Multi-Agent Robotics

Self-organizing robot fleets that outperform centralized systems through collective intelligence.

Solution Overview

Simulation-Derived Cooperation

Robots trained in simulation environments to implicitly cooperate without explicit algorithmic instructions. Learning occurs collectively but execution happens individually without communication.

Predictive Path Resolution

Probabilistic models infer the behavior of nearby agents without communication channels. Lookahead algorithms resolve potential conflicts through localized inference mechanisms.

Emergent Coordination

Uniform decision policies applied to local perceptions generate emergent system-wide behaviors. Global efficiency patterns arise without explicit programming, increasing throughput beyond centralized coordination systems.

Technical Specifications

Fleet Density: 20.0 robots/1000 bins
  • Real-time Control Loop: 150Hz refresh rate with 7ms response latency
  • Pathfinding Computation Efficiency: 1.2ms average path recalculation time per agent
  • Dynamic Rerouting Performance: 18ms maximum latency for conflict resolution
Benchmarked on single-core 480 MHz ARM IPC

Performance Advantages

Less Driving

38%* reduction in travel distance

Higher MTBF

75%* increase in mean time between failures

Fewer Turns

52%* reduction in direction changes

Higher Throughput

74%* increase in system throughput

* evaluated against heuristic-based centralised search methods