Zobrazeno 1 - 10
of 3 000
pro vyhledávání: '"Jagtap P"'
Fixed-wing UAVs have transformed the transportation system with their high flight speed and long endurance, yet their safe operation in increasingly cluttered environments depends heavily on effective collision avoidance techniques. This paper presen
Externí odkaz:
http://arxiv.org/abs/2407.19335
Autor:
Bredol, Philipp, David, Felix, Jagtap, Nagesh S., Klaß, Yannick S., Astakhov, Georgy V., Erbe, Artur, Weig, Eva M.
Hybrid quantum devices enable novel functionalities by combining the benefits of different subsystems. Particularly, point defects in nanomechanical resonators made of diamond or silicon carbide (SiC) have been proposed for precise magnetic field sen
Externí odkaz:
http://arxiv.org/abs/2405.02035
We consider the notion of resilience for cyber-physical systems, that is, the ability of the system to withstand adverse events while maintaining acceptable functionality. We use finite temporal logic to express the requirements on the acceptable fun
Externí odkaz:
http://arxiv.org/abs/2404.19223
Safety is a fundamental requirement of control systems. Control Barrier Functions (CBFs) are proposed to ensure the safety of the control system by constructing safety filters or synthesizing control inputs. However, the safety guarantee and performa
Externí odkaz:
http://arxiv.org/abs/2403.19332
Autor:
Tayal, Manan, Goswami, Bhavya Giri, Rajgopal, Karthik, Singh, Rajpal, Rao, Tejas, Keshavan, Jishnu, Jagtap, Pushpak, Kolathaya, Shishir
This work presents a unified approach for collision avoidance using Collision-Cone Control Barrier Functions (CBFs) in both ground (UGV) and aerial (UAV) unmanned vehicles. We propose a novel CBF formulation inspired by collision cones, to ensure saf
Externí odkaz:
http://arxiv.org/abs/2403.07043
Autor:
Brahmachary, Shuvayan, Joshi, Subodh M., Panda, Aniruddha, Koneripalli, Kaushik, Sagotra, Arun Kumar, Patel, Harshil, Sharma, Ankush, Jagtap, Ameya D., Kalyanaraman, Kaushic
Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios, i
Externí odkaz:
http://arxiv.org/abs/2403.02054
Autor:
Nilaksh, Nilaksh, Ranjan, Abhishek, Agrawal, Shreenabh, Jain, Aayush, Jagtap, Pushpak, Kolathaya, Shishir
Reinforcement Learning (RL) has progressed from simple control tasks to complex real-world challenges with large state spaces. While RL excels in these tasks, training time remains a limitation. Reward shaping is a popular solution, but existing meth
Externí odkaz:
http://arxiv.org/abs/2403.01410
Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numerical analysis. Herein, we employ neural operators to solve Riemann prob
Externí odkaz:
http://arxiv.org/abs/2401.08886
In this paper, we present a compositional methodology for constructing symbolic models of nonlinear interconnected impulsive systems. Our approach relies on the concept of "alternating simulation function" to establish a relationship between concrete
Externí odkaz:
http://arxiv.org/abs/2312.17006
Autonomy advances have enabled robots in diverse environments and close human interaction, necessitating controllers with formal safety guarantees. This paper introduces an experimental platform designed for the validation and demonstration of a nove
Externí odkaz:
http://arxiv.org/abs/2310.10839