Distributed Model Predictive Control with Connectivity-based Contracts
This paper proposes a scalable Distributed Model Predictive Control (DMPC) framework that ensures team connectivity for mobile robots by assigning each agent a local "connectivity contract" derived from neighbor exchanges, thereby guaranteeing network cohesion, collision avoidance, and constraint satisfaction without requiring global connectivity assumptions.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine a swarm of small, autonomous vehicles moving together through a crowded space, perhaps searching for survivors in a collapsed building or delivering supplies across a disaster zone. For these machines to function as a team, they must constantly talk to one another, sharing their positions and plans to avoid crashing and to coordinate their movements. If the group loses its ability to communicate, the team falls apart; information cannot flow, safety is compromised, and the mission fails. The challenge for engineers is to design a control system that lets these robots move freely to reach their goals while actively ensuring they never drift so far apart that they lose contact. This is a difficult balancing act because the mathematical rules that guarantee a group stays connected are complex and interdependent, making it hard to calculate a safe path for every robot in real time without a central computer telling everyone what to do.
Researchers have long sought a way to solve this problem using a method called distributed model predictive control, where each robot calculates its own future path based on what it knows about its neighbors. However, most existing approaches simply assume the network stays connected, rather than building a system that actively enforces it. When the mathematical condition for connectivity is applied directly, it creates a tangled web of constraints that links every single robot to every other robot, destroying the ability to calculate paths quickly and independently. To overcome this, a team of engineers from ETH Zurich and EPFL has developed a new framework that replaces these complex global rules with simple, local agreements. They call these agreements "contracts."
In this new system, each robot is assigned a specific, safe zone—a local region of space—where it is allowed to move over a short period of time. These zones are not arbitrary; they are carefully calculated based on a single exchange of information with immediate neighbors. The key insight is that if every robot stays within its own assigned zone, the entire group is mathematically guaranteed to remain connected. The researchers designed these zones so that they overlap just enough to keep the communication links alive, effectively creating a safety net that prevents the network from breaking. Instead of trying to solve one massive, impossible equation for the whole swarm, each robot only needs to solve a simple, independent problem: how to reach its target while staying inside its own moving boundary.
The team tested this approach using miniature, car-like robots in both computer simulations and real-world hardware experiments. In the simulations, they placed seven robots in cluttered environments filled with obstacles and asked them to navigate to specific target locations. They compared their new method against older techniques that either ignored connectivity or tried to enforce it using heavy, complex calculations that required all robots to be tightly coupled. The results were clear: the older methods either failed to keep the group connected or required so much computing power that they could not run fast enough for real-time control. In contrast, the new contract-based system kept the network connected in every single trial, allowed the robots to avoid collisions, and reached their destinations efficiently. The robots solved their individual path-planning problems in just a few milliseconds, a speed that makes real-time control possible even on small, low-power computers.
When the researchers moved the system to physical hardware, using real robots equipped with Wi-Fi and motion-capture sensors, the results held up. The robots successfully navigated a cluttered environment, maintaining a connected communication network throughout the entire journey. Without the new contracts, the robots drifted apart, and the network broke, causing the algebraic measure of their connectivity to drop to zero. With the contracts in place, the network remained robust, and the robots stayed safely within their communication range while avoiding obstacles. The system proved that it is possible to enforce complex group behaviors through simple, local rules, allowing a swarm to act as a cohesive unit without needing a central brain or constant, heavy computation. This work demonstrates that by rethinking how robots agree on where they can go, engineers can create autonomous teams that are both safe and scalable, capable of operating in the messy, unpredictable real world.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.