%% fig-alt: "UAV power consumption breakdown showing battery as total energy source distributing to four major components: flight propulsion (60-80% with sub-modes of hovering 100-200W, forward flight 150-300W, climbing 200-400W), communication (5-10% with Wi-Fi 2-5W and cellular 1-3W), sensors/camera payload (5-15%), and onboard computer (5-10%), all affecting total flight time of 15-45 minutes"
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#E67E22', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#fff', 'nodeTextColor': '#2C3E50'}}}%%
graph LR
subgraph "UAV Power Consumption Model"
Battery["Battery<br/>(Total Energy)"]
Flight["Flight Propulsion<br/>(60-80% of power)"]
Comm["Communication<br/>(5-10%)"]
Payload["Sensors/Camera<br/>(5-15%)"]
Computer["Onboard Computer<br/>(5-10%)"]
FlightSub1["Hovering:<br/>100-200W"]
FlightSub2["Forward:<br/>150-300W"]
FlightSub3["Climbing:<br/>200-400W"]
CommSub1["Wi-Fi:<br/>2-5W"]
CommSub2["Cellular:<br/>1-3W"]
FlightTime["Flight Time<br/>(15-45 min)"]
end
Battery --> Flight
Battery --> Comm
Battery --> Payload
Battery --> Computer
Flight --> FlightSub1
Flight --> FlightSub2
Flight --> FlightSub3
Comm --> CommSub1
Comm --> CommSub2
Flight -.-> FlightTime
Comm -.-> FlightTime
Payload -.-> FlightTime
Computer -.-> FlightTime
style Battery fill:#2C3E50,stroke:#16A085,color:#fff
style Flight fill:#E67E22,stroke:#2C3E50,color:#fff
style FlightTime fill:#16A085,stroke:#2C3E50,color:#fff
456 UAV Energy-Aware Mission Planning
456.1 Learning Objectives
By the end of this chapter, you will be able to:
- Model UAV Power Consumption: Calculate energy usage for flight, communication, and payload operations
- Estimate Mission Range: Determine feasible flight distances based on battery capacity and power requirements
- Plan Waypoint Missions: Design energy-efficient multi-waypoint tours with safety reserves
- Implement Return-to-Home Logic: Ensure safe operations with emergency return feasibility checks
456.2 Prerequisites
Before diving into this chapter, you should be familiar with:
- UAV Trajectory Control: Understanding trajectory optimization strategies and network monitoring provides context for why energy-efficient flight paths matter
- UAV Networks: Fundamentals and Topologies: Knowledge of UAV platform types, energy constraints, and operational envelopes informs power consumption modeling
456.3 UAV Power Consumption Model
Understanding where energy goes is critical for trajectory planning:
456.3.1 Power Budget Table
| Component | Power (Watts) | Notes |
|---|---|---|
| Forward Flight | 150-300 W | Depends on speed, wind |
| Hovering | 100-200 W | Motors fighting gravity |
| Climbing | 200-400 W | Additional lift power |
| Descending | 50-100 W | Partial regeneration possible |
| Communication (Wi-Fi) | 2-5 W | Continuous transmission |
| Communication (Cellular) | 1-3 W | Lower duty cycle |
| Camera/Sensors | 5-15 W | Depends on resolution |
| Onboard Computer | 5-20 W | Processing load dependent |
456.3.2 Flight Power by Mode
Flight propulsion dominates energy consumption (60-80% of total):
- Hovering: Least efficient per unit time - motors work against gravity with no forward progress
- Forward Flight: Most efficient for covering distance - translates energy into motion
- Climbing: Highest power demand - fighting gravity while gaining altitude
- Descending: Opportunity for energy recovery in some advanced platforms
456.4 Mission Range Estimation
%% fig-alt: "Mission range estimation flowchart starting with battery capacity (5000 mAh at 14.8V = 74 Wh), combining with total power requirement to calculate max flight time, subtracting 20-30% safety reserve to get usable mission time, multiplying by cruise speed (15 m/s) for maximum range, then subtracting return-to-home reserve distance to arrive at safe mission range"
