43  UAV Missions & Avoidance

In 60 Seconds

UAV missions follow six standard trajectory patterns: lawnmower (area survey, 80% overlap), racetrack (linear inspection), spiral (expanding search), and three others optimized for specific tasks. Collision avoidance requires minimum separation distances of 5-10m within a swarm and 150m+ from manned aircraft, with Detect-and-Avoid (DAA) pipelines processing sensor fusion within a 2-second decision budget. Coverage flight planning computes altitude from camera FOV and desired ground resolution – doubling altitude quadruples coverage area but halves spatial resolution.

43.1 Learning Objectives

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

• Design Collision Avoidance Systems: Architect Detect and Avoid (DAA) pipelines that combine sensor fusion, trajectory prediction, and avoidance maneuvers within a defined time budget
• Calculate Separation Requirements: Evaluate minimum safe distances for 5 UAV encounter scenarios (same-swarm, multi-operator, manned aircraft, obstacle, ground) and select appropriate sensing methods
• Analyze Mission Trajectory Patterns: Compare 6 mission profiles (lawnmower, racetrack, spiral, expanding square, point-to-point, station-keeping) and match each to its operational requirements
• Compute Coverage Flight Parameters: Calculate altitude, track spacing, number of passes, total distance, and flight time for a survey mission given camera FOV, resolution, and overlap constraints
• Evaluate Mission Feasibility: Determine whether a planned UAV mission fits within battery, airspace, and safety constraints using quantitative time and energy budgets

Minimum Viable Understanding

Before proceeding, ensure you grasp these three essentials:

• Time budget drives collision avoidance: A DAA system must detect threats at a range that provides enough time (detection range / closing speed) minus processing latency (typically 1-3 seconds) to execute an avoidance maneuver before the closest point of approach (CPA) drops below 50 m separation.
• Track spacing = footprint x (1 - overlap): For area survey missions, a camera with 144 m ground footprint at 80 m altitude and 20% side overlap yields 115 m track spacing – the single most important parameter for coverage mission planning.
• Battery reserve must exceed 30%: A feasible UAV mission consumes no more than 70% of available flight time, leaving at least 30% reserve for wind, return-to-home, and contingencies on a typical 30-minute multirotor platform.

Sensor Squad: UAV Mission Adventures

Max (motion) zooms around like a little drone: “I love planning flight paths! When a UAV needs to survey a big field, it flies back and forth in straight lines – like mowing a lawn. I figure out how fast to go and how far apart each line should be so nothing gets missed!”

Lila (light) points her camera downward: “I am the UAV’s eye! From 80 meters up, my camera can see a strip of ground 144 meters wide. But we overlap each strip by 20% so there are no gaps in the photos. That means the drone moves over 115 meters between each pass.”

Sammy (sound) listens carefully in all directions: “My job is collision avoidance! I detect other aircraft using radar and special radio signals called ADS-B. If something is getting too close, I shout a warning so the drone can climb or turn away before they get within 50 meters of each other.”

Bella (bio/button) checks the battery gauge: “Before every mission I ask: do we have enough battery? If the flight takes 16 minutes and the battery lasts 30 minutes, we have 14 minutes left over – that is our safety reserve. I always make sure we keep at least 30% in the tank!”

For Beginners: How Drones Plan Their Flights

Think of a UAV mission like planning a road trip with a limited fuel tank. You need to know three things: where you are going, how long the trip takes, and whether you have enough fuel to get back.

Collision avoidance is like defensive driving. The drone has sensors (cameras, radar) that watch for other aircraft. If something gets too close, the drone automatically climbs, turns, or slows down. The key question is always: “Do I have enough time to react?” If the drone spots a threat 400 meters away and both are flying toward each other at a combined 25 meters per second, the drone has only 16 seconds to decide what to do and move out of the way.

Mission patterns are the routes the drone flies. For photographing a field, the drone flies back and forth in parallel lines (like mowing a lawn). For patrolling a fence, it flies in circles. For searching for a lost hiker, it spirals outward from the last known location. Each pattern is designed to cover the area efficiently.

Coverage planning uses simple math. You figure out how wide a strip the camera can photograph at a given altitude, add some overlap between strips so nothing is missed, then count how many strips you need and add up the total distance. If the total flight time fits inside the battery life with room to spare, the mission is feasible.

