Robot teams improve mission info sharing using new communication method
DORA: Divergence-Oriented Data-Relay Algorithm for Partially Connected Robot Teams
Robotics
Summary
When flying robots look for things in an area, they need to decide whether to keep exploring or share info with teammates. This is tricky when each robot senses different things that matter more to others. The authors created a way to measure how useful new info is to teammates and use that to decide when to communicate. This method helps teams find targets faster by focusing on sharing valuable info, even with unreliable communication. They tested it both in simulations and on real drones, showing big improvements.
What this means in practice
- •For drone operations teams: Optimize drone communication strategies in search missions by relaying information only when it significantly benefits teammates’ detection capabilities.
- •For emergency response coordinators: Deploy partially connected robot teams that efficiently share mission-critical discoveries during intermittent communication in disaster zones.
Authors
Jonathan Diller, Fernando Cladera, Camillo Jose Taylor, Vijay Kumar
Abstract
Teams of unmanned aerial vehicles (UAVs) deployed for search and monitoring missions frequently operate as partially connected networks, forcing each robot to trade off exploring the environment against relaying information to teammates. This tradeoff is especially acute when robots are semantically heterogeneous: an observation that appears uninformative to the robot that made it may be critical to a teammate with complementary detection capabilities. In this work, we formalize this setting as the heterogeneous mission-aware coverage (HMAC) problem, which couples complete multi-robot coverage of an area with capability-constrained mission-relevant target (MRT) discovery under intermittent communication. We then present DORA, a divergence-oriented data-relay algorithm that drives communication by the value of information to the team rather than by discovery alone. DORA quantifies the mission-relevant divergence between a robot's current information state and its estimate of each teammate's knowledge, capturing mission relevance, discovery novelty, sensor uncertainty, and the age of information. We evaluate DORA in simulation across four environments with differing object densities and spatial structure, and validate it on a physical UAV platform. Our results show that DORA improves MRT resolution delay by up to 74.8% over traditional time-based communication scheduling methods.