457  UAV Swarm Formation and Multi-UAV Coordination

457.1 Learning Objectives

By the end of this chapter, you will be able to:

• Coordinate Multi-UAV Systems: Design cooperative flight patterns for distributed coverage
• Implement Formation Control: Apply Reynolds’ flocking rules for swarm coordination
• Select Formation Patterns: Choose appropriate formations for different mission types
• Design Hierarchical Architectures: Balance centralized coordination with distributed autonomy

457.2 Prerequisites

Before diving into this chapter, you should be familiar with:

• UAV Trajectory Control: Understanding trajectory optimization strategies provides the foundation for coordinating multiple UAV trajectories simultaneously
• UAV Energy-Aware Mission Planning: Knowledge of energy constraints and mission range helps in designing feasible swarm operations

457.3 Swarm Formation Control

⏱️ ~10 min | ⭐⭐⭐ Advanced | 📋 P05.C24.U03

NoteWorked Example: Swarm Coordination for Wildfire Perimeter Mapping

Scenario: A wildfire is spreading across a 5 km × 5 km forested area. Your team deploys a 6-UAV swarm to continuously monitor the fire perimeter. As the fire grows, UAVs must dynamically reposition to maintain coverage without collisions.

Given: - Swarm size: 6 UAVs - Fire perimeter (initial): Approximately circular, 8 km circumference - Camera FOV coverage: Each UAV covers 400 m of perimeter from 150 m altitude - Required overlap: 10% between adjacent UAV coverage zones - UAV cruise speed: 12 m/s - Minimum separation: 100 m (collision avoidance) - Fire spread rate: 0.5 km/hour (perimeter grows ~1.6 km/hour)

Steps: 1. Calculate effective coverage per UAV: 400 m × 0.9 (accounting for overlap) = 360 m effective coverage 2. Verify initial coverage: 6 UAVs × 360 m = 2,160 m. But perimeter = 8,000 m. Gap: 5,840 m uncovered! 3. Redesign: Use patrol pattern instead of static positions: Each UAV patrols a 1,333 m sector (8,000 m / 6). At 12 m/s, one pass = 111 seconds. With 400 m FOV, update interval = ~2 minutes per location. 4. Calculate formation spacing: 8,000 m perimeter / 6 UAVs = 1,333 m apart along perimeter path. At 150 m altitude, horizontal separation is always >100 m (safe). 5. Implement consensus-based repositioning: As fire grows, each UAV adjusts patrol sector. After 1 hour: New perimeter = 8,000 + 1,600 = 9,600 m. New sector = 9,600 / 6 = 1,600 m per UAV. 6. Verify patrol update rate after growth: 1,600 m / 12 m/s = 133 seconds = ~2.2 minutes per location (still acceptable)

Result: The 6-UAV swarm maintains continuous perimeter monitoring using coordinated patrol patterns. Each UAV covers a 1,333-1,600 m sector with ~2-minute revisit times. Consensus algorithms automatically expand sectors as the fire grows, keeping all UAVs evenly distributed without central coordination.

Key Insight: For large perimeters, static positioning fails because total coverage << perimeter length. Patrol-based coordination with consensus algorithms enables a small swarm to monitor large areas by trading instantaneous full coverage for frequent revisit patterns. The 2-minute update interval is acceptable for fire monitoring where changes occur over hours, not seconds.

457.3.1 Formation Patterns

Different missions require different formations:

Formation	Use Case	Advantages	Challenges
Line	Pipeline inspection	Single file, easy control	Limited coverage width
Column	Convoy escort	Good forward visibility	Vulnerable from sides
Wedge (V)	Search patterns	Wide coverage, aerodynamic	Complex coordination
Circle	Perimeter monitoring	360° coverage	Requires precise spacing
Grid	Area mapping	Uniform coverage	Many UAVs needed
Random	Search & rescue	Covers unexpected areas	Risk of overlap/gaps

