Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Hönig, Wolfgang"'
Autor:
Ortiz-Haro, Joaquim, Hönig, Wolfgang, Hartmann, Valentin N., Toussaint, Marc, Righetti, Ludovic
Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic constraints makes the motion planning problem significantly harder, as it
Externí odkaz:
http://arxiv.org/abs/2403.10745
Autor:
Xie, Fengze, Dominguez-Kuhne, Marcus, Riviere, Benjamin, Song, Jialin, Hönig, Wolfgang, Chung, Soon-Jo, Yue, Yisong
In this paper, we present a method of multi-robot motion planning by biasing centralized, sampling-based tree search with decentralized, data-driven steer and distance heuristics. Over a range of robot and obstacle densities, we evaluate the plain Ra
Externí odkaz:
http://arxiv.org/abs/2311.12385
Motion planning for robotic systems with complex dynamics is a challenging problem. While recent sampling-based algorithms achieve asymptotic optimality by propagating random control inputs, their empirical convergence rate is often poor, especially
Externí odkaz:
http://arxiv.org/abs/2311.03553
Autor:
Dimmig, Cora A., Silano, Giuseppe, McGuire, Kimberly, Gabellieri, Chiara, Hönig, Wolfgang, Moore, Joseph, Kobilarov, Marin
Uncrewed Aerial Vehicle (UAV) research faces challenges with safety, scalability, costs, and ecological impact when conducting hardware testing. High-fidelity simulators offer a vital solution by replicating real-world conditions to enable the develo
Externí odkaz:
http://arxiv.org/abs/2311.02296
We propose a motion planner for cable-driven payload transportation using multiple unmanned aerial vehicles (UAVs) in an environment cluttered with obstacles. Our planner is kinodynamic, i.e., it considers the full dynamics model of the transporting
Externí odkaz:
http://arxiv.org/abs/2310.03394
This paper presents a multi-robot kinodynamic motion planner that enables a team of robots with different dynamics, actuation limits, and shapes to reach their goals in challenging environments. We solve this problem by combining Conflict-Based Searc
Externí odkaz:
http://arxiv.org/abs/2309.16445
Autor:
Teetaert, Spencer, Zhao, Wenda, Xinyuan, Niu, Zahir, Hashir, Leong, Huiyu, Hidalgo, Michel, Puga, Gerardo, Lorente, Tomas, Espinosa, Nahuel, Carrasco, John Alejandro Duarte, Zhang, Kaizheng, Di, Jian, Jin, Tao, Li, Xiaohan, Zhou, Yijia, Liang, Xiuhua, Zhang, Chenxu, Loquercio, Antonio, Zhou, Siqi, Brunke, Lukas, Greeff, Melissa, Hoenig, Wolfgang, Panerati, Jacopo, Schoellig, Angela P.
Shared benchmark problems have historically been a fundamental driver of progress for scientific communities. In the context of academic conferences, competitions offer the opportunity to researchers with different origins, backgrounds, and levels of
Externí odkaz:
http://arxiv.org/abs/2308.16743
Multi-robot collision-free and deadlock-free navigation in cluttered environments with static and dynamic obstacles is a fundamental problem for many applications. We introduce MRNAV, a framework for planning and control to effectively navigate in su
Externí odkaz:
http://arxiv.org/abs/2308.13499
Autonomous flying robots, such as multirotors, often rely on deep learning models that make predictions based on a camera image, e.g. for pose estimation. These models can predict surprising results if applied to input images outside the training dom
Externí odkaz:
http://arxiv.org/abs/2308.00344
Autonomous flying robots, e.g. multirotors, often rely on a neural network that makes predictions based on a camera image. These deep learning (DL) models can compute surprising results if applied to input images outside the training domain. Adversar
Externí odkaz:
http://arxiv.org/abs/2305.12859