Micro-Navigation Economic Testbed

• Micro-navigation economic testbeds are platforms that combine navigation tasks with economic cost evaluation to analyze agent decision-making under realistic tradeoffs.
• They utilize diverse architectures—from agent-based simulations to physical mini-cities—to benchmark algorithms against complex economic and operational metrics.
• Insights from these testbeds inform optimization strategies and policy decisions by quantifying the balance between technical performance and economic viability.

A micro-navigation economic testbed is a physical or computational platform designed to evaluate navigation behaviors, decision-making, or autonomous control policies in environments that meaningfully expose economic costs and tradeoffs, under either simulated or real-world conditions. This class of testbed integrates micro-scale navigation tasks (e.g., agent routing, vehicle driving, web interaction, maritime control) with metrics grounded in economic principles—ranging from explicit profit/loss functions, resource utilization, maintenance costs, service-level agreements, or spatially-bounded agent economies. Contemporary testbeds span agent-based economic simulations, web-based economic task environments, robotic and vehicle micro-cities, commercial delivery robotics, and marine autonomy benchmarking.

1. Core Architecture and Instantiations

Micro-navigation economic testbeds are embodied through a range of architectures, each targeting specific application domains:

• Agent-based microeconomic environments. SEAL (Spatially-bounded Economic Agent-based Lab) organizes a set of Python modules representing citizens, families, households, firms, and government municipalities, each spatially embedded using real geographic shapefiles. Core modules advance time, update agent demographics, handle market interactions, and exchange state through global lists of agents, firms, households, and governments (Furtado et al., 2016).
• Web navigation economic tasks. EconWebArena operationalizes complex web navigation as an economic intelligence benchmark, exposing agents to 360 tasks drawn from 82 authoritative economic websites. The environment leverages BrowserGym (headless Chrome with AXTree and screenshot access), and defines agent actions over structured page elements, coordinates, browser tab management, and web navigation primitives (Liu et al., 9 Jun 2025).
• Physical mini-cities and connected vehicles. Smart mini-city platforms create 1/10th scale urban environments for evaluating autonomous and human-driven vehicles in controlled, extensible urban layouts. Systems are equipped with vehicles (e.g., MuSHR-inspired platforms), intelligent road infrastructure (e.g., V2I LiDAR-enabled intersections), real traffic signage, and pedestrian figures (Vargas et al., 2024).
• Cost-aware robot delivery. CostNav is a dedicated economic testbed for ground robot navigation, grounding navigation performance in a full economic lifecycle model—hardware amortization, training/maintenance, energy, operating costs, and SLA-constrained revenue. Integration with NVIDIA Isaac Lab and open-source urban packs enables systematic exploration of delivery economics (Seong et al., 25 Nov 2025).
• Marine digital-physical autonomy laboratories. Ship autonomy testbeds use model-scale vessels, high-fidelity simulation, sensor-rich and digital twin environments (Unity), and scalable ROS 2 middleware to benchmark guidance, navigation, and control (GNC) policies under quantifiable economic and operational constraints (Gezer et al., 10 May 2025).

2. Spatial and Environmental Modeling

Testbeds encode space and environment to ground navigation and economic activity:

• Geospatial agents. In SEAL, households and firms hold point geometries in $(x,y)$ using OGR/IBGE shapefiles, inheritance of agent location from household, and assignment consistent with empirical urban/rural splits. All commuting and consumption uses Euclidean distance $d_{i,j}=\sqrt{(x_i-x_j)^2+(y_i-y_j)^2}$; regional commutes aggregate across employed agents (Furtado et al., 2016).
• Simulated urban maps. Mini-city layouts are specified with precise area, materials, and costs. Building footprints, road segments, intersections, and obstacles are physically constructed, enabling systematic experiments. Facilities include 2D LiDAR, stereo/monocular cameras, WiFi infrastructure, and configurable signage or traffic elements (Vargas et al., 2024).
• Synthetic or live online spaces. In EconWebArena and robotic navigation, navigation challenges are created within live browser pages (structured by accessibility trees and visual layout) or auto-generated obstacle maps, such as the 300 BARN environments by cellular automata (Perille et al., 2020, Liu et al., 9 Jun 2025).

