Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Arul, Senthil Hariharan"'
Autor:
Weerakoon, Kasun, Elnoor, Mohamed, Seneviratne, Gershom, Rajagopal, Vignesh, Arul, Senthil Hariharan, Liang, Jing, Jaffar, Mohamed Khalid M, Manocha, Dinesh
We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs). Our method interprets human commands using a Large Language Model (LLM) and categorizes th
Externí odkaz:
http://arxiv.org/abs/2409.16484
Autor:
Arul, Senthil Hariharan, Kumar, Dhruva, Sugirtharaj, Vivek, Kim, Richard, Xuewei, Qi, Madhivanan, Rajasimman, Sen, Arnie, Manocha, Dinesh
We present VLPG-Nav, a visual language navigation method for guiding robots to specified objects within household scenes. Unlike existing methods primarily focused on navigating the robot toward objects, our approach considers the additional challeng
Externí odkaz:
http://arxiv.org/abs/2408.08301
We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing ov
Externí odkaz:
http://arxiv.org/abs/2303.10133
In decentralized multi-robot navigation, ensuring safe and efficient movement with limited environmental awareness remains a challenge. While robots traditionally navigate based on local observations, this approach falters in complex environments. A
Externí odkaz:
http://arxiv.org/abs/2209.06415
We present a decentralized path-planning algorithm for navigating multiple differential-drive robots in dense environments. In contrast to prior decentralized methods, we propose a novel congestion metric-based replanning that couples local and globa
Externí odkaz:
http://arxiv.org/abs/2202.11334
We present a decentralized collision avoidance method for dense environments that is based on buffered Voronoi cells (BVC) and reciprocal velocity obstacles (RVO). Our approach is designed for scenarios with large number of close proximity agents and
Externí odkaz:
http://arxiv.org/abs/2102.13281
We present decentralized collision avoidance algorithms for quadrotor swarms operating under uncertain state estimation. Our approach exploits the differential flatness property and feedforward linearization to approximate the quadrotor dynamics and
Externí odkaz:
http://arxiv.org/abs/2009.07894
In this paper, we present an online motion planning algorithm (3D-OGSE) for generating smooth, collision-free trajectories over multiple planning iterations for 3-D agents operating in an unknown obstacle-cluttered 3-D environment. Our approach const
Externí odkaz:
http://arxiv.org/abs/2005.13229
We present a novel, decentralized collision avoidance algorithm for navigating a swarm of quadrotors in dense environments populated with static and dynamic obstacles. Our algorithm relies on the concept of Optimal Reciprocal CollisionAvoidance (ORCA
Externí odkaz:
http://arxiv.org/abs/1909.03961
Autor:
Arul, Senthil Hariharan, Sathyamoorthy, Adarsh Jagan, Patel, Shivang, Otte, Michael, Xu, Huan, Lin, Ming C, Manocha, Dinesh
In this paper, we address the problem of collision avoidance for a swarm of UAVs used for continuous surveillance of an urban environment. Our method, LSwarm, efficiently avoids collisions with static obstacles, dynamic obstacles and other agents in
Externí odkaz:
http://arxiv.org/abs/1902.08379