基于视觉深度学习的机器人环境感知及自主避障

Mobile robotic perception and autonomous avoidance based on visual depth learning

动态避障是机器人实现自主移动、安全行走的关键,面对复杂多变的室内场景,机器人需要能够及时检测到障碍物并动态规划安全的行走路线。利用RGB-D深度相机和IMU单元建立机器人环境感知系统,为机器人提供三维视觉和姿态角度等多模态信息。首先构建基于YOLOv4改进的目标检测模型,通过YOLOv4-M目标检测算法对彩色图像中的障碍物进行识别;将彩色图与深度图对齐,获取障碍物的尺寸信息以及机器人与障碍物的空间距离;根据机器人的实时姿态角度和对周围障碍物的识别信,建立基于改进的人工势场法避障决策模型,解决总势场计算陷入局部极小解的问题,动态规划行走路径,并将决策结果发送到机器人底盘控制单元,从而实现机器人在陌生场景中的自主运动。通过仿真分析及实物实验表明该方法可以实现机器人的自主避障。该方法的研究为机器人仅依赖视觉和惯导传感器就可以实现障碍物识别和自主移动避障提供了依据和参考。

Dynamic obstacle avoidance is the key to the robot′s autonomous movement and safe walking,in the face of complex and changeable indoor scenes,the robot needs to be able to detect obstacles in time and dynamically plan a safe walking route.RGB-D depth camera and IMU unit was used to establish a robot environment perception system,multimodal information such as three-dimensional vision and attitude angle were provided to the robot.At first,build an improved target detection model based on YOLOv4,The YOLOv4-M target detection algorithm was proposed to identify and locate obstacles in color images,and the depth map was aligned with the color map in order to calculate the size information of the obstacle and the distance information between the robot and the obstacle.The model of obstacle avoidance was built on modified artificial potential field method with the obstacle information in the environment and the posture and angle information of the robot movement,to solve the problem that the calculation of the total potential field falls into a local minimum solution.The model was designed with dynamic programming of the walking path,and the decision result was send to the robot chassis control unit to realize the autonomous movement of the robot in unfamiliar scenes.Simulation analysis and physical experiments show that this method can realize autonomous obstacle avoidance of robots.The research of this method provides a basis and reference for the robot to realize obstacle recognition and autonomous movement avoidance by relying only on vision and inertial navigation sensors.