Mobile Robot Path Control Using Predicted Obstacle Motion
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Solution Overview
Problem
Existing robot control systems struggle to efficiently navigate around obstacles, particularly when encountering people or other moving objects, which can lead to reduced movement speed or evasive maneuvers, impacting overall conveyance efficiency.
Innovation Solution
A robot control system that utilizes a range sensor to measure distances to nearby objects, estimates their movement vectors, and updates a cost map to guide the robot's movement, allowing it to efficiently avoid obstacles while maintaining speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot reduces moving speed or performs evasive movement when detecting an obstacle, then collision avoidance is improved, but conveyance efficiency deteriorates
Solution Approach 1:
The system performs preliminary action by predicting the future position of moving objects before the robot reaches that area. The prediction unit calculates where a person or object will be in the future based on current movement data, and the path planning unit proactively adjusts the route to avoid the predicted position, preventing the need for reactive speed reduction or evasive maneuvers.
Solution Approach 2:
The system applies dynamics by continuously updating the prediction of moving object positions and dynamically adjusting the path plan in real-time. The movement vector estimation and prediction update based on changing distances and positions detected by the range sensor, allowing the robot to adapt its trajectory dynamically while maintaining efficient movement.
2Productivity
If the robot maintains high moving speed, then conveyance efficiency is improved, but collision risk with moving objects increases
Solution Approach 1:
The system performs preliminary action by predicting the future position of moving objects before the robot reaches that area. The prediction unit calculates where a person or object will be in the future based on current movement data, and the path planning unit proactively adjusts the route to avoid the predicted position, preventing the need for reactive speed reduction or evasive maneuvers.
Solution Approach 2:
The system implements feedback by continuously monitoring the distance to moving objects via the range sensor, updating the movement vector estimation, and adjusting the path plan based on this feedback loop. This real-time feedback enables the robot to maintain high speed while dynamically responding to moving objects' trajectories.
3Reliability
If the robot performs frequent evasive movements, then collision avoidance is improved, but travel time increases
Solution Approach 1:
The system performs preliminary action by predicting the future position of moving objects before the robot reaches that area. The prediction unit calculates where a person or object will be in the future based on current movement data, and the path planning unit proactively adjusts the route to avoid the predicted position, preventing the need for reactive speed reduction or evasive maneuvers.
Solution Approach 2:
The system applies dynamics by continuously updating the prediction of moving object positions and dynamically adjusting the path plan in real-time. The movement vector estimation and prediction update based on changing distances and positions detected by the range sensor, allowing the robot to adapt its trajectory dynamically while maintaining efficient movement.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables robots to move more efficiently by predicting the movement of nearby objects, such as people, and adjusting its path accordingly, thereby reducing travel time and improving conveyance efficiency.
Implementation Method 1
acquire a distance to a nearby object measured by using a range sensor
Data Source
AI summary
A robot control system according to an embodiment is configured to control a mobile robot configured to autonomously move by referring to a map, the robot control system being further configured to: acquire a distance to a nearby object measured by using a range sensor; specify a position of the nearby object on the map according to the distance from a position of the mobile robot to the nearby object; estimate a movement vector indicating a moving speed and a moving direction of the nearby object according to a change in the distance to the nearby object; add a cost for restricting a movement of the mobile robot on the map; and perform control so that the mobile robot moves according to the cost updated according to a result of measurement by the range sensor.