43.2 Prerequisites

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

• UAV Swarm Formation Control: Understanding formation patterns and multi-UAV coordination provides context for collision avoidance requirements
• UAV Energy-Aware Mission Planning: Knowledge of energy constraints and mission range helps in designing feasible mission profiles

Key Concepts

• Collision Avoidance Time Budget: The minimum reaction time needed to avoid collision = detection time + decision time + maneuver time — for UAVs at 15 m/s, the entire budget is 2–3 seconds, requiring onboard autonomous avoidance, not remote operator response
• Mission Trajectory Patterns: Standard coverage path shapes — lawnmower (parallel strips), spiral (concentric), grid (overlapping), and custom (following linear features like roads) — selected based on sensor footprint and overlap requirements
• Coverage Flight Planning: Calculating waypoint sequences that guarantee minimum overlap (typically 60–80% for photogrammetry, 20–30% for sensor coverage) while minimizing total path length
• Detect-and-Avoid (DAA): The regulatory requirement that UAVs detect and avoid other aircraft without relying on see-and-avoid (pilot vision) — requires ADS-B, radar, or computer vision sensors
• Mission Replanning: Dynamically adjusting the mission trajectory in response to battery depletion warnings, obstacle detection, or communication loss — requires onboard decision logic, not just waypoint following
• GSD (Ground Sample Distance): The spatial resolution of sensor data — at 100m altitude with a 12 MP camera, GSD ≈ 2.7 cm/pixel; doubling altitude doubles GSD (halves resolution)
• Obstacle Map: A 3D representation of known obstacles (buildings, trees, power lines) used for trajectory planning — must be updated from real-time sensor data during flight in dynamic environments

43.3 UAV Mission Architecture Overview

The following diagram illustrates the end-to-end UAV mission pipeline, from collision avoidance through mission pattern selection to coverage verification.

43.4 Collision Avoidance

43.4.1 Detect and Avoid (DAA) System

Figure 43.1: Detect and Avoid (DAA) system flowchart showing sensor input, trajectory prediction, risk assessment, and avoidance maneuver execution.

Worked Example: Collision Avoidance Time Budget for Converging UAVs

Scenario: Two UAVs from different operators are flying in the same airspace. UAV-A detects UAV-B approaching on a collision course. You need to calculate the time available for detect-and-avoid (DAA) response and determine if the current separation standards are adequate.

Given:

• UAV-A velocity: 15 m/s heading East
• UAV-B velocity: 20 m/s heading North
• Initial positions: UAV-A at (0, 0, 100m), UAV-B at (500m, -600m, 105m)
• Minimum safe separation: 50 m
• Sensor detection range: 400 m
• DAA system latency: 2 seconds (detection + processing)
• Avoidance maneuver: 5 m/s vertical climb

Steps:

1. Calculate closing geometry: UAV-A moves (+15, 0, 0) m/s, UAV-B moves (0, +20, 0) m/s. Relative velocity = UAV-B relative to UAV-A = (-15, +20, +0.167) m/s (including altitude convergence of 5m over ~30 seconds)
2. Calculate initial 3D separation: d = √(500² + 600² + 5²) = √(250000 + 360000 + 25) = 781.0 m
3. Calculate time to closest point of approach (CPA):
   • Position of UAV-A at time t: (15t, 0, 100)
   • Position of UAV-B at time t: (500, -600+20t, 105)
   • Distance² = (500-15t)² + (-600+20t)² + 5²
   • d(Distance²)/dt = 0 → -30(500-15t) + 40(-600+20t) = 0
   • -15000 + 450t - 24000 + 800t = 0 → 1250t = 39000 → t = 31.2 seconds
4. Calculate CPA distance: At t=31.2s:
   • UAV-A: (468, 0, 100)
   • UAV-B: (500, 24, 105)
   • CPA distance = √(32² + 24² + 5²) = √(1024 + 576 + 25) = 40.3 m < 50 m safe threshold!
5. Calculate detection time: UAV-B enters 400m detection range when: √((500-15t)² + (-600+20t)² + 5²) = 400
   • Solving: t ≈ 11.8 seconds (UAV-B detected at ~400m range)
6. Calculate available response time: Time from detection to CPA = 31.2 - 11.8 = 19.4 seconds. Minus 2 second DAA latency = 17.4 seconds for avoidance
7. Verify avoidance maneuver: UAV-A climbs at 5 m/s for 17.4 seconds = 87 m altitude gain. New altitude separation = 5 + 87 = 92 m (vertical) at CPA time. New 3D separation at CPA = √(32² + 24² + 92²) = 99.6 m > 50 m (safe!)