457.3.2 Formation Geometry

%% fig-alt: "Four UAV formation types illustrated: Line formation shows 4 UAVs in single file connected sequentially; V-formation shows leader UAV at apex with 4 followers in wedge pattern; Grid formation shows 4 UAVs in 2×2 square grid pattern; Circle formation shows 4 UAVs equally spaced around a center point with radius lines"
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#E67E22', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#fff', 'nodeTextColor': '#2C3E50'}}}%%
graph TB
    subgraph "Formation Types"
        subgraph "Line Formation"
            L1["UAV1"] --- L2["UAV2"] --- L3["UAV3"] --- L4["UAV4"]
        end

        subgraph "V-Formation (Wedge)"
            V1["UAV1<br/>(Leader)"]
            V2["UAV2"]
            V3["UAV3"]
            V4["UAV4"]
            V5["UAV5"]

            V1 --- V2
            V1 --- V3
            V2 --- V4
            V3 --- V5
        end

        subgraph "Grid Formation"
            G1["UAV1"] --- G2["UAV2"]
            G3["UAV3"] --- G4["UAV4"]
            G1 --- G3
            G2 --- G4
        end

        subgraph "Circle Formation"
            C1["UAV1"]
            C2["UAV2"]
            C3["UAV3"]
            C4["UAV4"]

            C1 -.->|Radius| Center((Center))
            C2 -.->|Radius| Center
            C3 -.->|Radius| Center
            C4 -.->|Radius| Center
        end
    end

    style V1 fill:#2C3E50,stroke:#16A085,color:#fff
    style L1 fill:#E67E22,stroke:#2C3E50,color:#fff
    style G1 fill:#16A085,stroke:#2C3E50,color:#fff
    style Center fill:#2C3E50,stroke:#16A085,color:#fff

Figure 457.1: Four UAV formation types: Line (sequential), V-Formation (leader with followers), Grid (2D array), Circle (equidistant around center).

457.3.3 Formation Maintenance Rules

UAVs in a formation follow three key behaviors based on Reynolds’ flocking rules:

%% fig-alt: "Formation maintenance flowchart based on Reynolds' flocking rules: Individual UAV senses neighbor positions and velocities, applies three parallel rules (Separation to avoid crowding generating repulsion force when too close, Alignment to match velocity adjusting speed and direction, Cohesion to stay with group moving toward group center), combines forces using weighted sum, computes new velocity vector, executes movement, and continuously loops back to sensing"
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#E67E22', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#fff', 'nodeTextColor': '#2C3E50'}}}%%
graph TB
    subgraph "Formation Maintenance (Reynolds' Flocking Rules)"
        UAV["Individual UAV"]

        Sense["Sense Neighbors<br/>(Position, Velocity)"]

        Sep["Separation Rule<br/>(Avoid crowding)"]
        Align["Alignment Rule<br/>(Match velocity)"]
        Coh["Cohesion Rule<br/>(Stay with group)"]

        SepForce["Repulsion Force<br/>(if too close:<br/>move away)"]
        AlignForce["Velocity Matching<br/>(adjust speed/<br/>direction)"]
        CohForce["Attraction Force<br/>(move toward<br/>group center)"]

        Combine["Combine Forces<br/>(Weighted sum)"]

        NewVelocity["Compute New<br/>Velocity Vector"]

        Execute["Execute Movement"]
    end

    UAV --> Sense
    Sense --> Sep
    Sense --> Align
    Sense --> Coh

    Sep --> SepForce
    Align --> AlignForce
    Coh --> CohForce

    SepForce --> Combine
    AlignForce --> Combine
    CohForce --> Combine

    Combine --> NewVelocity
    NewVelocity --> Execute
    Execute -.->|Continuous| Sense

    style UAV fill:#2C3E50,stroke:#16A085,color:#fff
    style Combine fill:#E67E22,stroke:#2C3E50,color:#fff
    style Execute fill:#16A085,stroke:#2C3E50,color:#fff

Figure 457.2: Formation maintenance flowchart based on Reynolds’ flocking rules: Separation, Alignment, and Cohesion forces combine to produce coordinated swarm behavior.

