Project Overview
Modern challenges in large-scale area coverage demand innovative solutions, especially when deploying multi-UAV teams for applications like disaster response, environmental surveys, and urban mapping. This project presents the Scalable Coverage Path Planning (SCoPP) framework, a highly efficient and adaptable approach tailored for non-convex, large-scale environments. SCoPP not only ensures comprehensive coverage but also emphasizes equitable load balancing across UAVs, minimizing mission time and maximizing efficiency.
Key Features
- Scalable framework supporting up to 150 UAVs for large-scale coverage missions
- Optimized load balancing with up to 30% improvement over baseline approaches
- Efficient handling of non-convex environments with complex boundaries and constraints
- Specialized variants for priority-based (SCoP3) and time-constrained (SCoPE) missions
- Validated through extensive simulations and real-world field trials
Framework Design
The SCoPP framework operates through a meticulously designed pipeline comprising five core stages:
Core Pipeline
- Area Selection: Identifying and segmenting the target zones, including defining priorities and constraints like no-fly zones.
- Discretization: Dividing the area into manageable grid cells, ensuring precise coverage calculations.
- Partitioning: Assigning grid cells to individual UAVs based on workload balance and spatial proximity.
- Conflict Resolution: Addressing overlaps or constraints to ensure smooth navigation and task distribution.
- Path Planning: Generating optimized, collision-free routes for each UAV, tailored to their assigned regions.
Specialized Variants
SCoP3: Priority-Based Coverage
The SCoP3 variant incorporates user-defined priorities, allowing UAVs to focus on high-importance regions first. This is particularly valuable in scenarios such as:
- Search and rescue operations where certain areas have higher likelihood of finding survivors
- Environmental monitoring where specific zones require more frequent or detailed observation
- Agriculture applications where certain crop sections need priority attention due to pest risks or ripening status
SCoPE: Time-Optimized Coverage
The SCoPE variant is optimized for time-constrained missions, prioritizing swift execution without compromising coverage. Key features include:
- Energy-aware path planning that considers UAV battery limitations
- Accelerated coverage patterns that minimize unnecessary maneuvers
- Dynamic reallocation of resources when certain UAVs complete their assigned regions ahead of schedule
Technical Innovations
Several innovative techniques were developed to enhance the performance of the SCoPP framework:
Advanced Load Balancing
Our partitioning algorithm uses a novel weighted approach that considers not just area size but also complexity factors like obstacle density and terrain variations, ensuring truly equitable workload distribution across the UAV team.
Adaptive Tessellation
Rather than using fixed grid cells, our discretization process adapts to environmental features, creating finer resolution in complex areas while maintaining efficiency in open spaces.
Hierarchical Planning
A multi-level planning approach that allows rapid global planning followed by detailed local refinement, significantly reducing computational time while maintaining plan quality.
Dynamic Replanning
Real-time adaptation capabilities that allow the system to respond to unexpected obstacles, UAV failures, or changing mission priorities without requiring a complete recalculation.
Results and Performance
Extensive simulations in both synthetic and real-world inspired scenarios demonstrated SCoPP's superiority over baseline approaches:
Quantitative Performance
| Metric | Baseline Approaches | SCoPP Framework | Improvement |
|---|---|---|---|
| Workload Balance | ±18.7% variation | ±7.2% variation | 30% improvement |
| Mission Completion Time | 38.4 minutes | 28.9 minutes | 25% reduction |
| Planning Computation Time | 8.2 minutes | 2.4 minutes | 71% reduction |
| Coverage Completeness | 94.8% | 99.2% | 4.6% improvement |
| Scalability (max UAVs) | 48 UAVs | 150+ UAVs | 212% improvement |
Field Trial Validation
Field trials using physical UAVs further validated these findings, showcasing robust performance in environments with real-world uncertainties:
- Real-world Implementation: Successfully deployed on a team of 8 custom quadrotor UAVs equipped with onboard computing and sensing capabilities.
- Robustness Testing: Maintained 96.4% performance efficiency even with introduced disturbances like wind gusts and temporary communication losses.
- Adaptability Demonstration: Successfully adjusted to the sudden "failure" of two UAVs mid-mission, redistributing their workload among the remaining team members.
Applications
The SCoPP framework has demonstrated its versatility across several critical application domains:
Disaster Response
The SCoPP framework enables rapid assessment of disaster-stricken areas, with SCoP3's priority-based coverage ensuring that high-risk zones receive immediate attention while maintaining comprehensive situational awareness.
Environmental Monitoring
For tracking environmental changes like deforestation, pollution spread, or wildlife population dynamics, SCoPP provides efficient coverage strategies that can be repeated over time to collect consistent temporal data.
Urban Mapping
SCoPP's ability to handle complex, non-convex environments makes it ideal for urban mapping missions, efficiently navigating around tall buildings and restricted airspaces while maintaining thorough coverage.
Precision Agriculture
The framework's adaptive tessellation allows for detailed monitoring of agricultural fields, with options to focus on specific crop sections that may require more attention based on growth patterns or pest outbreaks.
Technologies Used
Python
C++
ROS 2
Gazebo
CGAL
NumPy
Conclusion
SCoPP stands out as a cutting-edge solution bridging the gap between simulation and practical deployment. Whether managing disaster-stricken areas, tracking environmental changes, or enabling urban surveys, SCoPP redefines scalability and efficiency in multi-UAV path planning.
Future work focuses on extending the framework to heterogeneous teams of robots with varying capabilities, incorporating real-time environmental data feeds, and developing learning-based components that can adapt coverage strategies based on historical mission data.