Two-Wheeled Robot Balance Gain Control on Stairs and Steps
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Solution Overview
Problem
Robots with two-wheeled structures face challenges in maintaining balance and require adaptive control gains for different driving situations, such as flat ground, stairs, and steps, which existing technologies struggle to address automatically.
Innovation Solution
A robot control apparatus and method that utilize at least one first sensor on the front and one second sensor on the rear of the robot to sense ground distances, allowing a controller to determine the driving environment and automatically adjust the control gain for balance control, eliminating the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of control gain is used for balance control, then control accuracy can be optimized for specific situations, but operation complexity increases and automation is reduced
Solution Approach 1:
The robot control apparatus automatically adjusts control gains for balance control based on driving environment detection, eliminating the need for manual intervention. The system serves itself by autonomously selecting appropriate control parameters according to the detected environment (flat ground, stairs, steps), thereby resolving the contradiction between control accuracy and ease of operation.
Solution Approach 2:
The control gain parameter is dynamically changed based on the detected driving environment. The controller automatically selects different control gain values corresponding to different environments (flat ground, stairs, steps), enabling accurate balance control without manual adjustment while adapting to varying operational conditions.
2Measurement precision
If complex sensors such as lidar, camera, or radar are used for driving environment recognition, then detection precision improves, but device complexity and cost increase
Solution Approach 1:
The distance sensor performs multiple functions: it detects both the distance to the ground and infers the driving environment type (flat ground, stairs, steps). By making the simple distance sensor multi-functional, the system achieves accurate environment recognition without requiring complex sensors like lidar, camera, or radar, thus resolving the contradiction between detection precision and device complexity.
Solution Approach 2:
Instead of using complex sensors to directly detect environment features, the system uses a simple distance sensor to measure ground distance and creates an indirect representation (copy) of the driving environment. This copied information is then used to determine the environment type, achieving accurate detection with minimal hardware complexity.
3Stability of the object's composition
If different control gains are applied for different driving situations, then balance control stability improves, but control system complexity increases
Solution Approach 1:
The control gain is made dynamic rather than static. The controller automatically adjusts the control gain based on the detected driving environment, transitioning between different gain values as the robot moves through different terrains. This dynamic adaptation improves balance control stability without requiring a overly complex control system architecture.
Solution Approach 2:
The control gain parameter is changed according to the driving environment detected by the distance sensor. The system maintains simplicity by using a single sensor to trigger parameter changes in the controller, achieving stable balance control across different situations without complex control system modifications.
4Extent of automation
If automatic control gain adjustment is implemented, then automation level increases and manual operation is reduced, but control algorithm complexity increases
Solution Approach 1:
The control system automatically adjusts control gains based on environment detection without human intervention. The distance sensor continuously monitors ground distance, and the controller autonomously selects appropriate control parameters,实现ing high automation with relatively simple algorithms that map distance measurements to control gain selections.
Solution Approach 2:
The system uses feedback from the distance sensor to automatically adjust control gains. The sensor provides continuous information about ground distance, and the controller uses this feedback to select appropriate control parameters, creating a closed-loop automatic control system with manageable algorithmic complexity.
Data Source
AI summary
In an embodiment a robot includes a wheel part including a driving wheel, a head part arranged above the wheel part, the head part providing an inner loading space, at least one first sensor disposed on a front bottom of the head part and configured to sense a first distance from a ground, at least one second sensor disposed on a rear bottom of the head part and configured to sense a second distance from the ground and a controller configured to determine a driving environment of the robot based on at least one of the first distance or the second distance and to adjust a control gain related to a balance control of the robot based on the determined driving environment.