This article reviews recent progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. The main areas of research include consensus, formation control, optimization, task assignment, and estimation. The article highlights the advantages of distributed control, such as low operational costs, high robustness, and flexible scalability. It also discusses the challenges and open problems in these areas, emphasizing the importance of considering practical factors like actuation, communication, and vehicle dynamics. The review covers the following key topics: 1. **Consensus**: The article discusses the conditions for achieving consensus under different network topologies, including deterministic and stochastic settings. It explores the effects of time delay, packet drops, and quantization on consensus, and introduces the concept of sampled-data consensus and asynchronous effects. 2. **Formation Control**: This section focuses on the coordination of agents to form desired geometric patterns, either without a group reference (formation producing) or with a group reference (formation tracking). It reviews the stability analysis and control algorithms for these scenarios. 3. **Optimization**: The article examines algorithmic developments for optimizing large-scale distributed systems, including the design of efficient algorithms for resource allocation and task assignment. 4. **Task Assignment**: It discusses the implementation of task-assignment algorithms in a distributed manner based on local information, which is crucial for efficient resource management and task execution. 5. **Estimation and Control**: The article explores distributed control design based on local estimation of global information, which is essential for tasks requiring precise state estimation. The article concludes with a discussion on future research directions, emphasizing the need to integrate multiple physical properties and control performance analyses in distributed multi-agent coordination.This article reviews recent progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. The main areas of research include consensus, formation control, optimization, task assignment, and estimation. The article highlights the advantages of distributed control, such as low operational costs, high robustness, and flexible scalability. It also discusses the challenges and open problems in these areas, emphasizing the importance of considering practical factors like actuation, communication, and vehicle dynamics. The review covers the following key topics: 1. **Consensus**: The article discusses the conditions for achieving consensus under different network topologies, including deterministic and stochastic settings. It explores the effects of time delay, packet drops, and quantization on consensus, and introduces the concept of sampled-data consensus and asynchronous effects. 2. **Formation Control**: This section focuses on the coordination of agents to form desired geometric patterns, either without a group reference (formation producing) or with a group reference (formation tracking). It reviews the stability analysis and control algorithms for these scenarios. 3. **Optimization**: The article examines algorithmic developments for optimizing large-scale distributed systems, including the design of efficient algorithms for resource allocation and task assignment. 4. **Task Assignment**: It discusses the implementation of task-assignment algorithms in a distributed manner based on local information, which is crucial for efficient resource management and task execution. 5. **Estimation and Control**: The article explores distributed control design based on local estimation of global information, which is essential for tasks requiring precise state estimation. The article concludes with a discussion on future research directions, emphasizing the need to integrate multiple physical properties and control performance analyses in distributed multi-agent coordination.