Result: Without avoidance, the two UAVs would pass within 40.3 m (collision risk). With 400 m detection range and 2-second DAA latency, the system has 17.4 seconds to execute avoidance. A 5 m/s vertical climb provides 92 m altitude separation, achieving 99.6 m total separation at CPA - safely above the 50 m minimum.

Key Insight: Collision avoidance is a time-budget problem. Detection range, processing latency, and maneuver capability must combine to provide sufficient separation before CPA. The critical formula is: Required detection range = Closing speed × (DAA latency + Maneuver time + Safety margin). For high-speed convergences (>30 m/s combined), 400 m detection range may be insufficient, requiring either longer-range sensors (radar vs camera) or reduced operating speeds in congested airspace.

43.4.2 Separation Standards

Scenario	Minimum Separation	Method
UAV-UAV (same swarm)	5-10 m	Formation control
UAV-UAV (different operators)	50-100 m	ADS-B, radar
UAV-Manned Aircraft	500 m+	ADS-B, TCAS
UAV-Obstacle (building)	10-30 m	Lidar, camera
UAV-Ground	30-120 m	Altitude hold

43.5 Mission Types and Trajectory Patterns

43.5.1 Common Mission Profiles

Mission	Pattern	Key Considerations
Area Survey	Lawnmower/Boustrophedon	Overlap for complete coverage
Perimeter Patrol	Circular/Racetrack	Continuous monitoring
Point Inspection	Hover + Spiral	High-resolution data
Search & Rescue	Expanding square	Prioritize likely areas
Delivery	Point-to-point	Obstacle avoidance
Relay/Comm	Station-keeping	Minimize position error

43.5.2 Lawnmower Pattern for Area Coverage

Figure 43.2: Lawnmower survey pattern showing parallel tracks with 180-degree turns for systematic area coverage.

43.6 Hands-On Lab: UAV Mission Planning

viewof altitudeM = Inputs.range([20, 300], {value: 80, step: 5, label: "Flight altitude (m)"})

viewof fovDeg = Inputs.range([30, 120], {value: 84, step: 1, label: "Camera FOV - horizontal (degrees)"})

viewof overlapPct = Inputs.range([5, 60], {value: 20, step: 5, label: "Side overlap (%)"})

viewof fieldWidthM = Inputs.range([100, 5000], {value: 1000, step: 50, label: "Field width (m)"})

{
  const footprint = 2 * altitudeM * Math.tan((fovDeg / 2) * Math.PI / 180);
  const trackSpacing = footprint * (1 - overlapPct / 100);
  const numTracks = Math.ceil(fieldWidthM / trackSpacing);
  return html`<div style="background: var(--bs-light, #f8f9fa); padding: 1rem; border-radius: 8px; border-left: 4px solid #E67E22; margin-top: 0.5rem;">
  <p><strong>Ground footprint width:</strong> ${footprint.toFixed(1)} m</p>
  <p><strong>Track spacing (with ${overlapPct}% overlap):</strong> ${trackSpacing.toFixed(1)} m</p>
  <p><strong>Tracks needed for ${fieldWidthM} m field:</strong> ${numTracks}</p>
  <p style="font-size:0.85em;color:#555;">Footprint = 2 × altitude × tan(FOV/2). Track spacing = footprint × (1 − overlap).</p>
  </div>`
}

43.6.1 Lab Activity: Coverage Mission Design

Scenario: Plan a UAV survey mission for a 1 km × 1 km agricultural field.

Given:

• UAV: DJI Mavic-class (30 min flight time, 15 m/s max speed)
• Camera: 84° FOV, need 5 cm/pixel resolution
• Wind: 5 m/s from the north

Tasks:

1. Calculate Flight Altitude
   • For 5 cm/pixel with given sensor, altitude ≈ 80 m
2. Calculate Track Spacing
   • At 80 m altitude, swath width = 2 × 80 × tan(42°) ≈ 144 m
   • With 20% overlap: track spacing = 144 × 0.8 = 115 m
3. Calculate Number of Tracks
   • Field width: 1000 m ÷ 115 m = 9 tracks
4. Calculate Flight Distance
   • 9 tracks × 1000 m + 8 turns × 50 m = 9,400 m
5. Calculate Flight Time
   • At 10 m/s: 9,400 ÷ 10 = 940 s ≈ 16 minutes
   • Add takeoff/landing: ~20 minutes total
6. Verify Feasibility
   • 20 min < 30 min battery → Mission feasible with 33% reserve

