Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Feroskhan Mir"'
Autor:
Kamath Archit Krishna, Feroskhan Mir
Publikováno v:
Nonlinear Engineering, Vol 13, Iss 1, Pp 5348-58 (2024)
Traditional position and image-based visual servoing techniques often pose challenges in terms of target loss and actuator saturation. These challenges arise due to the requirement of calculating inverse Jacobians to determine robot motions and the s
Externí odkaz:
https://doaj.org/article/c78b55dfcee74a208d81b5de9e73598f
This paper addresses the pursuit control problem for multi-agent systems, aiming to ensure collision-free tracking under input saturation and external disturbances. We propose a novel Control Barrier Function (CBF)-Safe Reinforcement Learning (CSRL)
Externí odkaz:
http://arxiv.org/abs/2411.17552
The rapid advancement of drone technology has significantly impacted various sectors, including search and rescue, environmental surveillance, and industrial inspection. Multidrone systems offer notable advantages such as enhanced efficiency, scalabi
Externí odkaz:
http://arxiv.org/abs/2409.01953
In this paper, we propose a novel adaptive Control Barrier Function (CBF) based controller for nonlinear systems with complex, time-varying input constraints. Conventional CBF approaches often struggle with feasibility issues and stringent assumption
Externí odkaz:
http://arxiv.org/abs/2408.09534
UAV tracking and pose estimation plays an imperative role in various UAV-related missions, such as formation control and anti-UAV measures. Accurately detecting and tracking UAVs in a 3D space remains a particularly challenging problem, as it require
Externí odkaz:
http://arxiv.org/abs/2405.16867
Autor:
Dam, Tanmoy, Dharavath, Sanjay Bhargav, Alam, Sameer, Lilith, Nimrod, Chakraborty, Supriyo, Feroskhan, Mir
Publikováno v:
2024 IEEE International Conference on Robotics and Automation (ICRA)
Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems. Yet, the fusion encounters difficulties with extended distance detection due to the contrast between LiDAR's sparse data a
Externí odkaz:
http://arxiv.org/abs/2402.07680
Drones as advanced cyber-physical systems are undergoing a transformative shift with the advent of vision-based learning, a field that is rapidly gaining prominence due to its profound impact on drone autonomy and functionality. Different from existi
Externí odkaz:
http://arxiv.org/abs/2312.05019
Autor:
Xiao, Jiaping, Feroskhan, Mir
Publikováno v:
IEEE Transactions on Artificial Intelligence (2024)
Safe navigation of drones in the presence of adversarial physical attacks from multiple pursuers is a challenging task. This paper proposes a novel approach, asynchronous multi-stage deep reinforcement learning (AMS-DRL), to train adversarial neural
Externí odkaz:
http://arxiv.org/abs/2304.03443
Equipping drones with target search capabilities is desirable for applications in disaster management scenarios and smart warehouse delivery systems. Instead of deploying a single drone, an intelligent drone swarm that can collaborate with one anothe
Externí odkaz:
http://arxiv.org/abs/2204.12181
Publikováno v:
In Robotics and Autonomous Systems December 2024 182