Adaptive communication improves multi-robot search coordination and efficiency
AC-DC: Adaptive Communication for Scalable Dynamic Average Consensus in Multi-Robot Ergodic Search
Robotics
Summary
Coordinating many robots to work together can be hard because they need to share information quickly, but wireless communication has limits like range and data capacity. The authors created a method called AC-DC that helps robots decide who talks to whom, when, and what pieces of information they share, adapting as things change. This way, robots can better estimate group data without waiting for everyone at once, which saves communication and speeds up teamwork. Their tests with up to 120 robots showed better search results with much less communication traffic compared to other methods.
What this means in practice
- •For robotics engineers: Coordinate large robot teams more efficiently during search tasks by adapting communication patterns to reduce data traffic and speed consensus.
- •For autonomous drone operators: Improve on-the-fly data sharing among fleets of drones to maintain accurate team awareness under communication limits.
Authors
Robin Inho Kee, Begum Cannataro, Vasileios Tzoumas
Abstract
We study scalable peer-to-peer dynamic average consensus (DC) for multi-robot systems under finite-range, finite-rate, and interference-constrained communication. We introduce Adaptive Communication for Dynamic Average Consensus (AC-DC), which jointly adapts Who communicates with whom, When, and over What parts of the consensus state, using local inputs and successfully received neighbor information. Each robot's consensus state estimates the current average of the robots' local inputs. AC-DC updates these estimates as local inputs change and averages the values exchanged between robot pairs. In AC-DC, robot pairs update without waiting for every robot to complete a communication round, and the selected-state messages carry consensus state coordinates independent of team size for a fixed state representation. We apply AC-DC to dynamic-priority multi-robot ergodic search: one consensus stream estimates team visitation for motion coordination, while the other fuses regional measurement information to update uncertainty maps and search targets. Across twelve settings with up to 80 robots and 20 paired trials per setting, AC-DC has the lowest mean (i) normalized covariance-trace area under the curve (AUC) and (ii) attempted modeled communication payload among the compared decentralized methods. Averaged across settings, AC-DC achieves paired AUC reductions of 27.5% relative to state-of-the-art baselines, with 8.7x less communication traffic. As the number of robots increases, we observe that AC-DC's communication payload approaches that of the ideal centralized baseline (one ground compute-station communicating directly with all robots): with 120 robots in a fixed 600 x 600 m scaling test, AC-DC uses 19.3 MB versus 19.2 MB for the ideal centralized baseline, while remaining peer-to-peer.