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#E67E22', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#fff', 'nodeTextColor': '#2C3E50'}}}%%
graph TB
subgraph "Mission Range Calculation"
BattCap["Battery Capacity<br/>(e.g., 5000 mAh @ 14.8V<br/>= 74 Wh)"]
PowerReq["Total Power<br/>Requirement<br/>(Sum all components)"]
FlightTime["Max Flight Time<br/>(Battery / Power)"]
Reserve["Safety Reserve<br/>(20-30% buffer)"]
UsableTime["Usable Mission Time"]
Speed["Cruise Speed<br/>(e.g., 15 m/s)"]
MaxRange["Maximum Range<br/>(Time × Speed)"]
RTH["Return-to-Home<br/>Reserve<br/>(Distance to base)"]
MissionRange["Safe Mission<br/>Range"]
end
BattCap --> FlightTime
PowerReq --> FlightTime
FlightTime --> Reserve
Reserve --> UsableTime
UsableTime --> MaxRange
Speed --> MaxRange
MaxRange --> RTH
RTH --> MissionRange
style BattCap fill:#2C3E50,stroke:#16A085,color:#fff
style FlightTime fill:#E67E22,stroke:#2C3E50,color:#fff
style MissionRange fill:#16A085,stroke:#2C3E50,color:#fff
456.4.1 Range Calculation Example
Given: - Battery: 5000 mAh at 14.8V = 74 Wh - Average power consumption: 200 W - Cruise speed: 15 m/s - Safety reserve: 25%
Calculation: 1. Max flight time = 74 Wh / 200 W = 0.37 hours = 22.2 minutes 2. Usable time = 22.2 min × 0.75 = 16.7 minutes 3. Maximum distance = 16.7 min × 60 s/min × 15 m/s = 15,000 m = 15 km 4. With RTH reserve (2 km return): Safe mission range = 13 km
456.5 Waypoint Mission Planning
CautionPitfall: Planning Return-to-Home from Mission Endpoint Only
The Mistake: Calculating battery reserve for return-to-home (RTH) based on distance from the final waypoint to base, ignoring that emergencies can occur at any point in the mission, potentially stranding the UAV at the farthest waypoint from base.
Why It Happens: Simple mission planners calculate total mission distance and verify it fits within battery capacity, but don’t consider worst-case RTH scenarios from every point along the trajectory.
The Fix: For each waypoint in the mission, calculate the maximum distance to base station (not straight-line, but accounting for no-fly zones and obstacles). The battery reserve must cover RTH from the farthest reachable point, not just the endpoint. Example: If waypoint 3 is 4 km from base while the final waypoint is only 2 km away, reserve battery for 4 km return, not 2 km. Implement continuous “RTH feasibility” monitoring that triggers automatic return when remaining battery equals RTH requirement plus 10% safety margin.
CautionPitfall: Optimizing Trajectory for Distance Without Considering Altitude Changes
The Mistake: Using 2D distance optimization (like standard Traveling Salesman Problem solvers) for UAV waypoint ordering, ignoring that altitude changes consume significantly more energy than horizontal flight.
Why It Happens: Traditional route optimization tools are designed for ground vehicles where altitude is irrelevant. Teams apply these tools directly to UAV missions without adapting the cost function for 3D flight.
The Fix: Replace 2D Euclidean distance with energy-weighted 3D cost function. Climbing consumes 2-3x more power than level flight; descending uses 50-70% of level flight power. A waypoint 500m away horizontally but 100m higher costs more energy than one 700m away at the same altitude. Modify TSP solver cost matrix to use: cost = horizontal_distance + (altitude_gain * 2.5) - (altitude_loss * 0.3). This often produces routes that minimize climbing by visiting high-altitude waypoints in sequence rather than yo-yoing between altitudes.
