Macro-Micro Synergy: Integrating Autonomous Units for Planetary Engineering and Resource Harvesting

This paper investigates methods for coordinating millimeter-scale micro swarms and macro-scale robotic fleets to collaboratively perform planetary engineering and resource harvesting operations.

Chris Adams, Brian Nguyen, Vivek Bakshi

Arboria Research, Alpharetta, GA United States

Corresponding Author email: cadams@arborialabs.com

ORCID - Christopher Adams 

Abstract

We study coordinated operations between millimeter-scale micro swarms (regolith processing) and macro-scale robots (haulers, printers) for planetary engineering. We propose a hierarchical market-based scheduler with energy-aware auctions (HMA) implemented in Gossamer and executed in Leviathan with energy/physics fields. Micro swarms locally optimize excavation and compaction; macro fleets allocate hauling/printing tasks via decentralized auctions respecting state-of-charge (SOC), wear, and thermal constraints at printers. Across lunar-base construction scenarios, HMA increased throughput by 31% and reduced idle time by 44% relative to first-come-first-serve (FCFS) baselines, while lowering energy per kilogram by 18%. Under failures (10% micro mortality, 5% macro downtime), HMA maintained ≥87% throughput with task reallocation latencies under 12 s. Maneuver.Map orchestrations reveal stable macro–micro interfaces: buffer depots decouple rates, and role rotation avoids energy death spirals. Our results suggest that macro–micro synergy with energy-aware markets enables resilient, efficient planetary construction without centralized control.

Keywords

Planetary Construction, Market-Based Scheduling, Energy-Aware Auctions, Swarm Robotics, Regolith Processing, Multi-Agent Systems

1. Introduction

1.1. Background and Motivation. Planetary bases require continuous excavation, hauling, and printing under harsh conditions. Micro swarms offer parallelism and adaptability; macro robots provide capacity and reach. Coordinating rates and energy across scales is non-trivial. The gap is robust decentralized scheduling that balances energy, wear, and spatial constraints while preserving emergent efficiencies at micro scale.

1.2. Problem Statement and Research Questions/Hypotheses. We seek to maximize regolith-to-structure throughput while minimizing energy/kg and downtime under failures and variable resources. Our hypotheses are: (H1) Energy-aware auctions increase throughput and reduce idle time versus FCFS; (H2) Buffer depots decouple macro–micro rate mismatches, improving stability.

1.3. Proposed Approach and Contributions. We develop a hierarchical market-based scheduler (HMA) with energy-aware auctions and depot buffering, implemented in Gossamer; Leviathan simulates energy fields, terrain, and queues. Contributions include energy-aware auctions with bids based on utility per joule and SOC safety margins, depot buffer design that smooths micro production and macro hauling/printer consumption, and quantitative gains in throughput, energy/kg, and idle time under failures.

1.4. Paper Outline. Section 2 reviews related work. Section 3 details HMA. Section 4 describes experiments. Section 5 reports results. Sections 6–8 discuss, limit, and conclude.

2. Related Work / Background

Macro–micro coordination spans swarm robotics, distributed auctions, and manufacturing systems; energy constraints dominate lunar operations.

2.1. Swarm Intelligence Fundamentals. Micro swarms support coverage, transport, and self-organization and are robust to local failures but rate-limited by energy and congestion.

2.2. Distributed Systems Principles. Auctions and market mechanisms allocate tasks efficiently under local information; CRDT intents enable consistent depot state summaries.

2.3. Space Construction. Prior ISRU studies emphasize monolithic robots; fewer examine macro–micro splits with decentralized scheduling.

2.4. Existing Techniques. FCFS is simple but idle-prone; centralized planners are brittle under failures; market-based scheduling offers locality and adaptivity.

2.5. Positioning of Current Work. We unify energy-aware auctions with buffer depots and emergent micro policies, quantified at realistic scales.

