Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Tordesillas, Jesus"'
Autor:
Kondo, Kota, Tewari, Claudius T., Tagliabue, Andrea, Tordesillas, Jesus, Lusk, Parker C., How, Jonathan P.
In decentralized multiagent trajectory planners, agents need to communicate and exchange their positions to generate collision-free trajectories. However, due to localization errors/uncertainties, trajectory deconfliction can fail even if trajectorie
Externí odkaz:
http://arxiv.org/abs/2406.10060
The emergence of differentiable simulators enabling analytic gradient computation has motivated a new wave of learning algorithms that hold the potential to significantly increase sample efficiency over traditional Reinforcement Learning (RL) methods
Externí odkaz:
http://arxiv.org/abs/2404.02887
This paper presents RAYEN, a framework to impose hard convex constraints on the output or latent variable of a neural network. RAYEN guarantees that, for any input or any weights of the network, the constraints are satisfied at all times. Compared to
Externí odkaz:
http://arxiv.org/abs/2307.08336
Autor:
Kondo, Kota, Figueroa, Reinaldo, Rached, Juan, Tordesillas, Jesus, Lusk, Parker C., How, Jonathan P.
Communication delays can be catastrophic for multiagent systems. However, most existing state-of-the-art multiagent trajectory planners assume perfect communication and therefore lack a strategy to rectify this issue in real-world environments. To ad
Externí odkaz:
http://arxiv.org/abs/2303.06222
Autor:
Kondo, Kota, Tordesillas, Jesus, Figueroa, Reinaldo, Rached, Juan, Merkel, Joseph, Lusk, Parker C., How, Jonathan P.
Although communication delays can disrupt multiagent systems, most of the existing multiagent trajectory planners lack a strategy to address this issue. State-of-the-art approaches typically assume perfect communication environments, which is hardly
Externí odkaz:
http://arxiv.org/abs/2209.13667
Autor:
Tordesillas, Jesus, How, Jonathan P.
This paper presents Deep-PANTHER, a learning-based perception-aware trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments. Given the current state of the UAV, and the predicted trajectory and size of the obstacle, Deep-PANTHE
Externí odkaz:
http://arxiv.org/abs/2209.01268
Autor:
Agha, Ali, Otsu, Kyohei, Morrell, Benjamin, Fan, David D., Thakker, Rohan, Santamaria-Navarro, Angel, Kim, Sung-Kyun, Bouman, Amanda, Lei, Xianmei, Edlund, Jeffrey, Ginting, Muhammad Fadhil, Ebadi, Kamak, Anderson, Matthew, Pailevanian, Torkom, Terry, Edward, Wolf, Michael, Tagliabue, Andrea, Vaquero, Tiago Stegun, Palieri, Matteo, Tepsuporn, Scott, Chang, Yun, Kalantari, Arash, Chavez, Fernando, Lopez, Brett, Funabiki, Nobuhiro, Miles, Gregory, Touma, Thomas, Buscicchio, Alessandro, Tordesillas, Jesus, Alatur, Nikhilesh, Nash, Jeremy, Walsh, William, Jung, Sunggoo, Lee, Hanseob, Kanellakis, Christoforos, Mayo, John, Harper, Scott, Kaufmann, Marcel, Dixit, Anushri, Correa, Gustavo, Lee, Carlyn, Gao, Jay, Merewether, Gene, Maldonado-Contreras, Jairo, Salhotra, Gautam, Da Silva, Maira Saboia, Ramtoula, Benjamin, Kubo, Yuki, Fakoorian, Seyed, Hatteland, Alexander, Kim, Taeyeon, Bartlett, Tara, Stephens, Alex, Kim, Leon, Bergh, Chuck, Heiden, Eric, Lew, Thomas, Cauligi, Abhishek, Heywood, Tristan, Kramer, Andrew, Leopold, Henry A., Choi, Chris, Daftry, Shreyansh, Toupet, Olivier, Wee, Inhwan, Thakur, Abhishek, Feras, Micah, Beltrame, Giovanni, Nikolakopoulos, George, Shim, David, Carlone, Luca, Burdick, Joel
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques uti
Externí odkaz:
http://arxiv.org/abs/2103.11470
Autor:
Tordesillas, Jesus, How, Jonathan P.
This paper presents PANTHER, a real-time perception-aware (PA) trajectory planner for multirotor-UAVs (Unmanned Aerial Vehicles) in dynamic environments. PANTHER plans trajectories that avoid dynamic obstacles while also keeping them in the sensor fi
Externí odkaz:
http://arxiv.org/abs/2103.06372
Autor:
Tagliabue, Andrea, Tordesillas, Jesus, Cai, Xiaoyi, Santamaria-Navarro, Angel, How, Jonathan P., Carlone, Luca, Agha-mohammadi, Ali-akbar
State estimation for robots navigating in GPS-denied and perceptually-degraded environments, such as underground tunnels, mines and planetary subsurface voids, remains challenging in robotics. Towards this goal, we present LION (Lidar-Inertial Observ
Externí odkaz:
http://arxiv.org/abs/2102.03443
Autor:
Thakker, Rohan, Alatur, Nikhilesh, Fan, David D., Tordesillas, Jesus, Paton, Michael, Otsu, Kyohei, Toupet, Olivier, Agha-mohammadi, Ali-akbar
We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and
Externí odkaz:
http://arxiv.org/abs/2101.11110