1. Coordination of a Large Team of Robots

    Networked decentralized multi-robot systems has a wide variety of large-scale applications such as search and rescue, exploration of unknown environments and environmental sampling.

    We propose a novel decentralized and behavior-based approach for a large group of robots moving in unknown environments with obstacles. We prove that (a) the robots will never lose connectivity while coordinating or collide with one another or obstacles during moving, and (b) the motion strategy is robust to insertion and failure of individual robots.

    Figure: Coordination of a Large Team of Robots


    1. A. Li, W. Luo, S. Nagavalli and K. Sycara, "Decentralized Coordinated Motion for a Large Team of Robots Preserving Connectivity and Avoiding Collisions" IEEE International Conference on Robotics and Automation 2017 (ICRA2017) (Submitted)

    2. State Abstraction of Multi-Robot Systems under Uncertainty

    In many scenarios involving human interaction with a remote swarm, the human-swarm communication channel is often extremely bandwidth constrained and may have high latency. For good human-swarm interaction, a summary representation can be generated by selecting a subset of robots, known as the information leaders, whose own states gives an approximation of the entire swarm.

    We propose fully distributed asynchronous algorithms for information leader selection under state uncertainty that only rely on inter-robot local communication. We provide bounded optimality analysis and proof of convergence for the algorithms.

    Figure: State Abstraction of Multi-Robot Systems


    1. A. Li, W. Luo, S. Nagavalli, N. Chakraborty and K. Sycara, "Handling State Uncertainty in Distributed Information Leader Selection for Robotic Swarms" In Proceedings of the IEEE Conference on System, Man, and Cybernetics 2016 (SMC16), October, 2016

    3. Modeling Human Actions with Recurrent Neural Network

    One of the most important complex Cyber-Physical Systems (CPS) is the physiology of the human body. In this project, we are working on designing experiments and methodologies to model Human-CPS system under a medical fluid management task.

    We conducted human experiments, dicussed results, and proposed a compuational model based on Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) architecture to model human action in this human-CPS system with high fidelity.

    Figure: Modeling Human Actions


    1. A. Li, M. Lewis, C. Lebiere, K. Sycara, S. Khatib, Y. Tang, M. Siedsma and D. Morrison, "A Computational Model Based on Human Performance for Fluid Management in Critical Care" In Proceedings of the IEEE Symposium Series on Computational Intelligence 2016 (SSCI16), December, 2016

Past Projects

All content copyright 2016 Anqi Li unless otherwise noted.

Drop me a line.