EARS-Info: Information-Aware Event-Driven Reallocation for Multi-Robot Exploration
Authors: Kabeh Mohsenzadegan, Vahid Tavakkoli, Kyandoghere Kyamakya
Organizations: Department of Smart Systems Technologies, University of Klagenfurt, Austria · Faculté Polytechnique, Université de Kinshasa, Democratic Republic of the Congo
We introduce EARS-Info, an information-aware extension of Event-Driven Ant Reallocation Search for multi-robot exploration on OpenStreetMap-derived grids. It combines marginal sensing gain, feasible relocation, return-aware routing, sensing reservations, and cached shortest paths. Across 168,000 episodes comparing 30 implementations on eight city maps, EARS-Info achieves the highest mean operational score in every evaluation panel. With eight robots and 3% communication dropout, it improves the score over EARS-Safe by 5.43%, increases weighted discovery from 88.88% to 98.46%, and reduces mean energy use by 2.62%. Score differences are positive in all eight cities (p=0.0078125; descriptive 95% city-bootstrap interval [0.0159,0.0694]), with no simulated incidents across 5,600 EARS-Info episodes. These results support the complete controller; individual component advantages remain uncertain. Generalization is limited by reused geography, one training replicate, coarse maps, and adapted comparison methods.
Figures & tables
Parameter
Value
Eligible-check history capacity
12 positions
First eligible simulator step
7
Stagnation unique-cell ratio
≤0.46
Repeated-visit ratio
≥0.54
Congestion threshold
≥1.15
Relocation deadline offset
9 steps (inclusive)
Table 1: EARS parameters reported with the supplied results and matching the inspected implementation defaults. Values are verified against the selected EARS checkpoint archived with the latest study.
Figure 1: The proposed information controller. Both local exploration and relocation use observable sensing gain. Graph return and runtime action filtering remain part of execution.
City
Targets
Obstacles
Reachable (%)
San Francisco
879
241
99.20
Paris
236
2
100.00
Barcelona
476
30
100.00
Rome
531
139
99.81
New York
153
4
100.00
Chicago
1
0
100.00
Table 2: Frozen test-map abstraction. Reachability is from the north-east start; Tokyo reaches 100% of targets from the south-west start.
Method
J
Discovery (%)
Coverage (%)
Energy
Time (s)
Complete (%)
EARS-Info-v9 †
0.8150
98.46
54.23
559.32
0.420
35.62
EARS-Info-NoSensingReservations †
0.8117
97.71
54.54
566.77
0.418
31.87
EARS-Info-Periodic16 †
0.8021
94.38
55.08
565.89
0.436
33.12
H-MAPPO-EARS-Safe †
0.7843
91.24
56.44
597.57
5.983
25.00
PRISM-Plus-Safe
0.7789
92.37
53.98
605.73
4.581
25.00
EARS-NP-Safe †
0.7785
89.87
57.03
607.59
3.295
18.75
Table 3: All 30 implementations at eight robots, 3% dropout, mission stopping. Equal-city means over 160 episodes per method.
Method
Mission
Fixed
SWAP
Changed
EARS-Info-v9
0.7963
0.7846
0.8154
0.8161
EARS-Safe
0.7711
0.7607
0.7818
0.7777
AntSwarmSafe
0.7716
0.7633
0.7738
0.7685
H-MAPPO-EARS
0.7757
0.7673
0.7735
0.7670
Periodic16
0.7816
0.7723
0.8023
0.8010
No sensing reservations
0.7930
0.7821
0.8126
0.8129
Table 4: Selected family and reference scores across panels. All 30 methods are retained in the accompanying CSV.
Figure 2: EARS-Info-v9 operational score across all tested team/dropout settings. Each cell is the equal-city mean over 20 episodes per city. Mission-stop and fixed-budget results are kept separate.
Figure 3: Primary-setting city mean score differences, EARS-Info-v9 minus EARS-Safe. All eight are positive; magnitudes vary considerably.
Comparator
ΔJ
Nominal p
Holm p
EARS-Safe
+0.0420
0.0078
–
AntSwarmSafe
+0.0390
0.0156
1.00
H-MAPPO-EARS
+0.0307
0.0234
1.00
Periodic16
+0.0129
0.1250
1.00
No reservations
+0.0032
0.2500
1.00
Local only
+0.1968
0.0156
1.00
Table 5: Primary-setting EARS-Info-v9 score contrasts. The EARS-Safe contrast is primary; all others are secondary.
China University of Mining and Technology-Beijing · Macau University of Science and Technology · Institute of Automation, Chinese Academy of Sciences +3