Embedded Evaluation of Task Admission Coalescing in Decentralized Multi-Robot Systems

James Lott; Vahraz Honary

cs.ROOct 6, 2026

Embedded Evaluation of Task Admission Coalescing in Decentralized Multi-Robot Systems

Authors: James Lott, Vahraz Honary

Organizations: Shiley Marcos School of Engineering University of San Diego San Diego, United States of America

Abstract

Multi-robot task allocators in dynamic missions commonly admit newly released tasks immediately, potentially invoking allocation for each new arrival. We evaluate task-admission coalescing as a mechanism for controlling allocator processor work while accounting for target-service latency, using CBAA, ACBBA, PI, and HIPC as a representative MRTA suite. The study comprises a 3,000-mission AGX Orin campaign with measured computation delay, 3,000 paired zero-compute missions, and a 96-mission Pololu 3pi+ RP2040 allocator hardware-in-the-loop campaign. Immediate (Eager) admission is compared with thresholds of two, four, and eight tasks and a four-task policy with a 10-s waiting bound across three arrival rates. Across the AGX experiments, coalescing reduces both allocator calls and processor work in 31 of 48 evaluated conditions, although the magnitude of the saving varies by allocator. The principal cost appears under sparse arrivals, where four-task batching increases online-target mean latency by 20.08-23.57 s, predominantly through admission waiting. Bounded admission reduces mean work in ten of twelve allocator-load conditions, including a 19.35% reduction with a 1.40-s mean latency increase for high-arrival HIPC. RP2040 experiments show that this tradeoff can become more favorable as processor constraints tighten. Across four matched medium-arrival HIPC scenarios, Count b=4 reduces mean RP2040 work by 59.85% and service latency by 39.47%, while the corresponding AGX cases increase latency by 27.69%. These results show that the usefulness of task-admission coalescing depends on arrival intensity, allocator-specific behavior, and the relative cost of computation on the execution platform.

Figures & tables

Fig. 1: Task admission and causal execution architecture. Measured AGX or RP2040 allocator durations advance the corresponding logical robot’s virtual processor.

Campaign	Policies	Traces	Planned	Complete	Other
AGX causal	5	50	3,000	2,940	60
AGX zero-compute	5	50	3,000	3,000	0
RP2040 HIL	2	4	96	82	14

TABLE I: Experimental campaigns and retained coverage.

Fig. 2: Coalescing tradeoff on AGX relative to Eager. The horizontal axis shows allocator-work change and the vertical axis shows mean target service-latency change. Columns indicate arrival loads and rows indicate the representative allocators. Points are percentage changes between paired-cohort means with 95% pointwise scenario-bootstrap intervals. Negative values indicate lower work or latency. The origin denotes Eager. All comparisons retain 50 pairs except those annotated. The four ACBBA/high sample sizes follow the legend order. Latency scales differ between columns.

Quantity	HIPC Bounded $b=4$	CBAA Count $b=4$
Admission epochs	$43.00\to 12.12$	$43.00\to 12.00$
Allocator calls	$1009.90\to 835.50$	$461.08\to 495.36$
Mean work/call (ms)	$1.631\to 1.565$	$0.304\to 0.348$
Total allocator work (s)	$1.678\to 1.353$	$0.1405\to 0.1726$
Mean target-service latency (s)	$32.37\to 33.77$	$27.02\to 31.22$
Call-count contribution (ms)	$-274.29$	$+11.46$

TABLE II: High-arrival examples of how coalescing changes allocator work and target-service latency.

Fig. 3: Matched medium-arrival HIPC scenarios under Eager and Count $b=4$ . Lines connect the same scenario under both policies. The upper panels show summed allocator work on platform-specific scales, and the lower panels show mean target-service latency on a common scale. Legend entries identify the four shared traces.

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