online task
Distributed Online Task Assignment via Inexact ADMM for unplanned online tasks and its Applications to Security
In multi-robot system (MRS) applications, efficient task assignment is essential not only for coordinating agents and ensuring mission success but also for maintaining overall system security. In this work, we first propose an optimization-based distributed task assignment algorithm that dynamically assigns mandatory security-critical tasks and optional tasks among teams. Leveraging an inexact Alternating Direction Method of Multipliers (ADMM)-based approach, we decompose the task assignment problem into separable and non-separable subproblems. The non-separable subproblems are transformed into an inexact ADMM update by projected gradient descent, which can be performed through several communication steps within the team. In the second part of this paper, we formulate a comprehensive framework that enables MRS under plan-deviation attacks to handle online tasks without compromising security. The process begins with a security analysis that determines whether an online task can be executed securely by a robot and, if so, the required time and location for the robot to rejoin the team. Next, the proposed task assignment algorithm is used to allocate security-related tasks and verified online tasks. Finally, task fulfillment is managed using a Control Lyapunov Function (CLF)-based controller, while security enforcement is ensured through a Control Barrier Function (CBF)-based security filter. Through simulations, we demonstrate that the proposed framework allows MRS to effectively respond to unplanned online tasks while maintaining security guarantees.
- North America > United States > Massachusetts > Suffolk County > Boston (0.04)
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
How to Deploy Machine Learning on Google Cloud Platform
Editor's Note: Because our bloggers have lots of useful tips, every now and then we update and bring forward a popular post from the past. Today's post was originally published on August 15, 2019. In this post, I'll describe a few takeaways for deploying or submitting machine learning (ML) tasks on Google Cloud Platform (GCP). If you have less experience as a ML engineer, or if you're a solution architect, you might be in the right place to learn some tips. What exactly is an ML task?