Robot Return-to-Base Navigation Using Preset Paths and Guidance Signals
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Solution Overview
Problem
Intelligent robots face challenges in accurately returning to their bases due to varying methods and efficiencies, leading to instances of low accuracy and failure in returning effectively.
Innovation Solution
The method involves the robot receiving a return-to-base control signal and walking along different preset paths based on the receiving conditions of guidance signals, with the ability to directly return to the base upon detecting an intermediate signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot walks along preset paths to return to base, then the robot can systematically search for the base, but the return time increases and efficiency decreases
Solution Approach 1:
The robot utilizes the guidance signals emitted by the charging base itself to navigate back. The base actively provides directional information through left/right/guard rail signals, and the robot uses this information to self-correct its path, combining systematic path following with direct signal-responsive navigation to reduce return time while maintaining reliability.
Solution Approach 2:
The robot continuously detects guidance signals from the base and adjusts its navigation based on feedback. When detecting left/right signals, the robot modifies its path accordingly; when detecting guard rail signals, it knows it's near the base and can switch to a different path strategy, creating a feedback loop that optimizes return time based on real-time signal conditions.
2Device complexity
If the robot uses simple return methods, then the device complexity is low, but the accuracy of returning to base decreases
Solution Approach 1:
The robot dynamically switches between different preset paths based on real-time detection of guidance signals. The navigation system is not static but adapts its behavior according to the detected signal type (left signal, right signal, guard rail signal), allowing accurate return without requiring overly complex fixed-path planning algorithms.
Solution Approach 2:
The return journey is divided into multiple preset paths (first preset path, second preset path, third preset path), each optimized for different signal conditions. This segmentation allows the robot to use simpler navigation logic for each segment while achieving high overall accuracy through the combination of multiple path segments.
3Measurement precision
If the robot continuously searches for guidance signals, then the return accuracy improves, but the energy consumption increases
Solution Approach 1:
The robot performs periodic detection of guidance signals while following preset paths, rather than continuous intensive searching. The systematic path following provides periodic opportunities to detect signals, reducing energy consumption while maintaining detection accuracy through the structured nature of the search pattern.
Solution Approach 2:
The robot follows preset paths that are pre-calculated to pass through areas where guidance signals are most likely to be detected. This preliminary preparation of navigation routes optimizes the detection process, allowing the robot to find the base accurately without requiring excessive continuous searching and energy consumption.
Data Source
AI summary
The disclosure relates to a method for controlling a robot to return to a base. The method includes the robot receives a return-to-base control signal; the robot walks along different preset paths according to different receiving conditions of guidance signals; and when detecting an intermediate signal during walking along a preset path, the robot returns to the base directly under piloting of the intermediate signal instead of walking along the preset path. The guidance signals are signals sent by a charging base, for piloting the robot to return to the base, and include the intermediate signal.