Separation: Prevents collisions; each UAV maintains minimum distance from neighbors

Alignment: UAVs match velocity vectors of nearby UAVs; keeps formation moving together

Cohesion: UAVs steer toward the average position of neighbors; prevents stragglers

457.4 Multi-UAV Coordination Architectures

⏱️ ~8 min | ⭐⭐⭐ Advanced | 📋 P05.C24.U05

457.4.1 Centralized vs Distributed

%% fig-alt: "Comparison of two UAV coordination architectures: Centralized coordination shows Ground Control Station as central authority sending commands to and receiving telemetry from 4 UAVs in star topology with no direct UAV-to-UAV communication; Distributed coordination shows 4 autonomous UAVs communicating peer-to-peer in mesh topology with bidirectional links between all neighbors for decentralized decision making"
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#E67E22', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#fff', 'nodeTextColor': '#2C3E50'}}}%%
graph TB
    subgraph "Centralized Coordination"
        GCS1["Ground Control<br/>Station<br/>(Central Authority)"]

        U1["UAV 1"]
        U2["UAV 2"]
        U3["UAV 3"]
        U4["UAV 4"]

        GCS1 -.->|Command| U1
        GCS1 -.->|Command| U2
        GCS1 -.->|Command| U3
        GCS1 -.->|Command| U4

        U1 -.->|Telemetry| GCS1
        U2 -.->|Telemetry| GCS1
        U3 -.->|Telemetry| GCS1
        U4 -.->|Telemetry| GCS1
    end

    subgraph "Distributed Coordination"
        D1["UAV 1<br/>(Autonomous)"]
        D2["UAV 2<br/>(Autonomous)"]
        D3["UAV 3<br/>(Autonomous)"]
        D4["UAV 4<br/>(Autonomous)"]

        D1 <-->|Peer-to-Peer| D2
        D2 <-->|Peer-to-Peer| D3
        D3 <-->|Peer-to-Peer| D4
        D1 <-->|Peer-to-Peer| D3
        D2 <-->|Peer-to-Peer| D4
    end

    style GCS1 fill:#2C3E50,stroke:#16A085,color:#fff
    style D1 fill:#E67E22,stroke:#2C3E50,color:#fff
    style D2 fill:#E67E22,stroke:#2C3E50,color:#fff

Figure 457.3: Comparison of centralized (star topology with GCS) vs distributed (mesh topology with peer-to-peer) coordination architectures.

457.4.2 Architecture Comparison

Aspect	Centralized	Distributed	Hybrid
Decision Quality	Optimal (global view)	Suboptimal	Good
Latency	High (round-trip)	Low (local)	Medium
Fault Tolerance	Poor (single point)	Excellent	Good
Scalability	Limited (100s)	Good (1000s)	Good
Bandwidth	High (all to GCS)	Medium (local)	Medium
Best For	Safety-critical	Large swarms	Most missions

457.4.3 Hierarchical Coordination

For large swarms, hierarchical approaches work best:

%% fig-alt: "Hierarchical swarm coordination with three levels: Ground Control Station at strategic level assigning zones to cluster leaders; three cluster leaders managing local UAVs and coordinating inter-cluster; Cluster 1 with leader managing 3 UAVs with local peer-to-peer coordination, Cluster 2 with leader managing 3 UAVs, Cluster 3 with leader managing 2 UAVs, enabling scalability by combining centralized strategic planning with distributed tactical execution"
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#E67E22', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#fff', 'nodeTextColor': '#2C3E50'}}}%%
graph TB
    subgraph "Hierarchical Swarm Coordination"
        GCS["Ground Control<br/>Station<br/>(Strategic Level)"]

        CL1["Cluster Leader 1"]
        CL2["Cluster Leader 2"]
        CL3["Cluster Leader 3"]

        GCS -->|Zone Assignment| CL1
        GCS -->|Zone Assignment| CL2
        GCS -->|Zone Assignment| CL3

        subgraph "Cluster 1"
            CL1 --> U1A["UAV 1A"]
            CL1 --> U1B["UAV 1B"]
            CL1 --> U1C["UAV 1C"]

            U1A <-.->|Local Coord| U1B
            U1B <-.->|Local Coord| U1C
        end

        subgraph "Cluster 2"
            CL2 --> U2A["UAV 2A"]
            CL2 --> U2B["UAV 2B"]
            CL2 --> U2C["UAV 2C"]

            U2A <-.->|Local Coord| U2B
            U2B <-.->|Local Coord| U2C
        end

        subgraph "Cluster 3"
            CL3 --> U3A["UAV 3A"]
            CL3 --> U3B["UAV 3B"]
        end

        CL1 <-.->|Inter-Cluster| CL2
        CL2 <-.->|Inter-Cluster| CL3
    end

    style GCS fill:#2C3E50,stroke:#16A085,color:#fff
    style CL1 fill:#E67E22,stroke:#2C3E50,color:#fff
    style CL2 fill:#E67E22,stroke:#2C3E50,color:#fff
    style CL3 fill:#E67E22,stroke:#2C3E50,color:#fff

Figure 457.4: Hierarchical swarm coordination: GCS assigns zones to cluster leaders, who coordinate local UAVs autonomously.

