Virtual Vehicle Swarm Path Planning for Moving Obstacle Avoidance
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Solution Overview
Problem
In dynamic environments with moving obstacles, existing path planning technologies face challenges in quickly calculating optimal paths to avoid collisions in real-time, leading to potential delays in providing effective routes for moving objects.
Innovation Solution
A path providing apparatus and method that utilize a virtual vehicle swarm to generate and update path prediction data based on current location and obstacle data, combining path prediction and calculation data to determine optimal paths, even when start or destination locations are not initially mapped, using vector, distance, time, mission, or vehicle status data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time path calculation is performed to avoid moving obstacles, then collision avoidance capability is improved, but calculation time becomes too long to avert collisions
Solution Approach 1:
The system pre-calculates multiple possible path predictions before obstacles are detected, storing them for rapid selection. When a moving obstacle is detected, the system immediately selects from pre-computed paths rather than calculating from scratch, thus achieving both collision avoidance and real-time responsiveness
Solution Approach 2:
The system dynamically switches between pre-calculated path predictions based on real-time obstacle detection. Multiple path predictions are generated in advance, and the system dynamically selects and adjusts the appropriate path when obstacles are detected, enabling fast response without extensive real-time calculation
Data Source
AI summary
A path providing apparatus includes a communication interface configured to obtain current location data of a vehicle, receive obstacle data of moving objects, and receive a path finding command including a first location and a second location of the vehicle; and a processor configured to generate a virtual vehicle swarm comprising a plurality of virtual vehicles, generate a plurality of pieces of path prediction data by virtually driving each of the plurality of virtual vehicles on a first map based on the current location data, update the plurality of pieces of path prediction data by virtually driving each of the plurality of virtual vehicles on a second map based on the obstacle data, and generate optimal path data from among the updated plurality of pieces of path prediction data based on the path finding command including the first location and the second location.