Mobile Robot Velocity History Map for Collision Avoidance
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Solution Overview
Problem
Mobile robots face challenges in safely navigating environments with potential moving objects, such as people, as they cannot effectively identify and avoid collisions with objects that may emerge from blind spots or move unpredictably, leading to reduced efficiency and increased collision risks.
Innovation Solution
A mobile robot system that utilizes an outside world sensor to measure object positions, a traveling unit to displace its own position, and a control unit to control the robot's direction and velocity based on environment information and velocity history maps, identifying potential collision risk areas and adjusting its path to avoid collisions by recognizing past object movement patterns and velocities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the mobile robot travels far away from detected objects to avoid collision, then collision avoidance capability is improved, but movement efficiency deteriorates due to excessive conservative path planning
Solution Approach 1:
The system applies different safety strategies to different spatial regions by creating a velocity history map that divides the environment into high-risk and low-risk areas. The robot adjusts its movement based on local velocity statistics rather than applying uniform conservative behavior throughout the entire space, allowing efficient movement in safe regions while maintaining safety in regions where mobile objects have been detected.
2Reliability
If the mobile robot decelerates or stops frequently to avoid blind spots, then collision avoidance capability is improved, but movement efficiency deteriorates due to excessive conservative behavior
Solution Approach 1:
The system performs preliminary analysis of velocity history data to identify high-risk regions before the robot encounters potential hazards. By pre-processing velocity measurements and storing them in a velocity history map, the robot can make informed decisions about when to decelerate or stop based on historical patterns rather than reacting conservatively to all blind spots, reducing unnecessary stops and improving movement efficiency.
3Device complexity
If the mobile robot uses traditional obstacle detection methods, then simple obstacle avoidance is achieved, but inability to handle potential mobile objects from blind spots deteriorates safety
Solution Approach 1:
The system introduces a velocity history map as an intermediary data structure that accumulates and processes velocity information from multiple sensing events. This intermediary layer transforms raw sensor data into meaningful statistical patterns that reveal the presence and movement characteristics of mobile objects in blind spots, enabling the robot to detect potential hazards without requiring complex real-time tracking of each individual object.
Data Source
AI summary
A mobile robot system includes an outside world sensor, a traveling unit, and a control unit. The control unit is configured to provide a history map data. Based on environment information obtained from the outside world sensor, a mobile object appearance point at which no mobile body exists at present but there is the possibility that a mobile body may appear in the future is recognized. In recognizing a door as the mobile object appearance location, if part of an object being obtained from the outside world sensor and having a width equal to or greater than a predetermined width set in advance belongs to an object movement position recorded in the velocity history map, then the part is recognized as a door.