# Online Submodular Coordination With Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination

@article{Xu2022OnlineSC,
title={Online Submodular Coordination With Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination},
author={Zirui Xu and Hongyu Zhou and Vasileios Tzoumas},
journal={IEEE Robotics and Automation Letters},
year={2022},
volume={8},
pages={2261-2268}
}
• Published 26 September 2022
• Computer Science
• IEEE Robotics and Automation Letters
We enable efficient and effective coordination in unpredictable environments, i.e., in environments whose future evolution is unknown a priori and even adversarial. We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring. Such tasks are often modeled as submodular maximization coordination problems. We introduce the…

## Figures and Tables from this paper

• Computer Science
ArXiv
• 2023
This paper introduces the first projection-free meta-base learning algorithm with memory that minimizes dynamic regret and develops the ﬁrst controller with memory and bounded dynamic regret against any optimal time-varying linear feedback control policy.
• Computer Science
ArXiv
• 2022
An attack detection scheduling algorithm based on sequential submodular maximization, which incorporates expert problem to better cope with dynamic attack strategies is proposed, which can run in polynomial time with a theoretical lower bound on the optimization rate.

## References

SHOWING 1-10 OF 42 REFERENCES

• Computer Science
IEEE Robotics and Automation Letters
• 2021
This work proposes a fully distributed algorithm to guide each robot's action selection when the system is attacked, and guarantees performance in a worst-case scenario where up to a portion of the robots malfunction due to attacks.
• Computer Science
IEEE Robotics and Automation Letters
• 2019
This letter provides the first such algorithm for multi-target tracking that achieves maximal resiliency, and provides provable approximation bounds on the tracking performance, and quantifies the algorithm's approximation performance using a novel notion of curvature for monotone set functions subject to matroid constraints.
• Computer Science
2022 IEEE 61st Conference on Decision and Control (CDC)
• 2022
This paper introduces the first algorithm enabling the robots to choose with which few other robots to coordinate and provably balance the trade-off of centralized vs. decentralized coordination, and introduces the notion of Centralization Of Information among non-Neighbors (COIN).
• Computer Science
Auton. Robots
• 2019
Real-time simulation and experimental results for teams of quadrotors demonstrate online planning for multi-robot exploration and indicate that collision constraints have limited impacts on exploration performance.
• Computer Science
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 2021
This work proves that RSP planning approaches performance guarantees for fully sequential planners, by employing a novel bound which takes advantage of the independence of target motions to quantify effective redundancy between robots’ observations and actions.
• Computer Science
2018 IEEE Conference on Decision and Control (CDC)
• 2018
This work develops new tools for analysis of submodular maximization problems which are applied to design of randomized distributed planners that provide constant-factor suboptimality approaching that of standard sequential planners.
• Computer Science
IEEE Transactions on Robotics
• 2022
The first receding horizon algorithm, aiming for robust and adaptive multirobot planning against any number of attacks, is proposed, which is called RAIN, and based on notions of curvature introduced in combinatorial optimization is evaluated.
• Computer Science
AISTATS
• 2021
A tracking-regret-analysis framework is applied to online submodular optimization, one by which output is assessed through comparison with time-varying optimal subsets, and it is shown that the tracking regret bound for sub modular minimization is nearly tight.
• Computer Science
Robotics: Science and Systems
• 2020
This paper introduces the Robust Multiple-path Orienteering Problem (RMOP) where the main contribution is a general approximation scheme with bounded approximation guarantee that depends on $\alpha$ and the approximation factor for single robot orienteering.
• Computer Science
2015 IEEE International Conference on Robotics and Automation (ICRA)
• 2015
This paper provides a non-greedy centralized solution to the problem of controlling mobile sensing systems to improve the accuracy and efficiency of gathering information autonomously, and decentralizes the control task to obtain linear complexity in the number of sensors and provide suboptimality guarantees.