Consensus-Based Inference and Control

This project solves a multi-agent control problem involving collision avoidance and tracking under uncertainty.
Scenario: A group of smart devices (green dots) need to track an entity (yellow arrow) while coordinating to avoid collisions among themselves and with a moving threat (red dot).
To avoid the moving threat and following the target, the devices compute consensus-based estimates of the threat and target’s positions using data collected locally (by its sensors) and via neighbor-to-neighbor communication.
The devices use flocking to follow the target and avoid collisions. Flocking is a form of collective behavior that emerges from the local interactions of a large group of agents with a common objective.

Centralized approaches entail significant communication overhead and rely heavily on the performance of a single device (central node).
Hierarchical communication strategies (devices share information using a tree structure) help, but the associated risk of relying too much on the central node remains.
Linear consensus algorithms require minimal computation, communication, and synchronization to compute averages of local quantities (for example, estimates of a target’s position).
Flocking is an efficient algorithm to compute collaborative control policies.

Distributed methodologies like this one can help minimize the communication and computation consumption of devices forming Wireless Sensor Networks, a technology that is applicable in a wide range of monitoring applications (e.g., wildfire tracking, transportation surveillance, study of natural phenomena, etc.).

Code available here