Multi-Agent Robotics, Fleet Coordination & Distributed Intelligence

For the full course overview and capstone description, see the Physical AI & Humanoid Robotics — Course Specification.

Chapter Overview

Duration: Weeks 19–24
Focus: Inter-robot collaboration, fleet coordination, and distributed decision-making

Chapter 6 expands the scope of the course from a single autonomous robot to teams and fleets of robots that share information, coordinate tasks, and act as a distributed intelligent system. Building on ROS 2 middleware (Chapter 2), digital twins (Chapter 3), perception and mapping (Chapter 4), and single-agent autonomy (Chapter 5), this chapter introduces multi-agent architectures, shared world models, fleet communications, task allocation, and swarm-style cooperation.

By the end of this chapter, you will be able to design and simulate a multi-robot fleet where agents share maps, negotiate tasks, and execute missions in parallel under the direction of a high-level commander (often implemented with an LLM-based agent). This is the bridge from “one capable robot” to scalable, real-world deployments in warehouses, labs, rescue scenarios, and industrial facilities.

Learning Outcomes

Conceptual Understanding

• Understand the differences between single-agent and multi-agent robotics in terms of autonomy, coordination, and complexity
• Learn core multi-agent architectures, including centralized, decentralized, and hybrid coordination schemes
• Grasp how shared maps, world models, and memory can be maintained across multiple robots
• Understand multi-robot SLAM and how map fragments from multiple agents can be merged into a global representation
• Learn the fundamentals of distributed planning and task allocation (leader-based, auction/market-based, and peer-to-peer)
• Understand fleet communication patterns using ROS 2, DDS, and message brokers (MQTT, WebSockets, REST APIs)
• Study the basics of swarm intelligence, emergent behavior, and rule-based coordination (alignment, cohesion, separation)
• Analyze failure modes in fleets: conflict, deadlock, starvation, network partitioning, and how redundancy and fallback strategies mitigate them
• Understand how LLM-based orchestration can sit on top of a multi-agent system to assign roles, interpret missions, and coordinate complex workflows

Practical Skills

• Design and simulate a multi-robot SLAM scenario in Gazebo or Isaac Sim where two or more robots map different regions and share results
• Implement inter-robot messaging using ROS 2 topics/services/actions and QoS profiles appropriate for multi-robot systems
• Build shared perception pipelines where robots exchange detections, poses, or world-state updates to improve team-level awareness
• Implement task allocation strategies (e.g., leader assignment, auction-based bidding, or distance-based heuristics) for multi-robot missions
• Configure fleet communications using DDS domains, namespaces, and/or message brokers (MQTT, REST, WebSockets) across hosts
• Prototype swarm-style behaviors (flocking, formation, coverage, patrol) in simulation and analyze how simple rules yield emergent coordination
• Integrate an LLM “Commander” agent that receives a human mission, decomposes it into subtasks, and assigns those tasks to individual robots
• Implement mission logging and replay to analyze multi-robot executions, identify coordination bottlenecks, and iterate on strategies

Final Goal Alignment

• Transition from one autonomous robot to a coordinated fleet that can share maps, divide work, and act as a unified system
• Establish the conceptual and practical foundation for real-world deployments in logistics, inspection, search-and-rescue, and lab automation
• Prepare for large-scale systems where many robots, humans, and cloud services interact through shared world models and structured protocols

Chapter Structure

This chapter is organized into six topics/modules, each with its own detailed page:

Topic 1: Foundations of Multi-Agent Robotics (Week 19)

Topic 1 contrasts single-agent autonomy (Chapter 5) with multi-agent systems where coordination, communication, and shared objectives become primary design concerns. You will survey central vs decentralized architectures, cooperation vs competition, and how team-level goals are expressed and enforced.

Key concepts include:

• Single vs multi-agent objectives: Independent policies vs shared rewards and constraints
• Collaboration models: Central coordinator, distributed peer-to-peer, and swarm/emergent behavior
• Communication theory in robotics: Latency vs reliability, bandwidth limits, and how DDS underpins ROS 2 multi-robot networking
• Scalability and complexity: Why adding more robots is not just “copy-paste,” and how coordination overhead must be managed

Topic 2: Shared Perception, Mapping & World Models (Weeks 19–20)

Topic 2 focuses on building shared world understanding across multiple robots. Instead of each robot maintaining an isolated map and perception stack, you will design systems where agents contribute local observations to a global world model.

You will explore:

• Multi-robot SLAM: Robots mapping different regions in parallel and merging maps into a consistent global representation
• Shared sensor fusion: Exchanging detections, point clouds, poses, and target hypotheses to improve team awareness
• Conflict handling: Dealing with overlapping scans, inconsistent observations, and partial or stale data
• Digital twins for fleets: Keeping Gazebo/Isaac/Unity scenes synchronized with a global fleet state, so that the digital twin represents all robots and shared objects

By the end of this topic, you will understand how to move from “my robot’s map” to our shared map and how that impacts navigation, planning, and coordination.

