Department of Computer Engineering, "Gheorghe Asachi" Technical University of Iasi, 700050 Iasi, Romania.
Department of Automatic Control and Applied Informatics, "Gheorghe Asachi" Technical University of Iasi, 700050 Iasi, Romania.
Sensors (Basel). 2023 May 23;23(11):4992. doi: 10.3390/s23114992.
Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.
目标检测是自主移动机器人系统的重要组成部分，使机器人能够理解和与环境交互。使用卷积神经网络 (CNN)，目标检测和识别取得了重大进展。CNN 广泛应用于自主移动机器人应用中，可以快速识别复杂的图像模式，例如物流环境中的物体。环境感知算法和运动控制算法的集成是一个受到广泛研究的主题。一方面，本文提出了一种目标检测器，以更好地理解机器人环境和新获取的数据集。该模型经过优化，可在机器人上的移动平台上运行。另一方面，本文介绍了一种基于模型的预测控制器，该控制器可根据从自定义训练的 CNN 检测器和 LIDAR 数据获得的对象地图，引导全向机器人在物流环境中到达特定位置。目标检测有助于全向移动机器人实现安全、优化和高效的路径。在实际场景中，我们在仓库环境中部署了经过自定义训练和优化的 CNN 模型来检测特定对象。然后，我们通过模拟评估基于 CNN 检测到的对象的预测控制方法。在移动平台上使用内部获取的数据集对自定义训练的 CNN 进行目标检测，并对全向移动机器人进行优化控制，可获得结果。
