Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents
提出一种分布式搜索规划框架，让动态变化的自主代理团队在三维空间中协作搜索多个目标，代理可随时进出任务空间，利用模型预测控制生成协同搜索轨迹，减少重复工作。
In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.