Robot Skid Detection Using Grid Maps for Cliff-Aware Walking
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Solution Overview
Problem
Current robot walking prediction and control methods rely heavily on expensive hardware for obstacle detection, leading to inefficiencies and potential hazards like cliff falls or repeated entry into dangerous regions due to lack of predictive capabilities.
Innovation Solution
A method involving the construction of a grid map with dynamically detected units, where a dynamic detection model predicts the forward path condition of a robot, allowing for controlled walking modes based on predicted path statuses, thereby avoiding obstacles and cliffs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If infrared detection hardware is used for obstacle prediction, then prediction capability is improved, but hardware cost increases
Solution Approach 1:
The patent creates a virtual copy of the physical environment through grid map representation, where each grid cell stores status information about obstacles and terrain. This digital model allows the robot to predict and navigate without requiring expensive physical sensors like infrared detectors, achieving prediction capability through software-based environmental modeling instead of hardware copying
Solution Approach 2:
The patent replaces mechanical/optical detection systems (infrared sensors, visual cameras) with an information-processing system based on grid maps and status markers. Instead of using physical sensors to detect obstacles, the system uses logical operations on grid cell data to predict dangerous regions, substituting mechanical detection with computational analysis
2Device complexity
If no prediction about forward condition is made, then hardware cost is reduced, but robot may repeatedly enter dangerous regions or fall off cliffs
Solution Approach 1:
The patent performs preliminary marking of grid cells with status information (obstacle presence, cliff detection, dangerous region flags) before the robot actually encounters them. By pre-processing environmental data and storing it in the grid map, the system enables predictive navigation that prevents the robot from entering dangerous regions or falling off cliffs, achieving safety through advance information preparation rather than reactive sensing
Solution Approach 2:
The patent introduces a grid map as an intermediary data structure between the physical environment and the robot's navigation system. This grid map acts as a mediator that stores processed environmental information (obstacles, cliffs, dangerous regions) and provides predictive guidance to the robot, eliminating the need for expensive direct sensing hardware while maintaining safety through information mediation
Data Source
AI summary
The disclosure relates to a method for predicting and controlling robot walking. The method includes the following steps: constructing a grid map based on grid units marked with a status; establishing a dynamic detection model with a current location of a robot as a reference point based on the grid map; predicting a forward path condition of the robot based on the dynamic detection model; and controlling a walking mode of the robot based on the prediction result.