When planning multi-waypoint missions, order matters:
%% fig-alt: "Waypoint mission optimization flowchart showing mission start from base station, four waypoints as inputs (sensor clusters A and B, inspection point, data upload), route optimization using nearest neighbor or traveling salesman problem solver to minimize distance, energy feasibility check, execution of optimized route visiting waypoints in efficient order (WP1→WP3→WP2→WP4), continuous battery monitoring triggering emergency return-to-home if battery drops below 25%, and final return to base"
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#E67E22', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#fff', 'nodeTextColor': '#2C3E50'}}}%%
graph TB
subgraph "Waypoint Mission Optimization"
Start["Mission Start<br/>(Base Station)"]
WP1["Waypoint 1<br/>(Sensor Cluster A)"]
WP2["Waypoint 2<br/>(Sensor Cluster B)"]
WP3["Waypoint 3<br/>(Inspection Point)"]
WP4["Waypoint 4<br/>(Data Upload)"]
Optimize["Route Optimization<br/>(Nearest Neighbor<br/>or TSP solver)"]
EnergyCheck["Energy Feasibility<br/>Check"]
Execute["Execute Optimized<br/>Route"]
Emergency["Emergency RTH<br/>if Battery < 25%"]
Return["Return to Base"]
end
Start --> Optimize
WP1 -.->|Input| Optimize
WP2 -.->|Input| Optimize
WP3 -.->|Input| Optimize
WP4 -.->|Input| Optimize
Optimize --> EnergyCheck
EnergyCheck -->|Feasible| Execute
EnergyCheck -->|Not Feasible| Emergency
Execute --> WP1
WP1 --> WP3
WP3 --> WP2
WP2 --> WP4
WP4 --> Return
Execute -.->|Continuous Monitor| Emergency
style Start fill:#2C3E50,stroke:#16A085,color:#fff
style Optimize fill:#E67E22,stroke:#2C3E50,color:#fff
style Execute fill:#16A085,stroke:#2C3E50,color:#fff
style Emergency fill:#E74C3C,stroke:#2C3E50,color:#fff
456.6 UAV-WSN Data Collection
NoteWorked Example: Energy-Optimized Sensor Data Collection Tour
Scenario: A UAV must collect data from 8 ground sensors deployed across a remote vineyard for precision agriculture. The sensors have been logging soil moisture data for 24 hours and their buffers are nearly full. You need to plan an energy-efficient collection tour that visits all sensors before any buffer overflows.
Given: - 8 sensors at coordinates (km from base): S1(0.5, 0.3), S2(1.2, 0.8), S3(0.8, 1.5), S4(1.8, 1.2), S5(2.2, 0.5), S6(1.5, 2.0), S7(2.5, 1.8), S8(0.3, 2.2) - UAV flight speed: 10 m/s - Hover time per sensor: 30 seconds (data download at 2 Mbps) - Battery capacity: 74 Wh (30 min flight time) - Power: 148 W (cruise), 180 W (hover) - Base station at origin (0, 0) - Buffer status (% full): S1=60%, S2=85%, S3=70%, S4=95%, S5=75%, S6=80%, S7=90%, S8=65%
Steps: 1. Prioritize by buffer urgency: S4(95%) > S7(90%) > S2(85%) > S6(80%) > S5(75%) > S3(70%) > S8(65%) > S1(60%) 2. Apply nearest-neighbor heuristic starting from base: - Base → S1 (0.58 km) → S2 (0.86 km) → S4 (0.72 km) → S5 (0.81 km) → S7 (0.76 km) → S6 (0.68 km) → S3 (0.82 km) → S8 (0.85 km) → Base (2.23 km) - Total distance: 8.31 km 3. Calculate flight time: 8.31 km / 10 m/s = 831 seconds = 13.9 minutes 4. Calculate hover time: 8 sensors × 30 seconds = 4 minutes 5. Calculate total energy: - Flight: 13.9 min × (148 W / 60) = 34.3 Wh - Hover: 4 min × (180 W / 60) = 12.0 Wh - Total: 46.3 Wh (62.6% of 74 Wh capacity) 6. Verify critical sensor timing: Distance to S4 via optimized route = 0.58 + 0.86 + 0.72 = 2.16 km. Time to reach S4 = 216 sec + 60 sec (2 hovers) = 4.6 minutes. S4 at 95% full, fills at ~4%/hour = safe margin. 7. Calculate return-to-home reserve: After S8, base is 2.23 km away. RTH energy = 2.23 km × (148 W / 600 m/min) = 5.5 Wh. Remaining: 74 - 46.3 = 27.7 Wh (sufficient for RTH + 22 Wh reserve).
