Research Objective:

This work aims to further understand robot teams performing tasks in complex unstructured environments. We use stochastic macroscopic modeling to model and control high level team objectives, and provide mappings to microscopic instantiations of robot teams.

The miniature Autonomous Surface Vehicles (mASV) are differential drive robots which use an Arduino Fio, XBee communication, and OptiTrack for global positioning.

We experimentally verify our methods using the miniature Autonomous Surface Vehicles (mASVs) which are built in house at the ScALAR lab. Our testing environment is a 4m x 3m x 1.5m tank, equipped with 13 OptiTrack cameras, and the capability to make gyres in the tank. At any given time we can have upwards of 10 mASV in the tank performing different control strategies. Within the tank we have demonstrated heterogeneous teams using Crazyflie robots and mASV simultaneously.

Recent Work:

This video shows mixed reality trials with 4 real miniature Autonomous Surface Vehicles (mASV) and 6 simulated robots. The result is a robot team performing different tasks (red, blue, or green lights) where the distributions change over time without changing the model parameters or re-planning.

Our recent work presented at DARS uses a nonlinear stochastic model to achieve time-varying distributions of robots which was a limitation of previous linear stochastic models. Our results suggest further work is needed to understand how macroscopic models can better incorporate feedback from the environment.

Associated Publications

Stochastic nonlinear ensemble modeling and control for robot team environmental monitoring

Edwards, Victoria, Silva, Thales C, and Hsieh, M Ani

16th International Symposium on Distriubted Autonomous Robotic Systems (DARS) 2022

Abs

We seek methods to model, control, and analyze robot teams performing environmental monitoring tasks. During environmental monitoring, the goal is to have teams of robots collect various data throughout a fixed region for extended periods of time. Standard bottom-up task assignment methods do not scale as the number of robots and task locations increases and require computationally expensive replanning. Alternatively, top-down methods have been used to combat computational complexity, but most have been limited to the analysis of methods which focus on transition times between tasks. In this work, we study a class of nonlinear macroscopic models which we use to control a time varying distribution of robots performing different tasks throughout an environment. Our proposed ensemble model and control maintains desired time varying populations of robots by leveraging naturally occurring interactions between robots performing tasks. We validate our approach at multiple fidelity levels including experimental results, suggesting the effectiveness of our approach to perform environmental monitoring.

This work is done in collaboration with Dr. Thales C. Silva and Dr. M. Ani Hsieh at the University of Pennsylvania GRASP lab.