Region-Based Driving Control Using Swarm Behavior Maps
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Solution Overview
Problem
Existing methods for controlling the driving function of movable devices, such as vehicles or robots, require frequent application of complex and data-intensive swarm behavior maps, leading to increased processing and transmission efforts.
Innovation Solution
A method utilizing a map with defined first and second regions, where swarm behavior values are entered in the second region, allowing for reduced reliance on behavior maps by using parameters specific to each region, and adapting these parameters through iterative calculations to match target values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If swarm behavior maps are frequently applied to control driving functions, then driving accuracy is improved, but processing effort and data transmission requirements increase
Solution Approach 1:
The map is divided into multiple regions, each with different data density characteristics. First regions contain comprehensive swarm behavior data for high accuracy, while second regions contain reduced data for lower processing requirements. This segmentation allows the system to adaptively select appropriate data density based on location, resolving the contradiction between accuracy and processing effort.
Solution Approach 2:
Different regions of the map are assigned different qualities of data representation. The first region uses detailed swarm behavior values for precise control, while the second region uses reduced data representation. This local quality differentiation enables the system to maintain high accuracy where needed while reducing overall processing complexity.
2Measurement precision
If swarm behavior maps are frequently applied to control driving functions, then driving accuracy is improved, but data transmission requirements increase
Solution Approach 1:
The map data is segmented into regions with different transmission requirements. Only essential data for the second region is transmitted and stored, while comprehensive data exists only for the first region. This reduces the quantity of data that needs to be transmitted and stored without completely sacrificing accuracy where needed.
Solution Approach 2:
The invention extracts only the necessary swarm behavior data for the second region, storing minimal essential information rather than complete data sets. This extraction approach reduces data transmission volume while maintaining sufficient accuracy for regions where reduced data representation is applied.
3Device complexity
If parameters are used to control driving function, then processing effort is reduced, but adaptability to swarm behavior decreases
Solution Approach 1:
The system dynamically selects between parameter-based control and swarm behavior map-based control depending on the current region. In the second region, parameter-based control reduces processing effort, while in the first region, comprehensive swarm behavior data provides adaptability. This dynamic switching resolves the contradiction between processing effort and adaptability.
Data Source
AI summary
A method for controlling a driving function of a movable device, including a vehicle or a robot. The method includes: reading in parameters for controlling the driving function; using a map, wherein the map has at least a first region and a second region, wherein at least one value of a swarm behavior is entered in the second region; locating the device in the map; using the parameters to ascertain the driving function, if the movable device is in the first region of the map; using the at least one value of the swarm behavior to ascertain the driving function, if the movable device is in the second region of the map; controlling the device with the ascertained driving function. A method for creating a map for ascertaining a driving function of a movable device, a computing unit, a computer program, and a machine-readable storage medium, are also described.