1 · Where should robots search?
Search target: where information matters
Team visitation: where robots actually search
2 · AC-DC communication layer
Robots choose what, when, and with whom
have searched
have sensed
3 · What improves?
Lower uncertainty with less modeled traffic
AC-DC vs ideal centralized reference
Why communication is part of the search problem
We study robot teams that gather information while they move. The mission priority specifies where reducing uncertainty matters most, while team visitation describes where the robots actually search. Coordinated ergodic search therefore needs shared information about both where the team has searched and what the team has sensed.
The network is not an unlimited background service: communication has finite range and data rate, and nearby transmissions can interfere. We keep the receding-horizon ergodic controller fixed and instead adapt the communication policy that supplies its distributed information.
What AC-DC adapts
Each robot tracks two moving team averages: trajectory statistics for team visitation and regional sensing information used to update the search target. AC-DC uses local inputs, successfully received neighbor information, and stored neighbor blocks to decide:
Select the state block whose exchange is predicted to be most useful.
Request communication when the exchange score is high enough, with an age-based refresh fallback.
Among qualified neighbors, request the neighbor with the largest score for the selected block.
The exchange decision is made before receiving the neighbor’s current block. Robots use stored neighbor blocks and compact drift summaries as a scoring proxy. When both directions of an ordinary exchange succeed, the pair averages the exchanged block and refreshes its neighbor memory.
Dynamic-priority search experiment
The mission priority changes at 100 s, so the regions that matter most also change during the run. We evaluate closed-loop search using the normalized priority-weighted remaining covariance-trace ratio and summarize each 250 s trajectory by its time-averaged AUC. Lower AUC means less remaining mission-relevant uncertainty.
Results
Across all twelve settings, AC-DC had the lowest mean AUC and attempted modeled payload among the compared decentralized methods. The paired aggregate reductions were 29.5% versus ADMM-DAC and 24.5% versus PP-ACDC. Those baselines used 5.8× and 11.5× AC-DC’s modeled traffic, respectively.
In the fixed-area scaling study, selected-block AC-DC used 19.3 MB at 120 robots versus 19.2 MB for the ideal centralized reference. This is a communication-scaling comparison, not a claim of centralized-level search performance: the corresponding AUCs were 0.129 and 0.041.
Takeaway
AC-DC treats communication as a decision inside the autonomy stack. Rather than asking every robot to exchange every state whenever possible, each robot adapts What, When, and Who using information it can maintain locally.