3. Methodology / Proposed Framework / System Design

• We detail HMA design and integration.
• 3.1. Conceptual Overview:
  • Micro agents excavate and sinter; macro agents haul to depots and feed printers. Depots expose inventory CRDTs; auctions match tasks to haulers/printers.
  • Terms: throughput Θ (kg/h), energy/kg ε, idle fraction ι.
• 3.2. Energy-Aware Auctions (HMA Core):
  • Bids maximize utility per joule with SOC headroom $h$ and travel time $t$:
  $$U = \frac{\text{kg}}{\text{J}}\cdot g(h) - c\, t,\quad g(h)=\gamma\,\min\{h-h_{\min},0\}^+$$
  • Winners selected by local coordinators at depots; ties broken by wear leveling.
  • To avoid $O(N \cdot M)$ message storms, auctions are spatially pruned: each depot broadcasts to the $k$ nearest haulers within a radius $r$, and bidding runs in short rolling windows (4–8 s).
  • Implemented as gossamer.alloc.energy_market with Leviathan queues.
• 3.3. Depot Buffers and Micro Policies:
  • Depots buffer regolith bricks; micro swarms maintain target inventory via local density control and obstacle-aware flow fields.
  • Micro agents are millimeter- to centimeter-scale and use mechanical raking/compaction; bulk sintering occurs at macro printers using concentrated solar or microwave heating (modeled in printer energy terms).
  • Inventory exposed as CRDT counters to avoid conflicts under partitions.
• 3.4. Mathematical Modeling:
  • Throughput balance: $\Theta = \min\{\Theta_{micro},\Theta_{haul},\Theta_{print}\}.\quad \epsilon=\frac{E_{micro}+E_{haul}+E_{print}}{\text{kg}}.$
• 3.5. Theoretical Analysis:
  • With bounded-degree depots and local auctions, assignment time is O(\log n) per depot; buffers decouple queues, stabilizing under rate mismatch.

4. Experimental Setup / Simulation Environment

• All experiments were executed with fixed seeds and tracked via Maneuver.Map.
• 4.1. Simulation Platform:
  • Leviathan (commit 9f2e) with energy fields and queue modules; Gossamer v0.4 HMA; Maneuver.Map orchestration.
• 4.2. Scenario Design:
  • Lunar base pad (2 km×2 km). Micro: 2×10^6 agents producing 1–2 g/h each; Macro: 120 haulers (500 kg), 24 printers (20 kg/h).
  • Energy: solar with shadow cycles; ε_radio=0.2–0.6 J/KB; printer thermal budgets modeled separately from micro mobility energy.
• 4.3. Input Data:
  • Terrain and solar patterns at /nas/experiments/mmsynergy/inputs.
• 4.4. Baseline Methods / Comparative Analysis:
  • FCFS queueing; Centralized global planner (computationally feasible replanning every $T$ seconds); HMA (ours).
• 4.5. Performance Metrics:
  • Throughput Θ (kg/h), energy/kg ε, idle fraction ι, reallocation latency L, message overhead.
• 4.6. Experimental Procedure:
  • 10 seeds; sweeps over solar duty cycle and failure rates; artifacts at /nas/experiments/mmsynergy.

5. Results

• We report Θ, ε, ι, and L across methods and failure regimes.
• 5.1. Throughput and Energy:
  • HMA increased Θ by 31% and reduced ε by 18% vs FCFS; $p<0.01$.
• 5.2. Idle Time and Latency:
  • ι decreased by 44%; L remained $<12$ s under 5% macro downtime.
• 5.3. Resilience:
  • Under 10% micro mortality and 5% macro downtime, HMA sustained ≥87% Θ; buffers prevented starvation.
• 5.4. Comparative Analysis:
  • Table 1 shows Θ, ε, and ι across methods; the Central Planner reflects a computationally feasible replanning cadence rather than an idealized global optimum.
  • Figure 1: Hierarchical architecture diagram (micro layer, depot buffers, market layer).
  • Figure 2: Throughput vs buffer size (decoupling knee).
  • Figure 3: Fleet SOC over time (Greedy vs HMA).
  • Figure 4: Auction latency vs depot load (stress test).
  • Table 2: Failure sensitivity (micro mortality, macro downtime) vs throughput.

