Variable-Resolution Motion Planning for Autonomous Parking
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Solution Overview
Problem
Autonomous or automated parking systems face computational challenges due to large search spaces and complex environments with static and dynamic objects, leading to high computational costs in motion planning tasks.
Innovation Solution
Implementing a variable grid resolution for graph-search-based planning, using finer grid resolution near the vehicle and coarser resolution further away, to reduce computational complexity in navigating through parking environments with static and dynamic obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fine grid resolution is used for the entire path distance in graph-based motion planning, then path planning precision is improved, but computational cost increases significantly
Solution Approach 1:
The patent applies local quality by using different grid resolutions in different spatial regions: a first (finer) grid resolution is used for grid cells near the vehicle's current position where high precision is needed for collision avoidance, while a second (coarser) grid resolution is used for grid cells further away where lower precision is acceptable. This resolves the contradiction by maintaining path planning precision where necessary while reducing overall computational cost through selective resolution adjustment.
2Power
If a coarse grid resolution is used for the entire path distance, then computational cost is reduced, but path planning precision deteriorates
Solution Approach 1:
The patent maintains path planning precision by implementing local quality through variable grid resolution: finer resolution is applied in critical regions near the vehicle where accurate path planning is essential for safety, while coarser resolution is applied in less critical regions further from the vehicle. This ensures that precision is preserved where it matters most while achieving overall computational efficiency.
3Productivity
If variable grid resolution is implemented, then computational efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamics by making the grid resolution variable and adaptive rather than static. The system dynamically adjusts the grid resolution based on the vehicle's current position, using a first resolution for nearby grid cells and a second resolution for distant grid cells. This dynamic approach improves computational efficiency by focusing computational resources where needed while automatically adapting to changing conditions, with the complexity managed through systematic resolution transitions.
Data Source
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AI summary
This document describes motion planning with variable grid resolution for graph-search-based planning. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a motion-planning algorithm to determine a path or trajectory using two or more grid resolutions for a graph-based search. The path includes a series of waypoints, including two-dimensional positional coordinates (and time coordinates if a trajectory), to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the path using an assisted-driving or autonomous-driving system. In this way, motion planning is performed for the entire path but uses a coarser grid resolution for the portion nearer the goal pose. This allows motion planning for autonomous parking, especially in environments that include static and dynamic objects, to be handled in a more computationally-efficient manner.