Autonomous Vehicle Fleet Coverage Using Iterative Path Replanning
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Solution Overview
Problem
Existing methods for operating autonomous vehicles to accomplish coverage tasks are limited by the use of a single type of vehicle, which can restrict operational parameters such as coverage rate, precision, speed, and cost, leading to increased resources and time requirements.
Innovation Solution
A method and system that iteratively select and operate multiple types of autonomous vehicles (ground, aerial, and naval) based on their unique operational parameters to optimize coverage, analyzing sensory data to identify uncovered segments and adjust vehicle selection and operation paths to achieve optimal coverage with minimal resources and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single type of autonomous vehicle is used to accomplish coverage tasks, then device complexity is reduced, but productivity and coverage efficiency deteriorate
Solution Approach 1:
The patent segments the coverage task into multiple phases or zones, assigning different autonomous vehicle types to different segments based on their operational characteristics. This allows each vehicle type to operate in its optimal performance range, improving overall coverage efficiency without requiring a single complex vehicle to handle all scenarios.
Solution Approach 2:
The system creates a multi-functional vehicle fleet where different autonomous vehicle types (aerial, ground, naval) can be deployed for the same coverage task depending on environmental conditions, task requirements, and uncovered area characteristics. This universal approach to task completion improves productivity while managing complexity through standardized control architecture.
2Measurement precision
If high coverage precision is achieved using a single vehicle type, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent divides the coverage area into segments that are tackled by different vehicle types sequentially or in parallel. High-precision coverage is achieved in critical segments using appropriate vehicle types, while less critical segments are covered more rapidly, optimizing the trade-off between precision and time without requiring a single vehicle to achieve maximum precision everywhere.
Solution Approach 2:
The system applies different levels of coverage precision to different areas based on their importance or characteristics. Rather than uniformly applying maximum precision across the entire area (which would maximize time loss), the system applies precision selectively where needed, achieving sufficient coverage faster while maintaining high precision in critical zones.
3Productivity
If high coverage rate is achieved using a single vehicle type, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The patent segments the coverage task by assigning high-speed vehicles to areas where rapid coverage is prioritized and high-precision vehicles to areas requiring accurate coverage. This spatial segmentation allows the system to achieve high overall coverage rates while maintaining precision in critical areas, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The system applies different quality levels of coverage to different local areas based on their specific requirements. Rather than using a uniform approach, the patent tailors the coverage precision and speed to local conditions, allowing high coverage rates in appropriate areas while maintaining high precision where needed, thus resolving the contradiction between productivity and precision.
4Adaptability or versatility
If multiple types of autonomous vehicles are deployed, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal control architecture that can manage multiple types of autonomous vehicles through standardized interfaces and protocols. This allows the system to deploy diverse vehicle types for different operational scenarios (improving adaptability) while maintaining manageable complexity through unified control and coordination mechanisms.
Solution Approach 2:
The system dynamically selects and deploys appropriate vehicle types based on real-time assessment of environmental conditions, task requirements, and area characteristics. This dynamic adaptability allows the fleet to respond flexibly to changing conditions without requiring permanent configuration of all vehicle types, thereby managing complexity while maintaining versatility.
5Productivity
If iterative vehicle selection and path computation is performed, then productivity is improved, but loss of time in computation increases
Solution Approach 1:
The patent performs preliminary computations of optimal paths and vehicle selections based on initial assessments of the coverage area and task requirements. By pre-computing routes and making advance decisions about vehicle deployment, the system reduces real-time computation needs while maintaining high coverage efficiency, thus resolving the contradiction between iterative optimization and computation time.
Solution Approach 2:
The system implements feedback mechanisms where coverage progress is continuously monitored and used to adjust subsequent vehicle deployments and path computations. This feedback-driven iterative process optimizes coverage efficiency by focusing computational resources on uncovered or partially covered areas, reducing overall computation time while maintaining high productivity through targeted re-planning.
Data Source
AI summary
Described herein are methods and systems for automatically operating autonomous vehicles to accomplish a coverage task by receiving a plurality of task parameters defining a coverage task in a certain geographical area, calculating and outputting instructions for operating first autonomous vehicle(s) to cover the certain geographical area according to a first movement path computed according to operational parameters of the first autonomous vehicle with respect to the task parameters, identifying uncovered segment(s) in the certain geographical area by analyzing coverage of the certain geographical, and calculating and outputting instructions for operating second autonomous vehicle(s) to cover the uncovered segment(s) according to a second movement path computed according to operational parameters of the second autonomous vehicle. The first autonomous vehicle(s) and the second autonomous vehicle(s) are selected to optimally accomplish the coverage task. The second autonomous vehicle having increased coverage precision and reduced coverage rate compared to the first autonomous vehicle.