Archive for month: January, 2022

DCIST researchers pioneered strategies for teams of mobile robots to form intermittently connected communication networks by leveraging their mobility.  Robots assigned to monitor and patrol large urban environments can leverage their movements to carry information to other robots that are not within their communication ranges. Our work shows intermittent connectivity between pairs of robots can be achieved by synchronizing the robots’ motions and ensuring robots rendezvous or move into each other’s communication ranges periodically.  And while the network is not fully connected at any instance in time, it is guaranteed to be connected over a predetermined period of time ensuring the successful propagation of information across the entire network.  In addition, these strategies can be extended to guarantee resiliency of the mobile robot network in the presence of non-cooperative robots.  Resilience for a large intermittently connected mobile robot network is achieved by concatenating modular dynamic formations where each module is guaranteed to be resilient by design.

Left: The creation of a time-varying periodically connected in an urban environment by a team of mobile robots tasked to patrol different buildings.  Right: Two examples of time-varying networks formed using lattice modules.  Red nodes represent malicious agents.  Plots on the right show consensus formation in the network over time.

Capability: T3C2C Mobility to Extend Communication Networks 

Points of Contact: M. Ani Hsieh (PI), Xi Yu, Daigo Shishika, David Saldana

Paper: https://10.1109/LRA.2020.2967704, https://10.1109/TRO.2021.3088765 

Citation: X. Yu and M. A. Hsieh, “Synthesis of a time-varying communication network by robot teams with information propagation guarantees,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1413–1420,2020.

Yu, D. Shishika, D. Saldana and M. A. Hsieh. “Modular Robot Formation and Routing for Resilient Consensus,” in the Proc. of the 2020 IEEE American Control Conference (ACC 2020), Jul 2020 (Virtual).

X. Yu, D. Saldana, D. Shishika, and M. A. Hsieh.“Resilient Consensus in Robot Swarms with Periodic Motion and Intermittent Communication,” in IEEE Transactions on Robotics and Automation (T-RO), 2021.

A recent paper by members of the DCIST alliance proposes a new resource allocation game that studies a dynamic, adversarial resource allocation problem in environments modeled as graphs. By combining ideas from Colonel Blotto games with a population dynamics model, the proposed formulation incorporates: (i) dynamic reallocation in time-varying situations, and (ii) the presence of adversarial agents. A blue team of defender robots are deployed in the environment to protect the nodes from a red team of attacker robots. The engagement is formulated a discrete-time dynamic game, where the robots can move at most one hop in each time step. The game terminates with the attacker’s win if any location has more attacker robots than defender robots at any time. The goal is to identify dynamic resource allocation strategies, as well as the conditions that determine the winner: graph structure, available resources, and initial conditions. The authors analyze the problem using reachable sets and show how the outdegree of the underlying graph directly influences the difficulty of the defending task. Furthermore, they provide algorithms that identify sufficiency of the attacker’s victory. The proposed model has a potential for being extended to various scenarios to study dynamic and adversarial engagement between robots with traversability constraints.

Capability: T2C1B: Distributed Control for Dynamic Resource Allocation in Adversarial Environments

Points of Contact: Daigo Shishika (PI), Scott Guan, Michael Dorothy 

Paper: https://arxiv.org/pdf/2112.09890.pdf

Citation: Daigo Shishika, Yue Guan, Michael Dorohy, and Vijay Kumar, “Dynamic Defender-Attacker Blotto Game,” (under review), arXiv preprint, arXiv:2112.09890 (2021).

A recent paper by members of the DCIST alliance uses the deep learning method, knowledge-based neural ordinary differential equations (KNODE) to develop a data-driven approach for extracting single-robot controllers from the observations of a swarm’s trajectory. The goal is to reproduce global swarm behavior using the extracted controller. Different from the previous works on imitation learning, this method does not require action data for training. The proposed method can combine existing knowledge about the single-robot dynamics, and incorporates information decentralization, time delay, and obstacle avoidance into a general model for controlling each individual robot in a swarm. The decentralized information structure and homogeneity assumption further allow the method for scalable training, i.e., the training time grows linearly with the swarm size. This method was applied on two different flocking swarms, in 2D and 3D respectively, and successfully reproduced global swarm behavior using the learnt controllers. In addition to the learning method, the paper also proposed the novel application of proper orthogonal decomposition (POD) for evaluating the performance of a learnt controller. Furthermore, extensive analysis on hyperparameters is conducted to provide more insights on the properties and  characteristics of the proposed method.

Capability: T3C4C – Adaptive Swarm Behaviors for Uncertainty Mitigation (Hsieh)

Points of Contact: M. Ani Hsieh (PI) and Tom Z. Jiahao

Video: https://drive.google.com/file/d/1QV4kE8K0nYcoLWHTAZ9BNsSI0b4dUax_/view?usp=sharing

Paper: https://arxiv.org/pdf/2109.04927.pdf

Citation: T. Z. Jiahao, L. Pan, M. A. Hsieh “Learning to Swarm with Knowledge-Based Neural Ordinary Differential Equations.” Arxiv Preprint, December 2021.