Chao Jiang, Ph.D.

Chao Jiang, Ph.D.

He/Him/His

Associate Professor of Electrical Engineering and Computer Science

Multi-Robot Coordinated Planning and Control

Multi-Robot Trajectory Optimization

1. Multi-Robot Trajectory Optimization via Model Predictive Control

  • Distributed Variational Inference MPC for Multi-Robot Navigation [1]
    Stochastic skill prediction
    A multi-robot team represented as a Markov random field.
    Stochastic skill prediction 2
    An overview of the distributed VI-MPC algorithm.
    Demonstration of distributed sampling-based MPC for multi-robot formation navigation.
  • Incorporating Control Barrier Functions in Distributed MPC [2]
    Stochastic skill prediction
    Trajecotries of multi-robot formation
    Stochastic skill prediction 2
    Results of DMPC for multi-robot coordinated control with CBF safety.

2. Learning-Enabled Multi-Robot Formation

Optimization overview
Snapshots of triangulation formation using a GNN-based policy (9 robots). [3]
Optimization overview
Triangulation formation using GNN-based policies for different robot team sizes. [3]

Publications:

  • [1] Jiang, C. (2024). Distributed Sampling-Based Model Predictive Control via Belief Propagation for Multi-Robot Formation Navigation. IEEE Robotics and Automation Letters, vol. 9, no. 4, pp. 3467-3474. DOI: 10.1109/LRA.2024.3368794.
  • [2] Jiang, C., & Guo, Y. (2023). Incorporating Control Barrier Functions in Distributed Model Predictive Control for Multi-Robot Coordinated Control. IEEE Transactions on Control of Network Systems. DOI: 10.1109/TCNS.2023.3290430.
  • [3] Jiang, C., Huang, X., & Guo, Y. (2023). End-to-End Decentralized Formation Control Using Graph Neural Network Based Learning Method. Frontiers in Robotics and AI, 10, 1285412.
  • [4] Jiang, C., & Guo, Y. (2020). Multi-robot guided policy search for learning decentralized swarm control. IEEE Control Systems Letters, 5(3), 743-748.