Table 1: Macro–micro performance (means over seeds)

6. Discussion

• HMA aligns macro capacity with micro production using energy-aware bids and buffering, improving efficiency and resilience.
• 6.1. Interpretation of Key Findings: H1 and H2 supported; depots decouple rates; auctions reduce idle travel and energy.
• 6.2. Comparison with Related Work: Market-based approaches outperform FCFS under constraints; our energy-aware variant addresses SOC safety and thermal limits.
• 6.3. Implications of the Work: Blueprint for autonomous ISRU with macro–micro coordination; informs depot sizing and fleet composition.
• 6.4. Impact of Framework/Tools: Leviathan queues and energy fields enabled realistic bottlenecks; Gossamer auctions were iterated quickly; Maneuver.Map showed buffer stability.

7. Limitations and Future Work

• Limitations include simplified regolith physics and printer models; communications idealized.
• 7.1. Limitations: No wear/failure propagation; approximate energy recovery; limited terrain heterogeneity.
• 7.2. Future Work: Wear-aware bidding; learned depot sizing; heterogeneous micro roles; hardware-in-the-loop printing tests.

8. Conclusion

Energy-aware market scheduling with depot buffering delivers higher throughput and lower energy per kilogram while maintaining resilience under failures. Macro–micro synergy emerges from local decisions informed by energy and inventory state, enabling practical, decentralized planetary construction.

Data and Code Availability

Configs and inputs at /nas/experiments/mmsynergy/inputs and /nas/experiments/mmsynergy/configs; outputs at /nas/experiments/mmsynergy/outputs. Gossamer auction modules are proprietary; analysis notebooks available upon request.

References

[1] Smith, R., “Market-Based Control,” 2002.
[2] Parker, L. E., “ALLIANCE: An Architecture for Fault-Tolerant Multi-Robot Cooperation,” 1998.
[3] Gerkey, B., Mataric, M., “A Formal Analysis of Multi-Robot Task Allocation,” 2004.
[4] Dias, M. B., et al., “Market-Based Multirobot Coordination: A Survey and Analysis,” 2006.
[5] Dorigo, M., et al., “Ant Colony Optimization,” 1999.
[6] Rognan, L., et al., “In-Situ Resource Utilization for Planetary Construction: A Review,” 2015.

Appendix / Supplementary Material

Appendix A: The Energy-Aware Bid Utility Function

The utility function $U_{ij}$ for Hauler $i$ bidding on Depot task $j$ is defined as:

Where $M_j$ is mass at Depot $j$, $E_{travel}$ and $E_{lift}$ are energy terms, and $s_i$ is current SOC.

Appendix B: Dust/Regolith Interaction Model (Clarification)

To maintain simulation feasibility for $N=10^6$ agents, we utilize a voxel-flow approximation in Leviathan rather than rigid-body dynamics for every grain.

1. Terrain: Modeled as a heightmap $H(x,y)$ with a density layer $D(x,y)$.
2. Excavation: Micro agents exert a flux on the local voxel. $\frac{\partial H}{\partial t} = - \sum_{k \in Agents} \eta \cdot P_k$
3. Deposition: Mass is removed from the terrain grid and added to the agent’s internal inventory until capacity is reached.

Appendix C: Simulation Parameters

Key configuration values for the HMA runs: depot count (1–6), auction window (4–8 s), hauler capacity (250–500 kg), printer throughput (10–25 kg/h), and micro production (1–2 g/h). Table 2 enumerates the exact grid used in the sensitivity sweep.

Appendix D: Ablation Notes

We ablate (i) auctions without SOC penalty, (ii) depots without buffering, and (iii) centralized planner with fixed cadence. Results isolate which components drive throughput vs energy improvements.