The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multi-object tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

翻译：自主移动机器人在制造、物流和农业等复杂环境中的安全高效运行，需要精确的多目标跟踪和预测性碰撞避免。本文介绍了利用激光雷达传感器数据解决这些挑战的算法与技术，重点阐述了集成卡尔曼滤波器。所开发的预测性碰撞避免算法利用激光雷达传感器提供的数据跟踪多个目标，并预测其速度和未来位置，使自主移动机器人能够安全有效地导航。引入了一种动态窗口方法的改进方案，以提升碰撞避免系统的性能。整体系统架构包括目标检测、多目标跟踪和预测性碰撞避免控制。仿真与真实世界数据的实验结果表明，所提方法在各种场景下均有效，这为未来关于全局规划器、其他控制器以及集成更多传感器的研究奠定了基础。本文为复杂动态环境下安全高效自主系统的持续发展做出了贡献。