Skip to main content

AI Assistant

Physical AI & Humanoid Robotics

Hello! I'm your AI assistant for the AI-Native Guide to Physical AI & Humanoid Robotics. How can I help you today?

04:57 AM

Chapter 6 — Multi-Agent Robotics, Fleet Coordination & Distributed Intelligence

Overview​

After building a fully autonomous agent, this chapter elevates your system to a multi-robot team. You will design distributed architectures, inter-robot communication strategies, shared mapping systems, and team-level behaviors for collaborative and scalable robotics. Your robots will negotiate roles, synchronize maps, share sensor findings, and solve cooperative missions—preparing you for real-world applications in warehousing, logistics, search/rescue, and collaborative AI.

Duration: Weeks 19–24
Focus: Multi-agent coordination, decentralized planning, communication protocols, collective task execution

Learning Objectives​

Conceptual Understanding​

  • Understand the difference between single-agent and multi-agent intelligence.
  • Analyze architectures for shared memory, map merging, distributed world models.
  • Learn ROS 2/DDS fleet communication protocols and network reliability aspects.
  • Study task allocation: leader-based, market-based, graph partitioning, swarm approaches.
  • Comprehend emergent behavior, failure propagation, and conflict resolution in robot teams.

Practical Skills​

  • Build shared SLAM across multiple robots (parallel mapping and global fusion).
  • Implement inter-agent messaging and real-time policy exchanges.
  • Develop cooperative and swarm missions (search, lift, deliver, patrol, explore).
  • Use and simulate multi-robot fleets in Gazebo, Isaac Sim, and Unity.
  • Deploy task allocation and leader/commander agents with LLM or collaborative planners.

Final Goal Alignment​

  • Robots collaborate naturally, allocating and negotiating tasks.
  • System is ready for warehouse, industry, or multi-bot research deployments.
  • Foundation for lifelong fleet learning and collaborative intelligence.

Chapter Structure​

Chapter 6 is modular, grouped by core elements of multi-agent systems:

Topic 1: Foundations of Multi-Agent Robotics​

  • Single-agent vs. multi-agent: cooperation, competition, coordination.
  • Collaboration models: central coordinator, distributed peer-to-peer, swarm behavior.
  • Communication theory: latency, reliability, real-time messaging, fault-tolerance.

Topic 2: Shared Perception, Mapping & World Models​

  • Multi-robot SLAM, map merging, and conflict handling.
  • Shared sensor fusion: cross-broadcasting, confidence weighting, distributed target recognition.
  • Digital Twin sync: synchronizing scenes and states among agents and in simulation tools.

Topic 3: Task Allocation, Role Assignment & Distributed Planning​

  • Assignment models (auctions, leader election, graph partitioning).
  • Planning as a group: subtasks, negotiation, group fallback protocols.
  • Load balancing, real-time efficiency/trade-offs, dynamic redistribution.

Topic 4: Fleet Communications & Inter-Agent Messaging​

  • ROS 2 DDS networking, topic sharing, quality of service tuning.
  • API-based multi-robot control: REST, WebSocket, MQTT, edge/cloud coordination.
  • Security, identity, and access: robot authentication, channel encryption, role-based access.

Topic 5: Swarm Intelligence, Team Behaviors & LLM-Orchestrated Autonomy​

  • Emergent behaviors: flocking, grid coverage, multi-robot lift/carry.
  • Cooperative missions: retrieval, exploration, patrol, collaborative delivery.
  • Conflict avoidance and traffic: collision, right-of-way, and distributed queueing.
  • LLM- and VLM-orchestrated commander roles; multi-agent self-audit and replay.

Capstone: Multi-Robot Autonomous Fleet Mission​

  • Two or more robots share a map, negotiate and execute coordinated missions issued by a commander agent. Success = fully distributed, collision-free, mission-complete operation.

Use the sidebar to enter each topic for deep dives, design diagrams, and hands-on labs/demos.

Reading Materials​

Primary Resources​

  • ROS 2 Multi-Robot & Fleet Tutorials — Map sharing, coordination protocols, QoS tuning.
  • Swarm Robotics & Multi-Agent Planning — Survey/overview articles, canonical research papers.
  • Leader Election & Distributed Task Allocation — Algorithm textbooks and collaborative robotics case studies.
  • IoT/Cloud Messaging for Robotics — MQTT, DDS, and cloud-edge hybrid strategy docs.

Secondary Resources​

  • Human-Swarm Systems (book, case studies on human/machine team intelligence).
  • Multi-Robot SLAM and Map Fusion — Review and benchmarking articles.
  • Security/Trust in Multi-Agent Systems — Modern advances and pitfalls.

Reference​

  • Official DDS Quality of Service (QoS) and security documentation.
  • API docs for ROS 2 multi-robot communication and parameter remapping.
  • Messaging library docs (MQTT, WebSocket, HTTP REST for robotics).

Technical Requirements​

Software Stack​

  • ROS 2 Humble/Iron with multi-robot DDS configuration.
  • Gazebo/Isaac Sim/Unity (multi-agent scene and digital twin support).
  • Global fleet orchestrator/LLM API (local or cloud-based for task assignment).
  • Secure DDS or messaging (TLS, authentication, role/user management).

Hardware​

  • Access to 2+ robots or simulators (Gazebo or Isaac multi-robot support).
  • Networking gear (wired/wireless LAN or emulated), edge/cloud trial accounts.
  • Optional: Sensors/tagging for peer recognition in real environments.

External Dependencies​

  • Fleet navigation packages, multi-robot SLAM tools, broker/message server.
  • Cloud APIs for distributed planning (optional but recommended for LLM/VLM extensions).

Key Takeaways​

  • You will engineer, deploy, and evaluate a collaborative fleet of autonomous robots.
  • Learn how to synchronize world models, share policies, divide labor, and recover from distributed errors.
  • Capstone skills: moving from single-robot demos to warehouse-scale, field-level, and collaborative AI deployments.

Next Chapter Prerequisites​

  • A tested fleet: two or more robots can share a map and communicate in simulation.
  • Working multi-agent SLAM and world model sync.
  • At least one collaborative mission completed without deadlocks or collisions.
  • Commander/LLM agent in place for distributed task decomposition.
  • Clear understanding of security, coordination, and load-balancing approaches for robot teams.