Confined and subterranean areas are common in many civilian and industrial sites,
although they are hazardous for humans given the presence of noxious gases, extreme …

Autonomous navigation and information gathering in challenging environments are
demanding since the robot's sensors may be susceptible to non-negligible noise, its …

This paper contributes a novel and modularized learning-based method for aerial robots
navigating cluttered environments containing hard-to-perceive thin obstacles without …

This work contributes a novel deep navigation policy that enables collision-free flight of
aerial robots based on a modular approach exploiting deep collision encoding and …

In this work, we propose a reaction-based local planner for autonomous collision avoidance
of quadrotor in obstacle-cluttered environment without relying on an explicit map. Our …

This paper contributes a novel learning-based method for aggressive task-driven
compression of depth images and their encoding as images tailored to collision prediction …

Visual navigation has received significant attention recently. Most of the prior works focus on
predicting navigation actions based on semantic features extracted from visual encoders …

As aerial robots are tasked to navigate environments of increased complexity, embedding
collision tolerance in their design becomes important. In this survey we review the current …

Many existing obstacle avoidance algorithms overlook the crucial balance between safety
and agility, especially in environments of varying complexity. In our study, we introduce an …

This paper introduces a novel, small form-factor, aerial vehicle research platform for agile
object detection, classification, tracking, and interaction tasks. General-purpose hardware …