面向复杂环境的移动机器人自主导航与避障技术研究
摘要
移动机器人自主导航与避障技术在复杂环境中具有重要应用价值，其研究背景源于工业自动化、智能服务及特种作业等领域对机器人智能化水平的迫切需求。为解决传统导航方法在动态、非结构化场景中适应性不足的问题，本研究提出了一种融合多源传感器数据的自主导航与避障框架，旨在提升机器人在复杂环境中的感知能力和决策效率。研究通过引入深度学习算法优化环境建模与目标检测，并结合强化学习设计路径规划策略，实现了从静态地图依赖向动态场景自适应的转变。实验采用激光雷达、摄像头和超声波传感器构建多模态感知系统，验证了所提方法在障碍物识别精度和路径规划效率方面的优越性。结果表明，该方法能够有效应对动态障碍物和未知地形挑战，显著提高了导航成功率和实时性。本研究的主要创新点在于提出了基于深度强化学习的动态路径规划算法以及多传感器信息融合的环境感知机制，为复杂环境下移动机器人的自主能力提升提供了新思路，其成果可广泛应用于物流配送、灾害救援及智能制造等领域。
关键词：移动机器人自主导航；多传感器信息融合；深度强化学习
Abstract
The autonomous navigation and obstacle avoidance technology of mobile robots holds significant application value in complex environments, emerging from the urgent need for enhanced robot intelligence in fields such as industrial automation, intelligent services, and special operations. To address the limitations of traditional navigation methods in dynamic and unstructured scenarios, this study proposes a fr amework that integrates multisensor data for autonomous navigation and obstacle avoidance, aiming to improve the robot's perception capabilities and decision-making efficiency in complex environments. By incorporating deep learning algorithms to optimize environmental modeling and ob ject detection, and combining reinforcement learning to design path planning strategies, the proposed method achieves a transition from static map dependency to dynamic scene adaptability. Experiments were conducted using a multimodal sensing system comprising LiDAR, cameras, and ultrasonic sensors, which validated the superiority of the proposed method in terms of obstacle recognition accuracy and path planning efficiency. The results demonstrate that this approach can effectively handle dynamic obstacles and unknown terrains, significantly improving navigation success rates and real-time performance. The primary innovations of this research lie in the development of a dynamic path planning algorithm based on deep reinforcement learning and a multisensor information fusion mechanism for environmental perception, providing new insights into enhancing the autonomy of mobile robots in complex environments. These findings have broad applications in areas such as logistics delivery, disaster relief, and intelligent manufacturing.
Keywords：Mobile Robot Autonomous Navigation; Multi-Sensor Information Fusion; Deep Reinforcement Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法概述 2
二、复杂环境感知技术 2
(一) 环境感知传感器选择 2
(二) 传感器数据融合方法 3
(三) 动态环境建模分析 3
三、自主导航算法设计 4
(一) 路径规划算法研究 4
(二) 全局定位技术实现 5
(三) 局部导航策略优化 5
四、避障技术及应用 6
(一) 障碍物检测机制 6
(二) 实时避障决策模型 7
(三) 避障性能评估体系 7
结 论 9
参考文献 10
摘要
移动机器人自主导航与避障技术在复杂环境中具有重要应用价值，其研究背景源于工业自动化、智能服务及特种作业等领域对机器人智能化水平的迫切需求。为解决传统导航方法在动态、非结构化场景中适应性不足的问题，本研究提出了一种融合多源传感器数据的自主导航与避障框架，旨在提升机器人在复杂环境中的感知能力和决策效率。研究通过引入深度学习算法优化环境建模与目标检测，并结合强化学习设计路径规划策略，实现了从静态地图依赖向动态场景自适应的转变。实验采用激光雷达、摄像头和超声波传感器构建多模态感知系统，验证了所提方法在障碍物识别精度和路径规划效率方面的优越性。结果表明，该方法能够有效应对动态障碍物和未知地形挑战，显著提高了导航成功率和实时性。本研究的主要创新点在于提出了基于深度强化学习的动态路径规划算法以及多传感器信息融合的环境感知机制，为复杂环境下移动机器人的自主能力提升提供了新思路，其成果可广泛应用于物流配送、灾害救援及智能制造等领域。
关键词：移动机器人自主导航；多传感器信息融合；深度强化学习
Abstract
The autonomous navigation and obstacle avoidance technology of mobile robots holds significant application value in complex environments, emerging from the urgent need for enhanced robot intelligence in fields such as industrial automation, intelligent services, and special operations. To address the limitations of traditional navigation methods in dynamic and unstructured scenarios, this study proposes a fr amework that integrates multisensor data for autonomous navigation and obstacle avoidance, aiming to improve the robot's perception capabilities and decision-making efficiency in complex environments. By incorporating deep learning algorithms to optimize environmental modeling and ob ject detection, and combining reinforcement learning to design path planning strategies, the proposed method achieves a transition from static map dependency to dynamic scene adaptability. Experiments were conducted using a multimodal sensing system comprising LiDAR, cameras, and ultrasonic sensors, which validated the superiority of the proposed method in terms of obstacle recognition accuracy and path planning efficiency. The results demonstrate that this approach can effectively handle dynamic obstacles and unknown terrains, significantly improving navigation success rates and real-time performance. The primary innovations of this research lie in the development of a dynamic path planning algorithm based on deep reinforcement learning and a multisensor information fusion mechanism for environmental perception, providing new insights into enhancing the autonomy of mobile robots in complex environments. These findings have broad applications in areas such as logistics delivery, disaster relief, and intelligent manufacturing.
Keywords：Mobile Robot Autonomous Navigation; Multi-Sensor Information Fusion; Deep Reinforcement Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法概述 2
二、复杂环境感知技术 2
(一) 环境感知传感器选择 2
(二) 传感器数据融合方法 3
(三) 动态环境建模分析 3
三、自主导航算法设计 4
(一) 路径规划算法研究 4
(二) 全局定位技术实现 5
(三) 局部导航策略优化 5
四、避障技术及应用 6
(一) 障碍物检测机制 6
(二) 实时避障决策模型 7
(三) 避障性能评估体系 7
结 论 9
参考文献 10