Solution Verification

Parameter	Value	Calculation
Flight altitude	80 m	Resolution requirement
Track spacing	115 m	144 m × 0.8 overlap
Number of tracks	9	1000 m ÷ 115 m
Total distance	9.4 km	(9 × 1000) + (8 × 50)
Flight time	~16 min	9400 m ÷ 10 m/s
Battery reserve	47%	14 min remaining

Result: Mission is feasible with good safety margin.

43.7 Knowledge Check

Test your understanding of these architectural concepts.

Quiz: UAV Trajectory and Coordination

43.8 Visual Reference Gallery

UAV Trajectory Planning Visualization

UAV Trajectory Planning

Dynamic path planning algorithms optimize UAV flight trajectories considering energy consumption, coverage requirements, and network connectivity constraints.

UAV Swarm Coordination

UAV Swarm Coordination

Consensus-based swarm coordination enables multiple UAVs to maintain formations while executing complex missions like area surveillance and search patterns.

FANET Topology Visualization

FANET Topology

Flying Ad-hoc Networks (FANETs) provide the communication backbone for UAV swarms, adapting topology as vehicles move and missions evolve.

43.9 Common Pitfalls

Common Pitfalls in UAV Mission Planning

Ignoring wind in flight time calculations: A 5 m/s headwind reduces ground speed from 15 m/s to 10 m/s, increasing flight time by 50%. Always calculate worst-case leg times using ground speed (airspeed minus headwind component), not airspeed alone. Missions planned with calm-wind assumptions frequently exceed battery limits in real conditions.

Using camera FOV without accounting for altitude: A camera with 84-degree FOV produces vastly different ground footprints at different altitudes. At 80 m, the swath is approximately 144 m; at 40 m, it shrinks to approximately 72 m. Changing altitude to improve resolution halves coverage width, doubling the number of tracks and potentially exceeding battery life.

Setting overlap too low for terrain with elevation changes: A 20% side overlap is the minimum for flat terrain. Hilly terrain, where ground elevation varies by more than 10% of flight altitude, requires 30-40% overlap to prevent coverage gaps at ridgelines. Insufficient overlap creates blind spots that are only discovered after post-processing, requiring an expensive re-fly.

Underestimating DAA processing latency: Many planners assume instant collision detection, but real DAA systems require 1-3 seconds for sensor fusion, trajectory prediction, and decision-making. At a closing speed of 35 m/s (two UAVs approaching head-on), 3 seconds of latency consumes over 100 m of available separation distance. Always subtract full DAA latency from available reaction time.

Neglecting return-to-home energy: A UAV at the far end of a survey pattern may be 1.4 km from the launch point (diagonal of a 1 km x 1 km field). At 15 m/s, the return takes approximately 93 seconds. This return flight, plus altitude changes and landing, can consume 5-8% of total battery. Missions planned to 100% coverage without reserving return energy risk forced landings in the field.

Interactive: UAV Coverage Path Planning

Decision Framework: Coverage Mission Planning Parameters

Parameter	Agricultural Survey	Infrastructure Inspection	Search & Rescue	Emergency Mapping
Altitude	80-120m (balance resolution vs coverage)	10-30m (close-up detail)	50-100m (wide view)	150-200m (rapid overview)
Ground Speed	8-12 m/s (stable imagery)	2-5 m/s (detailed inspection)	10-15 m/s (fast coverage)	15-20 m/s (time-critical)
Overlap	20-30% side, 60% forward (photogrammetry)	40-50% (3D reconstruction)	10-15% (maximize area)	5-10% (single-pass)
Pattern	Lawnmower (parallel tracks)	Vertical or spiral (structure-following)	Expanding spiral (probability-based)	Grid (systematic)
Battery Reserve	30% (return-to-base planned)	40% (climb-down safety)	35% (reposition after find)	25% (mission-critical time pressure)

Planning Workflow:

1. Define coverage area (GPS boundary polygon)
2. Calculate camera footprint at chosen altitude: Width = 2 × altitude × tan(FOV/2)
3. Compute track spacing: Spacing = Footprint × (1 - overlap%)
4. Count tracks needed: Tracks = Field_width ÷ Spacing
5. Calculate distance: (Tracks × Length) + (Turns × Turn_radius × π)
6. Verify energy: (Distance ÷ Speed × Power) + Reserve < Battery
7. If fails: reduce coverage, increase altitude (wider footprint), or use multiple flights

Common Mistake: Using Camera FOV Without Accounting for Altitude

The Mistake: A team plans a mapping mission using a camera with 84-degree FOV. They calculate ground footprint as 144m (from a 1-time calculation at 80m altitude) and use this for all flight planning. When they fly at 120m for wider coverage, they discover 40% of the field has gaps in imagery.

