Research Objective:

We know that some dynamic environments have known periodic behavior, e.g., rivers with tidal shifts. To properly monitor changing environments it is necessary to assign robots to sampling locations. This is a variant of the Multi Robot Task Allocation (MRTA) problem in dynamic. Solutions to this problem can be categorized as follows. On one hand, microscopic solutions plan and control for individual robots and require expensive replanning when environment conditions change. On the other hand, macroscopic solutions design team-wide objectives to distribute robots throughout an environment. Macroscopic approaches have been shown to have nice analytical guarantees, scalability, and can be applied to heterogeneous robot collectives. However, macroscopic methods often ignore robot-robot collaborations in favor of model simplicity. As a result have been proven to be asympotically stable, i.e., given a desired distribution of robots we can find model parameters that will ensure those populations are achieved. The problem we aim to address is to design a macroscopic ensemble modeling and control method for multi robot teams that controls time-varying populations of robots. Our solution introduces robot-robot collaborations to the model which allow for the potential of time-varying distributions of robots in the workspace.

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.

Relevant Publications:

Left: An example of robot-robot collaboration. Right: An example of breaking the well-mixed assumption.

A photo of me presenting at the DARS '24 conference at Cornell Tech. Photo taken by Danna Ma.

I recently presented at DARS ‘24 at Cornell Tech on collaborative macroscopic ensemble modeling and control of robot teams. We showed that our proposed model had the possibility of capturing time-varying populations and we introduced two interpretations of robot-robot collaboration. This work had an emphasis on studying the well-mixed assumption which is critical when using macroscopic ensemble methods. Our work was well recieved by the audience and I had a great round of questions after my talk!

This is the conference video to correspond with our IROS 2023 paper.

I presented our work to IROS ‘23 in Detroit, MI. This work covered the application of collaborative macroscopic ensemble methods to environmental monitoring scenarios. Our solution avoids partitioning the environment and allows the robots to more flexibly perform the monitoring task.

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 work presented at DARS ‘22 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

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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.
On Collaborative Robot Teams for Environmental Monitoring: A Macroscopic Ensemble Approach

Edwards, Victoria, Silva, Thales C, Mehta, Bharg, Dhanoa, Jasleen, and Hsieh, M Ani

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2023

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With the rapidly changing climate and an increase in extreme weather events, it is necessary to have better methods to monitor and study the impacts of these phenomena on urban river environments. Multi-robot environmental monitoring has long focused on strategies that assign individual robots to distinct regions or task objectives. While these methods have seen success for Autonomous Surface Vehicles (ASVs), the spatial expanse and temporal variability of rivers impose an increased burden on existing techniques, necessitating computationally intensive replanning. Alternative methods aim to model and control teams of robots by prescribing global constraints on the system, using the insight that robots’ transitions between tasks are stochastic and time-based. These methods do not require replanning because robots will perform different tasks achieving the overall desired system state, focusing on temporal switching alone limits their overall descriptive power. In this paper, we present a method that considers collaborations between robots to inform task switching based on spatial proximity. Our results suggest that in unknown environments macroscopic models provide increased flexibility for individual robot task execution as compared to coverage control methods.
A Macroscopic Ensemble Modeling Approach to Collaborative Task Assignment in Dynamic Environments

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

17th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2024

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Monitoring a dynamic environment with robot teams requires continuously solving the multi-robot task allocation (MRTA) problem in response to environmental changes. The adaptive assignment of robots to different parts of the workspace as the environment changes makes this a Single-Task robots, Mulit-Robot tasks, and Time-extended Assignment (ST-MR-TA) problem. Solutions to this problem can be classified as either macroscopic, where the assignment is obtained using a mean-field model of the team dynamics, or microscopic, where the assignment is posed as a resource allocation problem. While macroscopic techniques are scalable with team sizes and number of tasks, they lack expressiveness. On the other hand, microscopic techniques are more expressive, but often require expensive replanning when environmental conditions change. In this work, we propose an alternative macroscopic formulation to the ST-MR-TA problem that results in a time-varying task assignment that can correspond to a changing environment. Our analysis of the macroscopic model uncovers parameter regimes where time-varying populations exist and further investigates the break down when the system is not well-mixed, i.e., when team sizes are small. Simulation validation shows that our proposed feedback control is necessary for small teams.

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