Topic 3: Task Allocation, Role Assignment & Distributed Planning (Weeks 20–21)

Topic 3 introduces multi-robot task allocation and distributed planning. Given a mission like “scan this building,” “retrieve all requested items,” or “inspect all machines,” multiple robots must decide who does what, where, and in what order.

You will study:

• Assignment models:
  • Leader-based coordination and role assignment (e.g., scout, carrier, inspector)
  • Auction/market-based task allocation where robots bid based on cost or utility
  • Graph partitioning and region assignment for large maps and coverage tasks
• Planning as a group:
  • Decomposing missions into subtasks with dependencies and shared resources
  • Negotiation and consensus for task ownership and timing
  • Fallback and reallocation when a robot fails or is overloaded
• Load balancing & efficiency:
  • Using distance, energy, and time metrics to allocate work fairly
  • Dynamic redistribution when robots complete tasks early or encounter delays

This topic connects high-level mission design with concrete distributed execution strategies that can be implemented on top of ROS 2 and your simulation environments.

Topic 4: Fleet Communications & Inter-Agent Messaging (Weeks 21–22)

Topic 4 dives into the networking layer that makes multi-agent systems practical. You will examine how ROS 2’s DDS transport scales across machines and networks, and how to mix ROS 2 with web and cloud APIs for remote fleet management.

You will cover:

• ROS 2 DDS networking for fleets:
  • Multi-robot topic sharing, namespaces, and domain IDs
  • QoS tuning (reliability, durability, history, deadline, liveliness) for high-throughput sensor data vs mission-critical commands
  • Services and actions across hosts for requesting help, handing off tasks, or querying fleet state
• API-based multi-robot control:
  • REST and WebSocket gateways for remote dashboards and monitoring
  • MQTT and message brokers for cloud-edge coordination, alerts, and telemetry
  • Edge vs cloud routing decisions: what stays local vs what can be offloaded
• Security & identity:
  • Robot authentication, identity, and access control within a fleet
  • Encryption and secure channels to prevent spoofing, hijacking, or data leakage
  • Role-based access per robot and per mission (e.g., Scout, Carrier, Worker, Observer)

By the end of this topic, you will know how to put multiple robots on the same network, keep their communication reliable and secure, and expose fleet capabilities to external systems.

Topic 5: Swarm Intelligence, Cooperation & Team Behaviors (Weeks 22–23)

Topic 5 introduces swarm and collective behavior inspired by biology (flocks, schools, colonies) and often used in drone swarms and warehouse fleets. The emphasis is on how simple local rules can lead to complex, coordinated global patterns without centralized control.

You will explore:

• Emergent behavior basics:
  • Alignment, cohesion, and separation as building blocks for flocking
  • Formation control and shape-keeping for multi-robot teams
  • Trade-offs between local rules and global guarantees (e.g., coverage, connectivity)
• Cooperative missions:
  • Multi-robot warehouse item retrieval and order fulfillment
  • Search-and-rescue exploration and victim/location marking
  • Two-robot lift-and-carry scenarios that require force and pose coordination
  • Patrol and coverage of grids, corridors, or open spaces
• Conflict avoidance & traffic management:
  • Collision-free route planning in dense robot traffic
  • Right-of-way rules, virtual traffic lights, and lanes
  • Comparing swarm-style distributed rules with centralized queueing or scheduling

You will implement and analyze at least one swarm-style demo in simulation, experimenting with parameters and observing how team behavior changes.

Topic 6: LLM-Orchestrated Multi-Agent Autonomy (Weeks 23–24)

Topic 6 layers LLM-based orchestration on top of your multi-robot stack. Instead of hard-coding all mission logic, you will explore how a Commander agent can interpret human instructions, query fleet state, and assign tasks dynamically.

You will cover:

• High-level Commander agent:
  • Receiving natural-language missions from humans (“Inventory aisle 3”, “Search zone B for a missing tool”)
  • Decomposing missions into structured tasks and constraints
  • Assigning subtasks to individual robots based on capabilities, location, and current workload
• Inter-robot dialogue:
  • Designing message formats for robots to request help, transfer tasks, or negotiate conflicts
  • Enabling robots to coordinate to find missing objects or resolve blocked paths
  • Handling negotiation outcomes when robots disagree or when resources are limited
• Mission replay and self-audit:
  • Logging missions, decisions, and outcomes for fleet-level analysis
  • Reviewing timelines to understand where delays, conflicts, or failures occurred
  • Using logs to refine prompts, policies, task allocation strategies, and safety constraints

This topic connects the agentic LLM stack from Chapter 5 with multi-agent robotics concepts so that high-level reasoning can coordinate many robots at once.

Prerequisites

Before starting this chapter, you should have:

• Completed Chapters 1–5:
  • Foundations of Physical AI
  • ROS 2 middleware, nodes, topics, services, and actions
  • Digital twins and simulation (Gazebo/Isaac/Unity)
  • Perception, mapping, and multimodal understanding
  • Single-agent autonomy, planning, and LLM-guided decision-making
• A working simulation environment with at least one autonomous robot running:
  • Gazebo or Isaac Sim world connected to ROS 2
  • Perception and SLAM pipeline from Chapter 4
  • Navigation and task execution pipelines from Chapter 5
• Basic understanding of:
  • Networking concepts (hosts, ports, IPs, bandwidth, latency)
  • ROS 2 networking (DDS domains, namespaces, and QoS at a basic level)
  • LLM-based agents and prompt design from previous chapters

Hardware access to two or more robots (or mobile bases) is ideal but not required—most concepts can be developed and validated in simulation first.

