Small Obstacle Avoidance Based on RGB-D Semantic Segmentation

Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning

Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D environments where the robot has to solely rely on a single monocular camera. In particular, we are interested in solving this problem without relying on localization, mapping, or planning techniques. Most of the existing work consider obstacle avoidance as two separate problems, namely obstacle detection, and control. Inspired by the recent advantages of deep reinforcement learning in Atari games and understanding hig…

EVReflex: Dense Time-to-Impact Prediction for Event-based Obstacle Avoidance

The broad scope of obstacle avoidance has led to many kinds of computer vision-based approaches. Despite its popularity, it is not a solved problem. Traditional computer vision techniques using cameras and depth sensors often focus on static scenes, or rely on priors about the obstacles. Recent developments in bio-inspired sensors present event cameras as a compelling choice for dynamic scenes. Although these sensors have many advantages over their frame-based counterparts, such as high dynamic range and temporal resolution, event-based perception has largely remained in 2D. This often leads t…

Learning Vision-based Reactive Policies for Obstacle Avoidance

In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most work in the field aims at improving one of those aspects, we provide a unified framework for approaching this problem. The main goal of this framework is to connect perception and motion by identifying the relationship between the visual input and the corresponding motion representation. To this end, we propose a method for learning reactive obstacle avoidance policies. We evaluate our method on goal-reaching tasks …

Simulation of optical flow and fuzzy based obstacle avoidance system for mobile robots

Honey bees use optical flow to avoid obstacles effectively. In this research work similar methodology was tested on a simulated mobile robot. Simulation framework was based on VRML and Simulink in a 3D world. Optical flow vectors were calculated from a video scene captured by a virtual camera which was used as inputs to a fuzzy logic controller. Fuzzy logic controller decided the locomotion of the robot. Different fuzzy logic rules were evaluated. The robot was able to navigate through complex static and dynamic environments effectively, avoiding obstacles on its path.

基于逆PPO的固定翼无人机单目避障

Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs

针对传统固定翼无人机避障系统在未知环境和小型平台的局限性，本文提出基于逆PPO的轻量级DRL避障系统，通过单帧图像深度推理模块和自适应熵调整机制，实现仅用视觉传感器以超30m/s速度避障，性能优于其他方法且可在边缘设备部署。

Obstacle Avoidance through Deep Networks based Intermediate Perception

Obstacle avoidance from monocular images is a challenging problem for robots. Though multi-view structure-from-motion could build 3D maps, it is not robust in textureless environments. Some learning based methods exploit human demonstration to predict a steering command directly from a single image. However, this method is usually biased towards certain tasks or demonstration scenarios and also biased by human understanding. In this paper, we propose a new method to predict a trajectory from images. We train our system on more diverse NYUv2 dataset. The ground truth trajectory is computed from…

DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems

Depth estimation-based obstacle avoidance has been widely adopted by autonomous systems (drones and vehicles) for safety purpose. It normally relies on a stereo camera to automatically detect obstacles and make flying/driving decisions, e.g., stopping several meters ahead of the obstacle in the path or moving away from the detected obstacle. In this paper, we explore new security risks associated with the stereo vision-based depth estimation algorithms used for obstacle avoidance. By exploiting the weaknesses of the stereo matching in depth estimation algorithms and the lens flare effect in op…

A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance

Integration of reinforcement learning with unmanned aerial vehicles (UAVs) to achieve autonomous flight has been an active research area in recent years. An important part focuses on obstacle detection and avoidance for UAVs navigating through an environment. Exploration in an unseen environment can be tackled with Deep Q-Network (DQN). However, value exploration with uniform sampling of actions may lead to redundant states, where often the environments inherently bear sparse rewards. To resolve this, we present two techniques for improving exploration for UAV obstacle avoidance. The first is …

基于操纵正交坐标的动态系统障碍避障研究

Study of dynamical system based obstacle avoidance via manipulating orthogonal coordinates

本文研究了基于动态系统的障碍避障问题，通过引入正交坐标开发了调制矩阵，使调制矩阵更加合理。新轨迹的方向可通过正交坐标的线性组合表示。提出了一种通过引入旋转矩阵来解决局部最小问题，并在三维或更高维空间中提供更合理运动的正交坐标操纵方法。该方法还为围绕凸形体巡逻提供了解决方案。实验结果表明所提出方法的有效性。