Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Meghjani, Malika"'
We present a viewpoint-based non-linear Model Predictive Control (MPC) for evacuation guiding robots. Specifically, the proposed MPC algorithm enables evacuation guiding robots to track and guide cooperative human targets in emergency scenarios. Our
Externí odkaz:
http://arxiv.org/abs/2409.19466
Autor:
Tan, Yu Xiang, Meghjani, Malika
Publikováno v:
IEEE International Conference on Intelligent Transportation Systems, 2024
GPS-based vehicle localization and tracking suffers from unstable positional information commonly experienced in tunnel segments and in dense urban areas. Also, both Visual Odometry (VO) and Visual Inertial Odometry (VIO) are susceptible to adverse w
Externí odkaz:
http://arxiv.org/abs/2409.01038
The underwater world remains largely unexplored, with Autonomous Underwater Vehicles (AUVs) playing a crucial role in sub-sea explorations. However, continuous monitoring of underwater environments using AUVs can generate a significant amount of data
Externí odkaz:
http://arxiv.org/abs/2402.03636
Autor:
Tan, Yu Xiang, Prasetyo, Marcel Bartholomeus, Daffa, Mohammad Alif, Nitin, Deshpande Sunny, Meghjani, Malika
The increasing demand for autonomous vehicles has created a need for robust navigation systems that can also operate effectively in adverse weather conditions. Visual odometry is a technique used in these navigation systems, enabling the estimation o
Externí odkaz:
http://arxiv.org/abs/2309.05249
This paper presents a localization algorithm for autonomous urban vehicles under rain weather conditions. In adverse weather, human drivers anticipate the location of the ego-vehicle based on the control inputs they provide and surrounding road conte
Externí odkaz:
http://arxiv.org/abs/2306.09134
A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase. This is specifically worsened by an increased variance in action responses when the DRL
Externí odkaz:
http://arxiv.org/abs/2305.16708
Multi-agent pursuit-evasion tasks involving intelligent targets are notoriously challenging coordination problems. In this paper, we investigate new ways to learn such coordinated behaviors of unmanned aerial vehicles (UAVs) aimed at keeping track of
Externí odkaz:
http://arxiv.org/abs/2303.01799
Publikováno v:
in Proceedings of Global Oceans 2020: Singapore-US Gulf Coast (pp. 1-8). IEEE
Autonomous marine environmental monitoring problem traditionally encompasses an area coverage problem which can only be effectively carried out by a multi-robot system. In this paper, we focus on robotic swarms that are typically operated and control
Externí odkaz:
http://arxiv.org/abs/2012.11641
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, 2021
Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change
Externí odkaz:
http://arxiv.org/abs/1909.03679