Multi-Vehicle Spatially Balanced Coverage System and Method
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Solution Overview
Problem
Conventional task allocation and path planning algorithms in network communication and ridesharing systems lack flexibility, dynamic adaptability, and consider practical factors like path length, leading to inefficient resource allocation and reduced user experience.
Innovation Solution
A multi-vehicle spatially balanced coverage system and method that uses sensor-equipped mobile vehicles to compute non-intersecting paths and balance workloads and spatial coverage, employing a submodular function and Matroid constraints to optimize path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional multi-root tree topology is used for network communication, then redundancy and flexibility are provided, but network management complexity increases and operational efficiency decreases
Solution Approach 1:
The patent segments the network into multiple independent coverage areas, each managed by a single root node (mobile vehicle). This divides the complex multi-root tree management into simpler single-root management units, reducing overall network management complexity while maintaining redundancy through parallel segmentation.
Solution Approach 2:
The patent implements dynamic path planning that allows mobile vehicles to adapt their routes in real-time based on environmental conditions and task requirements. This dynamic adjustment enables the system to maintain flexibility and redundancy without requiring complex static multi-root tree topology management.
2Extent of automation
If threshold-based load balancing is used in network communication systems, then resource reallocation is automated, but flexibility is reduced and system response to complex dynamic environments is slower
Solution Approach 1:
The patent employs continuous feedback mechanisms where mobile vehicles monitor their own workload, spatial coverage, and environmental conditions in real-time. This feedback drives dynamic path planning adjustments, enabling automated resource allocation that adapts flexibly to changing conditions rather than relying on fixed thresholds.
Solution Approach 2:
The system transitions from static threshold-based load balancing to dynamic workload balancing through real-time path planning. Mobile vehicles continuously adjust their routes based on current spatial coverage and workload conditions, providing both automation and flexibility simultaneously.
3Productivity
If integer linear programming methods are used for vehicle-passenger assignment, then large-scale assignment problems are handled effectively, but real-world scenario complexity is oversimplified
Solution Approach 1:
The patent performs preliminary path planning for multiple mobile vehicles before task execution, considering spatial coverage and workload balance constraints. This preliminary action prepares optimized routes that account for real-world complexities like path intersections and coverage overlap, rather than oversimplifying the assignment problem.
Solution Approach 2:
The system applies local quality optimization by considering specific spatial coverage requirements and workload characteristics for each individual mobile vehicle and coverage area. This localized approach handles real-world scenario complexity more effectively than global integer linear programming methods that treat all assignments uniformly.
4Use of energy by moving object
If resource-constrained travel function minimization is used, then travel function is minimized under resource cap, but path length constraint is omitted leading to suboptimal assignment schemes
Solution Approach 1:
The patent changes the optimization parameters to include both travel function and path length as concurrent constraints in the path planning process. By adjusting these parameters together rather than minimizing travel function alone, the system achieves assignment schemes that are optimal in both energy efficiency and path length.
Solution Approach 2:
The system performs preliminary path planning that simultaneously considers travel function minimization and path length constraints before task execution. This preliminary optimization ensures that subsequent task assignments are based on routes that satisfy both energy efficiency and practical path length requirements.
Data Source
AI summary
The present invention relates to a multi-vehicle spatially balanced coverage system. The system includes a first mobile vehicle including a first sensor unit configured to sense a first environmental information; a second mobile vehicle including a second sensor unit configured to sense a second environmental information; and a computing unit configured to receive the first environmental information and the second environmental information, input them into a balanced load function, and accordingly compute a first forward point of interest and a second forward point of interest, to command in real-time the first mobile vehicle to move from a first initial point of interest to the first forward point of interest and the second mobile vehicle to move from a second initial point of interest to the second forward point of interest.