Zobrazeno 1 - 10
of 255
pro vyhledávání: '"Schoellig, Angela"'
Autor:
Qin, Chao, Chen, Jingxiang, Lin, Yifan, Goudar, Abhishek, Schoellig, Angela P., Liu, Hugh H. -T.
Time-critical tasks such as drone racing typically cover large operation areas. However, it is difficult and computationally intensive for current time-optimal motion planners to accommodate long flight distances since a large yet unknown number of k
Externí odkaz:
http://arxiv.org/abs/2407.17944
Autonomous robots navigating in changing environments demand adaptive navigation strategies for safe long-term operation. While many modern control paradigms offer theoretical guarantees, they often assume known extrinsic safety constraints, overlook
Externí odkaz:
http://arxiv.org/abs/2404.14546
Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement learning. Here, safety is defined as staying in a pre-defined set, the s
Externí odkaz:
http://arxiv.org/abs/2404.12329
Learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully e
Externí odkaz:
http://arxiv.org/abs/2403.09504
In this technical report, we compare treating an IMU as an input to a motion model against treating it as a measurement of the state in a continuous-time state estimation framework. Treating IMU measurements as inputs to a motion model and then prein
Externí odkaz:
http://arxiv.org/abs/2403.05968
In this paper, we consider a Micro Aerial Vehicle (MAV) system teleoperated by a non-expert and introduce a perceptive safety filter that leverages Control Barrier Functions (CBFs) in conjunction with Visual-Inertial Simultaneous Localization and Map
Externí odkaz:
http://arxiv.org/abs/2403.04331
In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computational complexity during preintegration and interpolation. We use a wh
Externí odkaz:
http://arxiv.org/abs/2402.06174
Autor:
Heins, Adam, Schoellig, Angela P.
We present a controller for quasistatic robotic planar pushing with single-point contact using only force feedback to sense the pushed object. We consider an omnidirectional mobile robot pushing an object (the "slider") along a given path, where the
Externí odkaz:
http://arxiv.org/abs/2401.17517
Autor:
Dawood, Murad, Pan, Sicong, Dengler, Nils, Zhou, Siqi, Schoellig, Angela P., Bennewitz, Maren
In recent years, formation control of unmanned vehicles has received considerable interest, driven by the progress in autonomous systems and the imperative for multiple vehicles to carry out diverse missions. In this paper, we address the problem of
Externí odkaz:
http://arxiv.org/abs/2312.12861
Providing safety guarantees for learning-based controllers is important for real-world applications. One approach to realizing safety for arbitrary control policies is safety filtering. If necessary, the filter modifies control inputs to ensure that
Externí odkaz:
http://arxiv.org/abs/2312.09565