相关论文: UIVNAV: Underwater Information-driven Vision-based…
Open-Vocabulary Object Navigation (OVON) requires an embodied agent to locate a language-specified target in unknown environments. Existing zero-shot methods often reason over dense frontier points under incomplete observations, causing…
Underwater scenes intrinsically involve degradation problems owing to heterogeneous ocean elements. Prevailing underwater image enhancement (UIE) methods stick to straightforward feature modeling to learn the mapping function, which leads…
Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this paper, we propose…
Coral reefs are fast-changing and complex ecosystems that are crucial to monitor and study. Biological hotspot detection can help coral reef managers prioritize limited resources for monitoring and intervention tasks. Here, we explore the…
The Internet of Underwater Things (IoUT) supports ocean sensing and offshore monitoring but requires coordinated mobility and energy-aware communication to sustain long-term operation. This letter proposes a multi-AUV framework that jointly…
Direct communication between humans and autonomous underwater vehicles (AUVs) is a relatively underexplored area in human-robot interaction (HRI) research, although many tasks (\eg surveillance, inspection, and search-and-rescue) require…
Humans can robustly follow a visual trajectory defined by a sequence of images (i.e. a video) regardless of substantial changes in the environment or the presence of obstacles. We aim at endowing similar visual navigation capabilities to…
Since the application of Deep Q-Learning to the continuous action domain in Atari-like games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been qualitatively enhanced. Nowadays, modern Deep-RL can be successfully…
We study the problem of learning a navigation policy for a robot to actively search for an object of interest in an indoor environment solely from its visual inputs. While scene-driven visual navigation has been widely studied, prior…
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…
Occlusion is a longstanding difficulty that challenges the UAV-based object detection. Many works address this problem by adapting the detection model. However, few of them exploit that the UAV could fundamentally improve detection…
In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques…
With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic. It composes a multi-disciplinary field of robotics that encompasses embedded systems, control theory, path…
Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we propose Wild Visual Navigation (WVN), an…
How can a robot navigate successfully in rich and diverse environments, indoors or outdoors, along office corridors or trails on the grassland, on the flat ground or the staircase? To this end, this work aims to address three challenges:…
Humans can routinely follow a trajectory defined by a list of images/landmarks. However, traditional robot navigation methods require accurate mapping of the environment, localization, and planning. Moreover, these methods are sensitive to…
Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection, thanks to their ability to access remote areas and…
Effective robot navigation in unseen environments is a challenging task that requires precise control actions at high frequencies. Recent advances have framed it as an image-goal-conditioned control problem, where the robot generates…
Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a…
Deep reinforcement learning (RL) has been successfully applied to a variety of game-like environments. However, the application of deep RL to visual navigation with realistic environments is a challenging task. We propose a novel learning…