随着柔性制造系统的广泛应用和物流自动化运输系统的快速发展，AGV技术得到了快速发展。从一开始对单台AGV的研究，发展到了对多AGV组成的物流系统的研究。而多AGV路径规划作为直接影响多AGV系统整体性能的重要部分，一直倍受广大学者的关注。随着研究的深入，国内外学者提出了很多计算模型和策略。韩国的Jung Hoon Lee等人将两阶段的交通控制策略应用于多AGV的无碰规划，刘国栋等提出了多AGV调度系统中的两阶段动态路径规划的方法。两阶段控制策略离线生成路径库，减少了在线运算的负担，但是随着节点数的增多，动态规划的负担加重，不适用于大规模多AGV系统。其他如Petri网，遗传算法，Tabu Search算法（禁忌搜索算法）等策略和算法，在系统节点数增多的情况下，也有同样的缺陷。为了有效地共享系统路径，时间窗(Time-window)方法被提出并用于解决多AGV最优路径问题。然而使用时间窗实现多AGV路径规划也是一个NP完全问题，并且在使用时间窗的模型中，获得时间窗的AGV占用路径时间过长，容易导致关键路段发生拥堵，降低系统效率。
With the wide application of flexible manufacturing systems and the rapid development of logistics automated transportation systems, AGV technology has developed rapidly. From the beginning of the research on a single AGV, it has developed to the research on the logistics system composed of multiple AGVs. The multi-AGV path planning, as an important part that directly affects the overall performance of the multi-AGV system, has always attracted the attention of many scholars. With the deepening of research, scholars at home and abroad have proposed many computational models and strategies. South Korea's Jung Hoon Lee et al. applied the two-stage traffic control strategy to multi-AGV collision-free planning. Liu Guodong et al. proposed a two-stage dynamic path planning method in a multi-AGV dispatching system. The two-stage control strategy generates the path library offline, which reduces the burden of online computing, but with the increase of the number of nodes, the burden of dynamic programming increases, which is not suitable for large-scale multi-AGV systems. Other strategies and algorithms such as Petri Net, Genetic Algorithm, Tabu Search Algorithm (Tabu Search Algorithm), etc., also have the same defects when the number of system nodes increases. In order to effectively share the system path, a time-window method is proposed and used to solve the optimal path problem of multiple AGVs. However, the use of time windows to achieve multi-AGV path planning is also an NP-complete problem, and in the model using time windows, the AGVs that obtain the time windows occupy the path for too long, which may easily lead to congestion in key road sections and reduce system efficiency.
1) 静态环境中确定AGV 路径规划
AGV 路径规划在智能控制系统中具有重要作用， 对于保证工作的安全性来说具有重要意义。一直以来，很多学者都对此进行孜孜不倦的探索，这也是机器人学中最新最热的内容之一。主要研究的是在障碍物的环境下，机器人如何寻找到目标，也就是选择合适的路径规划。智能控制下的AGV 路径规划较为重要的两种形态，静态环境中的路径规划以及动态环境中的路径规划。
静态环境下的路径规划是假定在环境信息未被完全掌握的情况下，机器人是通过怎么样的路径感知环境，并且运用局部区域传播算法。因此这种路径一般会在环境中仅存在静态已知障碍物的情况下被采用。但是要分析静态环境中AGV 路径规划，需要解决的一个问题是在这种环境中什么样的路径才能够被认为是合理的。总而言之，能够使AGV 系统实现控制的就是合理路径。合理的路径由路径的平滑程度决定，路径越趋于平缓，则AGV 系统将会更容易实现。此时可以将路径分为四个种类，第一类平滑程度非常低，表现为路径的不连续性，此时很多存在位置会表现突变的特性， 这种情况下AGV 系统不容易被控制，因为这些曲线不连续，无法对其追踪。第二类，这类曲线相对于第一种来说具有连续性，但是在切线方向有时也会发生突变现象。此时也不能够被AGV 系统控制。第三类，这类曲线不仅具有连续性的特点， 而且还能在切线方向保持连续性，因此是较为合理的路径规划，一般情况下也常常被采用。第四类，将以上三类曲线的优点都集于一身，但是要生产这类曲线十分复杂，因此在实践当中，这类曲线很难被采用。
1) Determine AGV path planning in static environment
AGV path planning plays an important role in the intelligent control system and is of great significance for ensuring the safety of work. For a long time, many scholars have been tirelessly exploring this, which is also one of the latest and hottest contents in robotics. The main research is how the robot finds the target in the environment of obstacles, that is, chooses the appropriate path planning. There are two important forms of AGV path planning under intelligent control, path planning in a static environment and path planning in a dynamic environment.
The path planning in the static environment assumes that the robot perceives the environment through what path when the environmental information is not fully grasped, and uses the local area propagation algorithm. Therefore such paths are generally taken in situations where there are only static known obstacles in the environment. However, to analyze the AGV path planning in a static environment, a problem that needs to be solved is what kind of path can be considered reasonable in this environment. All in all, what can make the AGV system control is a reasonable path. A reasonable path is determined by the smoothness of the path. The smoother the path, the easier the AGV system will be. At this time, the path can be divided into four types. The first type has a very low degree of smoothness, which is characterized by the discontinuity of the path. At this time, many existing positions will show the characteristics of sudden changes. In this case, the AGV system is not easy to be controlled, because the These curves are discontinuous and cannot be traced. The second type, this type of curve is continuous relative to the first type, but sometimes abrupt changes occur in the tangential direction. At this time, it cannot be controlled by the AGV system either. The third type, this type of curve not only has the characteristics of continuity, but also maintains continuity in the tangential direction, so it is a more reasonable path planning, and is often used in general. The fourth type combines the advantages of the above three types of curves, but it is very complicated to produce such curves, so in practice, such curves are difficult to use.
2) 动态环境中确定的路径规划
在动态复杂环境的中的路径规划不同于静态环境中的路径规划。因为环境变化之后，很多信息无法被掌握，要保证最优性在这种情况下是无法被实现的。在进行路径规划时，应当在安全性以及时间性之间进行衡量。在较为复杂的环境下，不管决定适用何种性能指标，都必须要考虑目标吸引、动态安全性以及时间约束三个方面的内容。
2) Path planning determined in a dynamic environment
Path planning in dynamic complex environments is different from path planning in static environments. Because after the environment changes, a lot of information cannot be grasped, and it is impossible to ensure optimality in this case. When planning a path, a balance should be made between safety and timeliness. In a more complex environment, no matter which performance index is applied, three aspects of target attraction, dynamic security and time constraints must be considered.
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