piloted High-speed Flight

Unmanned aerial vehicles, and multi-rotors in particular, can now perform dexterous tasks in impervious environments, from infrastructure monitoring to emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark to develop and evaluate these capabilities. Its challenges include accurate and robust visual-inertial odometry during aggressive maneuvers, complex aerodynamics, and constrained computational resources. As researchers increasingly channel their efforts into it, they also need the tools to timely and equitably compare their results and advances. With this dataset, we want to (i) support the development of new methods and (ii) establish quantitative comparisons for approaches originating from the broader robotics and artificial intelligence communities. We want to provide a one-stop resource that is comprehensive of (i) aggressive autonomous and piloted flight, (ii) high-resolution, high-frequency visual, inertial, and motion capture data, (iii) commands and control inputs, (iv) multiple light settings, and (v) corner-level labeling of drone racing gates. We also release the complete specifications to recreate our flight platform, using commercial off-the-shelf components and the open-source flight controller Betaflight, to democratize drone racing research. Our dataset, open-source scripts, and drone design are available at: https://github.com/tii-racing/drone-racing-dataset

cs.RO↗

arXiv2021

To Charge or to Sell? EV Pack Useful Life Estimation via LSTMs, CNNs, and Autoencoders

Electric vehicles (EVs) are spreading fast as they promise to provide better performance and comfort, but above all, to help face climate change. Despite their success, their cost is still a challenge. Lithium-ion batteries are one of the most expensive EV components, and have become the standard for energy storage in various applications. Precisely estimating the remaining useful life (RUL) of battery packs can encourage their reuse and thus help to reduce the cost of EVs and improve sustainability. A correct RUL estimation can be used to quantify the residual market value of the battery pack. The customer can then decide to sell the battery when it still has a value, i.e., before it exceeds the end of life of the target application, so it can still be reused in a second domain without compromising safety and reliability. This paper proposes and compares two deep learning approaches to estimate the RUL of Li-ion batteries: LSTM and autoencoders vs. CNN and autoencoders. The autoencoders are used to extract useful features, while the subsequent network is then used to estimate the RUL. Compared to what has been proposed so far in the literature, we employ measures to ensure the method's applicability in the actual deployed application. Such measures include (1) avoiding using non-measurable variables as input, (2) employing appropriate datasets with wide variability and different conditions, and (3) predicting the remaining ampere-hours instead of the number of cycles. The results show that the proposed methods can generalize on datasets consisting of numerous batteries with high variance.

cs.LG↗