Safe flight in dynamic environments requires autonomous unmanned aerial vehicles (UAVs) to make effective decisions when navigating cluttered spaces with moving obstacles. Traditional approaches often decompose decision-making into hierarchical modules for prediction and planning. Although these handcrafted systems can perform well in specific settings, they might fail if environmental conditions change and often require careful parameter tuning. Additionally, their solutions could be suboptimal due to the use of inaccurate mathematical model assumptions and simplifications aimed at achieving computational efficiency. To overcome these limitations, this paper introduces the NavRL framework, a deep reinforcement learning-based navigation method built on the Proximal Policy Optimization (PPO) algorithm. NavRL utilizes our carefully designed state and action representations, allowing the learned policy to make safe decisions in the presence of both static and dynamic obstacles, with zero-shot transfer from simulation to real-world flight. Furthermore, the proposed method adopts a simple but effective safety shield for the trained policy, inspired by the concept of velocity obstacles, to mitigate potential failures associated with the black-box nature of neural networks. To accelerate the convergence, we implement the training pipeline using NVIDIA Isaac Sim, enabling parallel training with thousands of quadcopters. Simulation and physical experiments show that our method ensures safe navigation in dynamic environments and results in the fewest collisions compared to benchmarks in scenarios with dynamic obstacles. 

pdf/navrl-learning-safe-flight-in-dynamic-environments-3ibvy8qzlco3.pdf

NavRL: Learning Safe Flight in Dynamic Environments

Abstract To reduce operator workload and enhance safety, this study proposes a deep reinforcement learning (DRL)-based teleoperation method. The operator only provides a high-level signal specifying the desired end-effector position, while an end-to-end DRL model plans collision-free paths and tracks the target trajectory. To improve the safety and interpretability of motion planning, a collision risk model is designed to evaluate the risk during manipulator movement. An agent trained with the collision risk model achieves higher average rewards and lower collision rates, validating the effectiveness of the proposed method. 

Teleoperation obstacle avoidance motion planning based on a collision risk model and reinforcement learning

Towards trajectory following and vision-based collision avoidance for Micro Aerial Vehicles with Deep Reinforcement Learning

Reactive collision avoidance is essential for agile robots navigating complex and dynamic environments, enabling real-time obstacle response. However, this task is inherently challenging because it requires a tight integration of perception, planning, and control, which traditional methods often handle separately, resulting in compounded errors and delays. This paper introduces a novel approach that unifies these tasks into a single reactive framework using solely onboard sensing and computing. Our method combines nonlinear model predictive control with adaptive control barrier functions, directly linking perception-driven constraints to real-time planning and control. Constraints are determined by using a neural network to refine noisy RGB-D data, enhancing depth accuracy, and selecting points with the minimum time-to-collision to prioritize the most immediate threats. To maintain a balance between safety and agility, a heuristic dynamically adjusts the optimization process, preventing overconstraints in real time. Extensive experiments with an agile quadrotor demonstrate effective collision avoidance across diverse indoor and outdoor environments, without requiring environment-specific tuning or explicit mapping.

pdf/reactive-collision-avoidance-for-safe-agile-navigation-2537fdwv60lt.pdf

Reactive Collision Avoidance for Safe Agile Navigation

Offline training of Deep Reinforcement Learning agents is a valuable solution for addressing autonomous control and robotics challenges, especially when acquiring real-world data is particularly difficult and simulators are not available. Despite its validity, this approach is burdened by the issue of sample inefficiency, particularly with high-dimensional data, such as images, making it less effective for vision-based tasks. Counterfactual data augmentation addresses this issue by expanding the training dataset with plausible samples consistent with stochastic environments. However, its application in vision-based control remains underexplored. This study introduces a novel counterfactual data augmentation technique for vision-based tasks, leveraging Deep Generative Models to estimate the Structural Causal Model (SCM) and the associated reward model. The learned SCM is then used to augment the training dataset and optimize the DRL agents. We show the effectiveness of our method compared to existing state-of-the-art approaches on a series of stochastic control problems, both with discrete and continuous action spaces.

Enhancing Counterfactual Data Augmentation for Offline Reinforcement Learning in Vision-Based Control

Visual Odometry (VO) and Visual SLAM (VSLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75 % with respect to a traditional fixed lighting technique.

pdf/active-illumination-for-visual-ego-motion-estimation-in-the-5vnku0im04ne.pdf

Active Illumination for Visual Ego-Motion Estimation in the Dark

Enabling Micro Aerial Vehicles (MAVs) with semi-autonomous capabilities to assist their teleoperation is crucial in several applications. Remote human operators do not have, in general, the situational awareness to perceive obstacles near the drone, nor the readiness to provide commands to avoid collisions. In this work, we devise a novel teleoperation setting that asks the operator to provide a simple high-level signal encoding the speed and the direction they expect the drone to follow. We then endow the MAV with an end-to-end Deep Reinforcement Learning (DRL) model that computes control commands to track the desired trajectory while performing collision avoidance. Differently from State-of-the-Art (SotA) works, it allows the robot to move freely in the 3D space, requires only the current RGB image captured by a monocular camera and the current robot position, and does not make any assumption about obstacle shape and size. We show the effectiveness and the generalization capabilities of our strategy by comparing it against a SotA baseline in photorealistic simulated environments.

Monocular Reactive Collision Avoidance for MAV Teleoperation with Deep Reinforcement Learning

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Raffaele Brilli

Author Tools

Chat about Author

Papers

Enhancing Counterfactual Data Augmentation for Offline Reinforcement Learning in Vision-Based Control

Active Illumination for Visual Ego-Motion Estimation in the Dark

Monocular Reactive Collision Avoidance for MAV Teleoperation with Deep Reinforcement Learning