1 citations · 3 across the 5 of their papers we have counts for
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Confidence-Gated Vision-Only Heading Alignment for UAV-UGV Cooperative Systems
Reza Ahmari, Vahid Hemmati, Parham Kebria +3
Vision-based heading prediction is useful for UAV--UGV cooperation, but accurate prediction alone does not guarantee that every predicted heading should be issued directly as a con…
Real-Time Neuromorphic Navigation: Guiding Physical Robots with Event-Based Sensing and Task-Specific Reconfigurable Autonomy Stack
Sourav Sanyal, Amogh Joshi, Adarsh Kosta +1
Neuromorphic vision, inspired by biological neural systems, has recently gained significant attention for its potential in enhancing robotic autonomy. This paper presents a systema…
Energy-Efficient Autonomous Aerial Navigation with Dynamic Vision Sensors: A Physics-Guided Neuromorphic Approach
Sourav Sanyal, Amogh Joshi, Manish Nagaraj +2
Vision-based object tracking is a critical component for achieving autonomous aerial navigation, particularly for obstacle avoidance. Neuromorphic Dynamic Vision Sensors (DVS) or e…
Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge
Amogh Joshi, Sourav Sanyal, Kaushik Roy
The integration of human-intuitive interactions into autonomous systems has been limited. Traditional Natural Language Processing (NLP) systems struggle with context and intent und…
ASMA: An Adaptive Safety Margin Algorithm for Vision-Language Drone Navigation via Scene-Aware Control Barrier Functions
Sourav Sanyal, Kaushik Roy
In the rapidly evolving field of vision-language navigation (VLN), ensuring safety for physical agents remains an open challenge. For a human-in-the-loop language-operated drone to…
Real-Time Neuromorphic Navigation: Integrating Event-Based Vision and Physics-Driven Planning on a Parrot Bebop2 Quadrotor
Amogh Joshi, Sourav Sanyal, Kaushik Roy
In autonomous aerial navigation, real-time and energy-efficient obstacle avoidance remains a significant challenge, especially in dynamic and complex indoor environments. This work…