Technical Requirements

Software Stack

• ROS 2 Humble or Iron (Ubuntu 22.04 LTS)
• Gazebo and/or Isaac Sim for multi-robot simulation
• Unity (optional) for high-fidelity visualization and fleet dashboards
• DDS-based ROS 2 networking with properly configured domains and namespaces
• ROS 2 Nav2 or equivalent navigation stack for each robot
• Message broker or API layer (e.g., MQTT, REST/GraphQL, WebSockets) for fleet-level integration
• LLM/VLM stack (cloud API or local deployment) for Commander and coordination agents

Hardware

• Linux workstation(s) with sufficient CPU and RAM to simulate multiple robots concurrently
• At least one GPU (RTX-class recommended) if running intensive perception or LLM workloads locally
• Optional:
  • Two or more mobile robots or humanoid platforms with ROS 2 support
  • Onboard compute (Jetson-class or similar) and Wi‑Fi/Ethernet connectivity
  • Lab or warehouse-style environment with clear safety procedures for multi-robot testing

External Dependencies

• ROS 2 multi-robot networking tutorials and examples
• DDS vendor documentation (Cyclone DDS, Fast DDS, etc.) for QoS tuning and discovery behavior
• Nav2, multi-robot SLAM, and multi-robot navigation packages where available
• Message broker (e.g., Mosquitto for MQTT) or API gateway for remote fleet control
• LLM platform access (e.g., OpenAI, local LLMs, or equivalent) with tooling to integrate into ROS 2 or your orchestration layer

Reading Materials

Primary Resources

• ROS 2 multi-robot and DDS networking documentation
• Research and tutorials on multi-robot SLAM and distributed mapping
• Nav2 and multi-robot navigation examples
• Foundational texts and tutorials on swarm robotics and distributed AI

Secondary Resources

• Research papers on auction-based and market-based task allocation
• Case studies of warehouse robotics, inspection fleets, and drone swarms
• Articles on integrating ROS 2 with MQTT/message brokers and cloud services
• Tutorials on LLM-orchestrated multi-agent systems and tool-using agents

Reference Materials

• ROS 2 QoS configuration guides for multi-robot deployments
• DDS configuration and troubleshooting references (discovery, reliability, and partitions)
• Example multi-robot simulation worlds and launch files
• Templates for mission logs, replay tools, and fleet dashboards

Common Mistakes to Avoid

Mistake: Treating a multi-robot system as “N copies of a single robot.” Result: Coordination failures, duplicated work, and resource conflicts.
Prevention: Design explicit coordination mechanisms (task allocation, shared world models, communication protocols) rather than assuming independent agents will “figure it out.”

Mistake: Ignoring network and QoS constraints. Result: Dropped messages, stale data, and unsafe behavior under load.
Prevention: Measure bandwidth and latency, choose QoS profiles per topic, and test worst-case loads in simulation before deploying to hardware.

Mistake: Centralizing everything on a single “brain” node. Result: Single point of failure and poor scalability.
Prevention: Use a hybrid architecture where high-level coordination is centralized when helpful, but perception, control, and local safety remain distributed on each robot.

Mistake: Neglecting identity, authentication, and access control. Result: Difficulty auditing which robot did what, and increased attack surface.
Prevention: Assign stable identities to robots, use authenticated and encrypted channels, and define role-based permissions for commands and data.

Mistake: Over-trusting LLM outputs for mission orchestration. Result: Plans that look reasonable in text but are infeasible or unsafe in the real environment.
Prevention: Treat LLMs as planners or advisors whose outputs must be checked against maps, kinematics, safety constraints, and resource availability before execution.

Chapter Summary

Duration: 6 weeks (Weeks 19–24)
Topics/Modules: 6
Major Milestone: Multi-Robot Autonomous Fleet Mission
Total Estimated Reading: 140–180 pages
Total Estimated Coding & Simulation: 40–70 hours

Key Takeaways

• Multi-agent robotics extends single-robot autonomy to teams and fleets that share perception, maps, and tasks
• Robust fleet systems require careful design of architecture, communication, task allocation, and safety
• Swarm and distributed coordination techniques allow scalable, resilient behavior under uncertainty and partial failures
• LLM-based Commander agents can greatly simplify mission design and coordination, but must operate within well-defined, safety-aware frameworks

Next Chapter Prerequisites

By the end of Chapter 6, you should have:

• A simulated multi-robot fleet capable of sharing maps and executing coordinated missions
• Working examples of inter-robot messaging, basic task allocation, and at least one swarm-style behavior
• An LLM-driven Commander that can translate human missions into multi-robot task plans

These capabilities position your system for future extensions such as large-scale deployment, human–fleet interaction, and advanced safety and reliability engineering.