Result: The optimized tour covers all 8 sensors in 17.9 minutes total (flight + hover), using 62.6% battery capacity with 37.4% reserve. Critical sensor S4 is reached within 5 minutes, well before buffer overflow. The nearest-neighbor route reduces total distance by ~18% compared to sequential ordering by sensor number.
Key Insight: UAV-WSN collection tours must balance two constraints: (1) energy efficiency (minimize total distance) and (2) data urgency (visit high-buffer sensors first). The nearest-neighbor heuristic provides a good trade-off, but in practice, insert high-priority sensors into the route even if they increase distance slightly to prevent data loss from buffer overflow.
456.6.1 The Collection Tour Problem
UAVs can efficiently collect data from ground sensor networks:
%% fig-alt: "UAV-WSN data collection tour flowchart: Mission starts with computing collection priorities based on buffer fullness, time since last collection, and battery level; solving traveling salesman problem to optimize tour order; visiting sensors in priority order (critical sensor first, then high/medium/low priority sensors); hovering at each collection point for single-hop communication at high rate (~50 Mbps); checking battery and storage capacity after each collection; continuing to next waypoint or returning to base if battery low or storage full"
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#E67E22', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#fff', 'nodeTextColor': '#2C3E50'}}}%%
graph TB
subgraph "UAV-WSN Data Collection Tour"
Mission["Mission Start"]
Priority["Compute Collection<br/>Priorities<br/>(Buffer fullness,<br/>time since collection,<br/>battery level)"]
TSP["Solve Traveling<br/>Salesman Problem<br/>(Optimize tour)"]
S1["Sensor 1<br/>(High Priority)"]
S2["Sensor 2<br/>(Medium)"]
S3["Sensor 3<br/>(Low)"]
S4["Sensor 4<br/>(Critical)"]
Hover["Hover at<br/>Collection Point<br/>(Single-hop<br/>communication)"]
Collect["Collect Data<br/>(High rate:<br/>~50 Mbps)"]
Check["Check Battery<br/>& Storage"]
Next["Next Waypoint"]
Return["Return to Base<br/>(Upload data)"]
end
Mission --> Priority
Priority --> TSP
TSP --> S4
S4 --> Hover
Hover --> Collect
Collect --> Check
Check -->|More waypoints| Next
Next --> S1
S1 --> Hover
Check -->|Low battery or<br/>storage full| Return
style Mission fill:#2C3E50,stroke:#16A085,color:#fff
style TSP fill:#E67E22,stroke:#2C3E50,color:#fff
style Collect fill:#16A085,stroke:#2C3E50,color:#fff
456.6.2 Collection Priority Factors
| Factor | Weight | Rationale |
|---|---|---|
| Buffer Fullness | High | Prevents data loss from overflow |
| Time Since Collection | Medium | Ensures freshness |
| Sensor Battery | Medium | Dying sensors need priority |
| Data Criticality | High | Emergency alerts first |
| Distance from Path | Low | Energy efficiency |
456.6.3 Hover vs Fly-By Collection
| Method | Data Rate | Energy Cost | Best For |
|---|---|---|---|
| Hover | High (~50 Mbps) | High | Large data volumes |
| Slow Fly-By | Medium (~10 Mbps) | Medium | Regular collection |
| Fast Fly-By | Low (~1 Mbps) | Low | Status updates only |
| Cluster Head | Varies | Low (per sensor) | Dense networks |
456.7 Summary
This chapter covered energy-aware UAV mission planning:
- Power Consumption Modeling: Flight propulsion dominates energy use (60-80%), with hovering, climbing, and forward flight having distinct power profiles that must be accounted for in mission planning
- Range Estimation: Battery capacity, power consumption, safety reserves, and return-to-home requirements combine to determine safe mission range
- Waypoint Optimization: Route ordering using nearest-neighbor or TSP heuristics reduces total flight distance, while altitude-aware cost functions account for the extra energy of climbing vs level flight
- UAV-WSN Integration: Collection tours balance energy efficiency (distance minimization) with data urgency (buffer fullness priority) to maximize data retrieval within battery constraints
456.8 What’s Next
Having mastered energy-aware mission planning, the next chapter explores swarm formation control and multi-UAV coordination architectures for large-scale deployments.
Continue to Swarm Formation Control →