Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection (1706.04399v1)

Abstract: In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.

• The paper reformulates UAV inspection path planning as an extended Traveling Salesman Problem that includes both coverage and obstacle avoidance.
• An enhanced Discrete Particle Swarm Optimization algorithm, incorporating deterministic initialization, mutation, and edge exchange, efficiently solves this complex path planning problem.
• Using parallel computing, the algorithm achieves significant reductions in computation time, making UAV surface inspection more efficient and applicable to complex structures.

Enhanced Discrete Particle Swarm Optimization for UAV Vision-based Surface Inspection

The paper "Enhanced Discrete Particle Swarm Optimization Path Planning for UAV Vision-based Surface Inspection" presents a methodological advancement in path planning algorithms specifically designed for Unmanned Aerial Vehicles (UAVs) conducting surface inspections of built infrastructure. In the field of structural health monitoring (SHM), efficient inspection algorithms are paramount to enhance data acquisition capabilities, particularly for structures like bridges and buildings.

The researchers reformulate the inspection path planning problem into an extended Traveling Salesman Problem (TSP). This reformulation considers both coverage and obstacle avoidance aspects, which are crucial for UAV navigation in complex environments. The innovative approach employs an enhanced Discrete Particle Swarm Optimization (DPSO) algorithm, tailored to efficiently solve the IPP challenge. The proposed DPSO algorithm incorporates deterministic initialization, random mutation, and edge exchange strategies to optimize path planning, thereby enhancing its accuracy and efficiency.

Key Contributions

1. Problem Formulation: The paper models the IPP as an extended TSP, integrating both coverage requirements and obstacle avoidance within the optimization process. This dual focus distinguishes it from conventional TSP formulations which typically do not handle dynamic environmental constraints to this extent.
2. Enhanced DPSO Algorithm: The enhancement of DPSO involved three significant modifications:
   • Deterministic initialization allows for a more informed initial positioning of the particles, potentially leading to faster convergence.
   • Random mutation and edge exchange techniques are employed to circumvent local optima, thus improving the overall exploration of the solution space.
3. Parallel Computing Implementation: By leveraging graphical processing units (GPUs) through parallel computing, the DPSO achieves significant reductions in computation time without necessitating additional hardware resources. This implementation is particularly advantageous for real-time applications where computational efficiency is crucial.

Experimental Validation

The robustness and applicability of the enhanced DPSO are demonstrated through experimental validations using real-world datasets obtained from UAV inspections of an office building and a bridge. These case studies exhibit the algorithm's capability to generate efficient inspection paths while adhering to the coverage and obstacle avoidance constraints. The results indicate a substantial improvement in both travel cost and computational efficiency over traditional methods such as the Ant Colony System (ACS).

Implications and Future Directions

The practical implications of this research are profound within the field of UAV-based inspections. The enhanced DPSO not only provides a more computationally efficient solution to the IPP problem but also extends its applicability to larger, more complex inspection areas with irregular structures. This could lead to more widespread use of UAVs in infrastructure monitoring, reducing inspection times and costs while improving data quality.

Theoretically, the development of a more generalized optimization framework that can accommodate non-planar surfaces and dynamically update paths in real time represents an avenue for future exploration. Further research could focus on integrating this algorithm with machine learning techniques to enhance adaptive path planning capabilities in dynamic environments.

In conclusion, by innovating on established optimization techniques, the authors provide a compelling solution to a pressing challenge in UAV-based inspection methodologies. The integration of enhanced DPSO within a parallel computing framework underscores its potential for real-time applications and sets a precedent for upcoming advancements in robotic inspection systems.