3. Micro-Navigation Algorithms and Economic Decision Rules

A key characteristic is the joint modeling of navigation and economic processes:

• Agent economic action. In SEAL, agents’ micro-navigation is realized through routines combining location choice (min distance or min price), random sampling of firms or jobs, and decision rules governing consumption, labor matching, and real estate moves. Probabilistic mechanisms interact with agent resource constraints and policy parameters (e.g., BETA for consumption propensity, SIZE_MARKET for market choice set) (Furtado et al., 2016).
• Web and GUI navigation primitives. EconWebArena formalizes an agent’s action space $\mathcal{A}$, including element- and coordinate-level interactions (e.g., click, select_option, mouse_move, keyboard_type), navigation (goto, tab management), and contextual messaging. Task workflows can involve view toggling, table parsing, form submission, and numeric extraction; navigation choices determine outcome and success (Liu et al., 9 Jun 2025).
• Physical control and autonomy. Mini-city vehicles implement micro-navigation via onboard pure-pursuit steering control, dynamic window obstacle avoidance, and V2I-assisted trajectory planning. Mapping and SLAM, depth estimation with stereo disparity ($Z = f B / d$), and local costmaps support real-time collision and path selection. Marine vessels implement PI-feedback, extended Kalman filtering, and quadratic thrust allocation (Vargas et al., 2024, Gezer et al., 10 May 2025).
• Economic RL and reward structuring. CostNav explicitly designs cost-aware reinforcement learning signals: economic reward formulations reflect revenue, energy cost, and maintenance (collision-induced) cost, e.g., $$reward_t = \delta I\{arrival\} r_{base} - (cost_{energy,t} + cost_{maint,t})$$, and the profit-per-run is central to evaluation (Seong et al., 25 Nov 2025).

4. Economic Lifecycle Modeling, Parameters, and Cost Structures

Micro-navigation economic testbeds implement detailed cost and revenue models:

• CostNav:
  • Fixed costs: $C_{hardware}$ (robot chassis, compute, sensors), $C_{training}$ (data acquisition, collision maintenance during RL training).
  • Variable costs: $C_{energy}$ (measured power draw at commercial rates), $$C_{maintenance}=c_{shock}\times I_{coll}\times p_{coll}\times C_{hardware}$$ (collisions are dominant).
  • Revenue: SLA-bounded per-run delivery revenue, $R = r_{base}\, \eta$, with $r_{base}=\$3.49$$; break-even and time-to-profitability derived from$$\Delta=R-C_{energy}-C_{maintenance}$ and cumulative runs (<a href="/papers/2511.20216" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Seong et al., 25 Nov 2025</a>).</li> </ul></li> <li><strong>Mini-city:</strong> <ul> <li><strong>Component cost breakdown:</strong> Vehicles (≈\$500 each), sensors (≈\$580), infrastructure (up to \$9,500 per smart intersection), city build (≈\$1,080 for shell, \$1,200–1,400 overall). Suggests trade-offs in choice of hardware (e.g., substituting 3D LiDAR for 2D scanners) and scaling strategies (Vargas et al., 2024).
• Agent-based systems:
  • Policy tuning is enabled by parameters such as ALPHA, BETA, MARKUP, tax rates—modulating productivity, consumption, and price dynamics; outputs (short/long-term wealth, utility, GINI) are analyzed under different policy regimes (Furtado et al., 2016).