NoteWorked Example: Hierarchical Swarm Task Allocation for Infrastructure Inspection

Scenario: A utility company deploys 15 UAVs to inspect a 50 km power transmission line corridor. The corridor has 200 transmission towers that need visual inspection. You must design a hierarchical coordination strategy that divides the swarm into clusters and allocates towers efficiently.

Given: - 15 UAVs total - 200 towers across 50 km corridor (average 4 towers/km) - UAV inspection time per tower: 3 minutes (hover + photograph) - UAV flight speed: 10 m/s between towers - UAV battery: 35 minutes flight time - Communication range: UAV-to-UAV = 2 km, UAV-to-GCS = 5 km - GCS location: Corridor midpoint (km 25)

Steps: 1. Design cluster structure: Divide 50 km corridor into 5 zones (10 km each). Assign 3 UAVs per zone (15/5 = 3). Each cluster has 1 leader + 2 workers. 2. Calculate towers per UAV: 200 towers / 15 UAVs = 13.3 towers per UAV 3. Calculate inspection time per UAV: 13 towers × 3 min = 39 minutes. Exceeds 35 min battery! 4. Revise: Add transit time: Average tower spacing = 50 km / 200 = 250 m. Transit between 13 towers = 12 × 250 m = 3 km. Transit time = 3000 m / 10 m/s = 5 min. Total = 39 + 5 = 44 minutes >> 35 min battery 5. Solution: Multiple sorties per UAV: Each UAV can inspect 35 min / (3 + 0.4) min per tower = 10 towers per sortie. Need 200 / (15 × 10) = 1.33 sorties → 2 sorties per UAV 6. Optimize cluster leader placement: Leaders positioned at zone center (km 5, 15, 25, 35, 45). Leader at km 25 communicates directly with GCS. Other leaders relay through adjacent leaders (max 2 hops to GCS). 7. Calculate total mission time: Sortie 1: 35 min flight + 15 min battery swap = 50 min. Sortie 2: 35 min flight. Total: 85 minutes for complete corridor inspection 8. Verify communication: Cluster workers within 2 km of leader (10 km zone, workers at ±3 km from center = within range). Leaders spaced 10 km apart, relay through overlapping ranges at zone boundaries.

Result: The 15-UAV swarm completes 200 tower inspections in 85 minutes using 2 sorties. Hierarchical organization (5 clusters of 3 UAVs) enables local coordination without overwhelming the GCS. Each cluster leader aggregates inspection data from workers and relays to GCS through the leader network.

Key Insight: Hierarchical swarm architectures scale better than flat organizations. With 15 UAVs reporting directly to GCS, communication bottleneck limits throughput. With cluster leaders, GCS manages only 5 connections while each leader manages 2 workers locally. This reduces GCS bandwidth by 67% while enabling parallel inspection across all zones. The key design parameter is cluster size - too small (2 UAVs) wastes leader overhead, too large (10 UAVs) overloads cluster coordination.

457.5 Summary

This chapter covered swarm formation control and multi-UAV coordination:

• Formation Patterns: Different missions require different formations (line, wedge, grid, circle) with trade-offs between coverage, coordination complexity, and communication requirements
• Reynolds’ Flocking Rules: Separation (collision avoidance), Alignment (velocity matching), and Cohesion (group attraction) provide the foundation for distributed swarm behavior
• Coordination Architectures: Centralized approaches offer optimal decisions but poor fault tolerance; distributed approaches scale well but produce suboptimal solutions; hybrid hierarchical approaches balance both
• Hierarchical Design: Cluster-based organization with leaders reduces GCS communication burden while enabling local autonomous coordination within clusters

457.6 What’s Next

Having mastered swarm coordination, the next chapter covers collision avoidance systems, mission types, and hands-on lab activities for UAV trajectory planning.

Continue to UAV Missions, Collision Avoidance, and Labs →

---