Robot Obstacle Response Control for User Interaction Retention
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Solution Overview
Problem
Existing robot technologies do not effectively increase opportunities for communication between robots and users while avoiding obstacles, as they primarily focus on avoidance behaviors without considering the type of obstacle, which can lead to reduced user affinity and interaction.
Innovation Solution
A robot equipped with sensors and a processor that determines whether to perform interaction-increasing behaviors or obstacle-avoidance behaviors based on the type of obstacle detected, using a memory to store various behavior types and execute appropriate actions to maximize user interaction while safely navigating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot performs avoidance behavior for all detected obstacles, then collision risk is reduced, but communication opportunities with users decrease
Solution Approach 1:
The robot applies different avoidance behaviors based on the local quality of the detected obstacle. When a user is detected, the robot performs small avoidance while maintaining communication. When a non-user obstacle is detected, the robot performs large avoidance. This localized differentiation resolves the contradiction by tailoring the avoidance intensity to the specific obstacle type.
Solution Approach 2:
The robot dynamically adjusts its avoidance behavior based on real-time obstacle identification. The system transitions between different behavior modes (small avoidance for users, large avoidance for non-users) depending on the detected obstacle type, allowing flexible optimization of both safety and communication opportunities.
2Reliability
If the robot makes large avoidance for all obstacles, then safety is improved, but user affinity and interaction opportunities decrease
Solution Approach 1:
The robot differentiates its avoidance response based on the specific obstacle type. For user obstacles, it applies small avoidance to maintain affinity. For non-user obstacles, it applies large avoidance to ensure safety. This localized quality adjustment resolves the contradiction between safety and user affinity.
Solution Approach 2:
The system changes the avoidance parameter (avoidance magnitude) based on the obstacle type. The avoidance magnitude is adjusted from large (for non-users) to small (for users), optimizing both safety and user affinity through parameter adaptation.
3Reliability
If the robot focuses on obstacle avoidance, then collision prevention is achieved, but interaction opportunities with users are reduced
Solution Approach 1:
The robot dynamically switches between avoidance intensities based on real-time obstacle classification. The system transitions from large avoidance mode (for non-users) to small avoidance mode (for users), dynamically optimizing the balance between collision prevention and interaction opportunities.
Solution Approach 2:
The obstacle avoidance function is segmented into different behavior types based on obstacle classification. The system divides avoidance into small avoidance for users and large avoidance for non-users, allowing simultaneous optimization of collision prevention and interaction opportunities through segmented behavior strategies.
Data Source
AI summary
A processor determines, by referring to a memory, which of a plurality of behavior types a behavior type executed by a robot when a first sensor detects an obstacle is; determines a type of the obstacle detected by the first sensor; decides whether first behavior for increasing opportunities of interaction with the user or second behavior for handling the obstacle is performed, based on the behavior type executed by the robot when the first sensor detects the obstacle and the type of the obstacle detected by the first sensor; and controls the robot to cause the robot to execute the decided behavior.