5. Performance Metrics, Output, and Evaluation Methodologies

Systematic metrics anchor comparison and policy evaluation:

• SEAL: Tracks time series (price index, GDP index, unemployment, GINI), region-level aggregates (commuting, per capita GDP, tax revenue), and agent-level variables (location, money, firm/family IDs). Monthly logging supports inference on utility, inequality, and mobility (Furtado et al., 2016).
• EconWebArena: Core metrics are binary task success (numeric + correct source URL), average step count for completion, and information extraction precision. Composite domain-weighted metrics prohibit dominance by any single economic field. Ablation studies reveal criticality of feature modules (e.g., AXTree access, planning routines) (Liu et al., 9 Jun 2025).
• CostNav: Measures traditional navigation success (arrival rate $\eta$, collisions, energy, path length) and economic metrics (profit/run, cost components, break-even point, SLA compliance). Leaderboards require reporting both types; optimization must address economic viability, not just physical completion (Seong et al., 25 Nov 2025).
• Mini-city & maritime testbeds: Benchmarks for localization error ($e_{loc}$), SLAM performance (KNN, RMSE, IoU), navigation speed, crash/success rates, and intersection safety. Basin marine results interpret RMSE in position/yaw/speed under simulation and real-waves, providing direct simulation-to-hardware error comparisons (Vargas et al., 2024, Gezer et al., 10 May 2025).

Testbed	Primary Metrics	Economic Signals
SEAL	GDP, unemployment, GINI, utility, commute lengths	Tax, policy parameters
EconWebArena	Task success, steps, composite weighted accuracy	None explicit
CostNav	SLA compliance, collisions, profit/run, BEP	Energy/maint./hardware
Mini-city	Map RMSE, crash %, speed, depth MAE	Sensor/infra costs
Marine basin	Waypoint RMSE, trajectory error, repeatability	Vessel/lab costs

6. Implementation and Reproducibility Protocols

Testbeds are designed for transparency and practical deployment:

• SEAL: Requires Python 3.4.4, geospatial and economic datasets, parameter configuration (urban splits, population scaling, random seed). Simulation scripts control single/multi-run, sensitivity analysis, and automated outputs; output format is well-specified (Furtado et al., 2016).
• EconWebArena: Environments are sandboxed; each task includes start URL, answer format, gold value, and curated domain. Agent episodes are capped at 30 steps, observable states include AXTree, screenshot, and element/action metadata. Task seeds and curation details are structured for repeatability (Liu et al., 9 Jun 2025).
• CostNav: Public codebase (https://github.com/worv-ai/CostNav), evaluation on Isaac Lab standardized episodes and seed sets. Economic/reporting protocols strictly prescribe logging, computation of all key signals, and direct cost/reward calculations (Seong et al., 25 Nov 2025).
• Mini-city/marine: Published CAD, configuration, and test scripts. Modular hardware permits scaling and hardware substitution. ROS/Unity-based digital twin architectures support hardware-in-the-loop, batch simulation, and parallel experiment pipelines; standardized logging of all relevant telemetry is central for analysis (Vargas et al., 2024, Gezer et al., 10 May 2025).

7. Prospects, Challenges, and Implications

Micro-navigation economic testbeds are increasingly central to research across robotics, autonomous vehicles, marine GNC, digital economics, and policy simulation. Several themes and open challenges emerge:

• Economic versus physical optimization: Work such as CostNav quantitatively demonstrates the divergence between traditional navigation metrics and commercial viability; minimizing collision-induced maintenance cost is critical to economic success (Seong et al., 25 Nov 2025).
• Fidelity and domain complexity: Live web and physical city platforms expose agents to realistic heterogeneity, challenging the robustness and generalization of algorithms while highlighting environmental uncertainties (flaky web domains, sensor drift) (Vargas et al., 2024, Liu et al., 9 Jun 2025).
• Integration of economic reasoning: Agent-based and RL paradigms integrating multi-level cost/reward signals enable fine-grained analysis of policy or controller design, but require careful model parameterization and data curation.
• Reproducibility and open testbeds: Emphasis on open code/data (e.g., BARN, CostNav) and detailed usage guides supports scientific comparability and field-wide progress.
• Extensibility: Modular implementations (hardware, software, scenario structure) enable the adaptation of core platforms to evaluate new algorithms, sensor suites, or policy innovations across domains (Gezer et al., 10 May 2025).

A plausible implication is that such testbeds will continue to serve as reference platforms, both for benchmarking technical advances (e.g., navigation and control) and for shaping economic policy and deployment decisions. These systems provide the only current method to bridge theoretical advances with practical, economically grounded deployment metrics across diverse navigation domains.