Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2209.06336v1
• Date: Tue, 13 Sep 2022 22:51:34 GMT
• Title: Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning
• Authors: Matheus G. Mateus, Ricardo B. Grando, Paulo L. J. Drews-Jr
• Abstract summary: This work aims to contribute to the active perception of UAVs by tackling the problem of tracking and recognizing water surface structures. We show that our system with classical image processing techniques and a simple Deep Reinforcement Learning (Deep-RL) agent is capable of perceiving the environment and dealing with uncertainties.
• Score: 0.5161531917413708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range of applications in which they can be used autonomously. However, they need intelligent systems capable of providing a greater understanding of what they perceive to perform several tasks. They become more challenging in complex environments since there is a need to perceive the environment and act under environmental uncertainties to make a decision. In this context, a system that uses active perception can improve performance by seeking the best next view through the recognition of targets while displacement occurs. This work aims to contribute to the active perception of UAVs by tackling the problem of tracking and recognizing water surface structures to perform a dynamic landing. We show that our system with classical image processing techniques and a simple Deep Reinforcement Learning (Deep-RL) agent is capable of perceiving the environment and dealing with uncertainties without making the use of complex Convolutional Neural Networks (CNN) or Contrastive Learning (CL).

Related papers

• Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
  We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning. Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques. Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
  arXiv  Detail & Related papers  (2024-05-30T23:20:23Z)
• Floor extraction and door detection for visually impaired guidance [78.94595951597344]
  Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people.
  arXiv  Detail & Related papers  (2024-01-30T14:38:43Z)
• Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing [52.50284630866713]
  Existing systems often require hand-engineered components for state estimation, planning, and control. This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies.
  arXiv  Detail & Related papers  (2022-10-26T19:03:17Z)
• Virtual Reality via Object Poses and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities [39.29763956979895]
  This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments. The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot's workspace. We show over 70 robust executions of pick-and-place, force application and peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM)
  arXiv  Detail & Related papers  (2022-10-18T08:42:30Z)
• Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration [83.96729205383501]
  We introduce prompt-based learning to achieve fast adaptation for language embeddings. Our model can adapt to diverse vision-language navigation tasks, including VLN and REVERIE.
  arXiv  Detail & Related papers  (2022-03-08T11:01:24Z)
• Interpretable UAV Collision Avoidance using Deep Reinforcement Learning [1.2693545159861856]
  We present autonomous UAV flight using Deep Reinforcement Learning augmented with Self-Attention Models. We have tested our algorithm under different weather and environments and found it to be robust compared to conventional Deep Reinforcement Learning algorithms.
  arXiv  Detail & Related papers  (2021-05-25T23:21:54Z)
• Robust Deep Reinforcement Learning via Multi-View Information Bottleneck [7.188571996124112]
  We introduce an auxiliary objective based on the multi-view information bottleneck (MIB) principle. This encourages learning representations that are both predictive of the future and less sensitive to task-irrelevant distractions. We demonstrate that our approach can achieve SOTA performance on challenging visual control tasks, even when the background is replaced with natural videos.
  arXiv  Detail & Related papers  (2021-02-26T02:24:36Z)
• Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments [20.017277077448924]
  NavACL is a method of automatic curriculum learning tailored to the navigation task. Deep reinforcement learning agents trained using NavACL significantly outperform state-of-the-art agents trained with uniform sampling. Our agents can navigate through unknown cluttered indoor environments to semantically-specified targets using only RGB images.
  arXiv  Detail & Related papers  (2020-09-11T13:28:26Z)
• Active Visual Information Gathering for Vision-Language Navigation [115.40768457718325]
  Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments. One of the key challenges in VLN is how to conduct a robust navigation by mitigating the uncertainty caused by ambiguous instructions and insufficient observation of the environment. This work draws inspiration from human navigation behavior and endows an agent with an active information gathering ability for a more intelligent VLN policy.
  arXiv  Detail & Related papers  (2020-07-15T23:54:20Z)
• Robot Perception enables Complex Navigation Behavior via Self-Supervised Learning [23.54696982881734]
  We propose an approach to unify successful robot perception systems for active target-driven navigation tasks via reinforcement learning (RL) Our method temporally incorporates compact motion and visual perception data, directly obtained using self-supervision from a single image sequence. We demonstrate our approach on two real-world driving dataset, KITTI and Oxford RobotCar, using the new interactive CityLearn framework.
  arXiv  Detail & Related papers  (2020-06-16T07:45:47Z)
• Weakly-Supervised Reinforcement Learning for Controllable Behavior [126.04932929741538]
  Reinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks. In many settings, an agent must winnow down the inconceivably large space of all possible tasks to the single task that it is currently being asked to solve. We introduce a framework for using weak supervision to automatically disentangle this semantically meaningful subspace of tasks from the enormous space of nonsensical "chaff" tasks.
  arXiv  Detail & Related papers  (2020-04-06T17:50:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.