面对环境参数未知且随时可能发生变化的应急场景，采用群组机器人进行救援工作，可为后续救援人员进入降低人身风险、提高救援保障能力和救援效率。在进行救援的过程中，群组机器人之间需要协作与合作，而机器人之间的资源分配是实现群组机器人协作与合作机制的关键途径。博弈论是研究多个具有共同或者相关利益的理性个体决策行为的学科，能够保证理性个体按照自身需求进行决策。针对机器人之间的资源分配问题，本文提出应急场景中去中心化群组机器人基于博弈论的资源分配策略，主要工作如下：
（ 1）针对应急场景群组机器人系统中资源提供机器人和资源消费机器人的服务资源分配问题，提出了基于 Stackelberg 博弈的资源分配模型。在博弈中，首先，资源提供机器人根据自身需求对服务资源进行定价，而资源消费机器人根据资源提供机器人对服务资源的定价确定购买数量。其次，引入资源流行度与偏好内容流行度构建了资源消费机器人的收益函数；综合时间与能耗构建了资源提供机器人的收益函数，并证明了给定资
源提供机器人的定价后，资源消费机器人之间的非合作博弈存在纳什均衡。最后，利用分布式迭代法求解，计算出资源提供机器人和资源消费机器人的均衡收益。
（ 2）在实现稳定分配群组机器人服务资源后，针对存储信息时机器人的不同需求函数会产生不同的缓存购买策略问题， 将多个资源提供机器人和多个资源消费机器人对缓存资源的分配问题抽象为 Stackelberg 博弈。首先，资源提供机器人根据自身需求设置缓存资源的价格， 而资源消费机器人根据资源提供机器人对缓存资源的定价确定购买数量，再将购买到的缓存资源出售给任务机器人。其次，设定了任务机器人的线性需求和指数需求，推导出不同需求函数下，资源提供机器人和资源消费机器人的收益函数。最后利用逆向归纳法求解。数值仿真结果表明，在两种需求函数下，资源提供机器人的收益都是先增长后下降，而资源消费机器人的收益则一直呈下降趋势；在资源提供机器人最优收益值方面，线性需求要优于指数需求；而在到达最优收益值速度方面，指数需求要优于线性需求。
综上所述，本文从博弈理论的角度分析和认识应急场景群组机器人的资源分配问题，为应急场景中群组机器人系统的研究奠定了理论基础。
 

In the face of emergency scenarios where environmental parameters are unknown and may change at any time, the use of group robots for rescue work can reduce personal risks for subsequent rescuers and improve rescue support capabilities and rescue efficiency. In the process of rescue, group robots need to cooperate, and resource allocation among robots is the key way to realize the cooperation and cooperation mechanism of group robots. Game theory is a discipline that studies the decision-making behavior of multiple rational individuals with common or related interests, and can ensure that rational individuals make decisions according to their own needs. Aiming at the problem of resource allocation among robots, this thesis proposes a resource allocation strategy based on game theory for decentralized group robots in emergency scenarios. The main work is as follows:

(1)Aiming at the service resource allocation problem of resource-providing robots and resource-consuming robots in the emergency scene group robot system, a resource allocation model based on the Stackelberg game is proposed. In the game, firstly, the resource-providing robot prices the service resources according to its own needs, and the resource-consuming robot determines the purchased quantity according to the pricing of the service resources by the resource-providing robot. Secondly, the revenue function of the resource-consumption robot is constructed by introducing the popularity of resources and the popularity of preferred content; the revenue function of the resource-providing robot is constructed by integrating
time and energy consumption, and it is proved that after the pricing of the resource providing robot is given, the relationship between the resource consumption robot There is a Nash
equilibrium in the non-cooperative game among them. Finally, the distributed iterative method is used to solve the problem, and the equilibrium income of the resource-providing robot and the resource-consuming robot is calculated.

(2)After the stable allocation of group robot service resources is realized, different demand functions of robots when storing information will result in different cache purchasing strategies, and the allocation of multiple resources providing robots and multiple
resource-consuming robots to cache resources The abstraction is a Stackelberg game. First, the resource-providing robot sets the price of cached resources according to its own needs, while the resource-consuming robot determines the purchased quantity according to the pricing of the cached resources by the resource-providing robot, and then sells the purchased cached resources to the task robot. Secondly, the linear demand and exponential demand of task robots are set, and the income functions of resource-providing robots and resource-consuming robots are derived under different demand functions. Finally, it is solved by the backward induction method. Numerical simulation results show that under the two demand functions, the income of the resource-providing robot first increases and then decreases, while the income of the resource-consuming robot has always shown a downward trend; in terms of the optimal income value of the resource-providing robot, the linear demand is better than In
terms of exponential demand; and in terms of the speed of reaching the optimal income value, exponential demand is better than linear demand.

To sum up, this thesis analyzes and understands the resource allocation problem of group robots in emergency scenarios from the perspective of game theory, and lays a theoretical foundation for the research of group robot systems in emergency scenarios.
 

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