System V 2.5 Online

ADVANCED
RESCUE SYSTEMS

Optimizing critical response through Agent-Based Intelligence.

Deploy autonomous agent simulations to analyze terrain, predict target probability, and optimize resource allocation in real-time complex environments.

LAUNCH SIMULATOR LEARN MORE
/// SYSTEM_MONITOR_ACTIVE
FPS: 60.0
Target Probability
Agents Deployed
1,024
> Init_Sequence_Alpha... OK
> Connecting to satlink_04... OK
> Terrain_Mesh loaded (Gunung Ledang)
> Calculating optimal paths...

Agent Logic

Decentralized Decision Making

Swarm Net

Inter-Agent Communication

Terrain GIS

Real-world Topography

Analytics

Coverage & Detection Rates

Core Technology

What is ABM?

Agent-Based Modeling (ABM) simulates the simultaneous actions and interactions of multiple autonomous agents to recreate complex systems.

In our Search & Rescue context, agents represent individual searchers (humans, drones, K9s) operating under specific heuristic rules. By running these simulations thousands of times, we can observe emergent patterns that static mathematical models miss.

Predict "Missing Person" behavior
Optimize search paths in rugged terrain
Minimize resource overlap

Operational Modules

Select Simulation Protocol

Access specific simulation environments ranging from stochastic baselines to real-world geospatial deployments.

Stochastic Grid
MOD_01

Stochastic Search Dynamics

Base simulation utilizing Random Walk algorithms to establish baseline efficiency metrics. Useful for understanding coverage capabilities in unmapped, communication-denied environments without prior intelligence.

Initialize
Swarm Drones
MOD_02

Swarm Intelligence

Advanced multi-agent system featuring inter-agent communication. Simulates drone swarms sharing coverage maps to minimize search overlap and maximize area scanning speed through cooperative logic.

Initialize
Heatmap Data
MOD_03

Heuristic Probability

Implements Bayesian Search Theory. Agents utilize "Last Known Point" (LKP) data and missing person profiles to prioritize high-probability sectors, optimizing Time-to-Find (TTF) rates.

Initialize
Topography Map
MOD_04

Geospatial: Gunung Ledang

Full-scale deployment simulation on Real GIS Data of Gunung Ledang. Accounts for actual elevation, terrain difficulty, and natural barriers to simulate realistic rescue operations in mountain environments.

Initialize

Why ABM?

Strategic Advantages

Reduced Response Time

Pre-calculate optimal search patterns to significantly reduce "Time to Find" (TTF) when every second counts.

Zero-Risk Training

Train commanders in a virtual sandbox. Test risky strategies without endangering personnel or incurring helicopter fuel costs.

Evidence-Based Logic

Move beyond intuition. Use statistical coverage data and probability heatmaps to justify resource allocation.