Why It Fails: Camera footprint scales linearly with altitude: - At 80m altitude: Footprint = 2 × 80m × tan(42°) = 144m - At 120m altitude: Footprint = 2 × 120m × tan(42°) = 216m (not 144m) - Track spacing with 20% overlap at 80m: 144m × 0.8 = 115m - At 120m with SAME 115m spacing: Coverage = 115m ÷ 216m = 53% (47% gaps!)

The Fix: Always recalculate footprint when altitude changes:

Correct Formula:

Footprint_width = 2 × Altitude × tan(FOV_horizontal / 2)
Footprint_length = 2 × Altitude × tan(FOV_vertical / 2)
Track_spacing = Footprint_width × (1 - Overlap_fraction)
Number_of_tracks = Field_width ÷ Track_spacing

Example Correction: For 1000m × 1000m field at 120m altitude: - Footprint: 216m wide - Spacing (20% overlap): 216m × 0.8 = 173m - Tracks needed: 1000m ÷ 173m = 6 tracks (not the 9 tracks calculated for 80m altitude) - Total distance: (6 × 1000m) + (5 turns × 50m) = 6,250m (not 9,400m) - Flight time: 6,250m ÷ 10 m/s = 10.4 minutes (not 15.7 minutes)

Rule: Altitude doubling roughly halves the number of tracks needed but halves spatial resolution. Choose based on required ground sample distance (GSD).

Interactive Quiz: Match Concepts

Interactive Quiz: Sequence the Steps

Label the Diagram

💻 Code Challenge

43.10 Summary and Key Takeaways

This chapter covered collision avoidance, mission types, and hands-on UAV mission planning:

• Detect and Avoid (DAA): Multi-sensor systems (cameras, radar, ADS-B, lidar) detect objects, predict trajectories, assess collision risk, and execute avoidance maneuvers when needed. The critical constraint is the time budget: detection range must exceed closing speed multiplied by (DAA latency + maneuver time + safety margin).
• Separation Standards: Different scenarios require different minimum separations – from 5-10 m for same-swarm UAVs using formation control to 500+ m for UAV-manned aircraft encounters requiring ADS-B and TCAS.
• Mission Patterns: Six standard trajectory patterns address different operational needs – lawnmower for area survey, racetrack for perimeter patrol, spiral for point inspection, expanding square for search and rescue, point-to-point for delivery, and station-keeping for relay communications.
• Coverage Planning: Flight altitude determines camera footprint, track spacing equals footprint multiplied by (1 - overlap fraction), total tracks equal field width divided by track spacing, and total flight time must remain below 70% of battery life to maintain safe reserves.
• Feasibility Verification: Every mission plan must be validated against battery duration, wind conditions, airspace restrictions, and return-to-home energy before launch approval.

Related Chapters

Deep Dives:

• UAV Fundamentals and Topologies - Network architectures
• UAV Production and Review - Complete system implementation
• UAV FANET Integration - Protocol integration

Comparisons:

• Mobile Ad-Hoc Networks - Ground vs aerial mobility
• Wireless Sensor Networks - Static vs mobile collection

Applications:

• Search and Rescue - Emergency missions
• Precision Agriculture - Crop monitoring

Design:

• Energy-Aware Design - Battery optimization
• Network Simulation - Testing tools

Learning:

• Simulations Hub - UAV flight simulators
• Videos Hub - Mission planning tutorials

43.11 What’s Next

If you want to…	Read this
Study ad hoc routing protocols	Ad Hoc Networks: Labs and Quiz
Review UAV trajectory control fundamentals	UAV Trajectory Control
Study energy-aware mission planning	UAV Energy-Aware Mission Planning
Get hands-on with trajectory labs	UAV Trajectory Labs and Implementation
Review all UAV network concepts	UAV Networks: Production and Review