Autonomous Drone Path Planning Using Simulated 3D Views
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Solution Overview
Problem
Existing techniques for drone-based inspection and maintenance of three-dimensional structures are limited in their ability to perform tasks autonomously and efficiently, as they require detailed user instructions and lack the capability to learn from their operations, making them tedious and time-consuming for complex structures.
Innovation Solution
A method and system that utilize a simulated 3D view with a hierarchy of augmented views, historical data, and drone capabilities to configure paths and learn maneuverability, allowing drones to autonomously perform tasks by planning and executing them based on previous experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed user instructions are provided for each task, then task execution accuracy is improved, but operation time and complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-planning task execution paths using simulated 3D views and historical data before actual drone deployment. The path planning is configured in advance based on simulated environments, allowing the drone to execute pre-determined routes without real-time user intervention, thus reducing operation time while maintaining accuracy through pre-calculated optimal paths
Solution Approach 2:
The system creates a simulated 3D copy of the target structure to perform path planning and task simulation before actual execution. This virtual copy allows the system to learn and optimize paths without affecting real operations, enabling faster execution by reusing proven paths from historical data while maintaining execution accuracy through virtual validation
2Productivity
If autonomous learning is implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms by continuously learning from historical task execution data and performance outcomes. The learning module analyzes past operations to improve future path planning and task execution, enabling autonomous improvement of productivity without requiring proportional increases in system complexity, as the feedback loop leverages existing operational data
Solution Approach 2:
The system performs preliminary learning and path configuration using simulated environments before actual task execution. This pre-learning phase allows the autonomous system to develop competence in advance, improving productivity during actual operations without requiring complex real-time decision-making systems, as the heavy learning burden is shifted to the pre-execution simulation phase
3Measurement precision
If simulated 3D views with augmented reality are used, then task planning accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs computationally intensive path planning and analysis in advance using simulated 3D views before actual drone deployment. By completing the heavy computational work during the planning phase rather than during real-time execution, the system achieves high path planning accuracy while minimizing energy consumption and processing requirements during the actual task execution phase
Solution Approach 2:
The system creates and manipulates simulated 3D copies of the target structure for path planning purposes, rather than requiring complex real-time processing during actual operations. This virtual copying approach allows detailed accuracy analysis in the simulation environment while keeping real-time computational requirements minimal, as the heavy processing is confined to the pre-execution planning phase
Data Source
AI summary
This disclosure relates generally to drones, and more particularly to method and system for performing inspection and maintenance tasks of three-dimensional structures (3D) using drones. In one embodiment, a method for performing a task with respect to a 3D structure is disclosed. The method includes receiving a simulated 3D view of the 3D structure. The simulated 3D view comprises a hierarchy of augmented views to different degrees. The method further includes configuring one or more paths for performing a task on the 3D structure based on the hierarchy of augmented views, historical data on substantially similar tasks, and a capability of the at least one drone. The method further includes learning maneuverability and operations with respect to the one or more paths and the task based on the historical data on substantially similar tasks, and effecting performance of the task based on the learning through the at least one drone.