Dynamic Proximity Thresholds for Robotic Object Avoidance
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Solution Overview
Problem
Robotic vehicles face challenges in navigating and avoiding obstacles due to unpredictable environmental conditions and object movements, as existing fixed proximity thresholds are insufficient in ensuring safe and precise maneuvering.
Innovation Solution
The robotic vehicle's processor dynamically adjusts the proximity threshold based on environmental conditions and object classification, increasing the threshold for unpredictable objects and conditions to ensure safe navigation and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed proximity threshold is used in the collision avoidance system, then the system is simple to operate and has low computational complexity, but it cannot adapt to unpredictable environmental conditions and object movements, reducing safety and reliability
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed proximity threshold to a dynamically adjustable threshold that changes in real-time based on environmental conditions and object classification. The processor continuously monitors conditions and adjusts the threshold accordingly, making the system adaptive to unpredictable variations in the operating environment.
Solution Approach 2:
The patent implements parameter changes by modifying the proximity threshold parameter based on detected environmental conditions and object classifications. The system changes the threshold value dynamically rather than using a constant value, allowing adaptation to different scenarios such as varying lighting, weather conditions, and object types.
2Adaptability or versatility
If a fixed proximity threshold is used, then the computational requirements and processing complexity are low, but the system lacks adaptability to different environmental conditions and object types
Solution Approach 1:
The system changes the proximity threshold parameter dynamically based on environmental conditions and object classifications, enabling adaptability to different scenarios while maintaining a relatively simple adjustment mechanism through processor-based control.
Solution Approach 2:
The patent implements feedback by continuously monitoring environmental conditions and object positions, then using this information to adjust the proximity threshold. The system creates a closed-loop control mechanism where detection results feed back into threshold adjustment decisions, improving adaptability through real-time responsiveness.
3Reliability
If the proximity threshold is increased to account for unpredictable conditions, then collision safety improves, but the robotic vehicle's maneuvering flexibility and operational efficiency decrease
Solution Approach 1:
The patent applies dynamics by making the proximity threshold adjustable rather than fixed, allowing the system to increase the threshold when safety is concerned and decrease it when conditions permit, thereby maintaining both safety and maneuvering efficiency through real-time adaptation.
Solution Approach 2:
The system dynamically changes the proximity threshold parameter based on current conditions, increasing it when unpredictable conditions or animate objects are detected to improve safety, and decreasing it when conditions are favorable to maintain maneuvering efficiency and productivity.
4Reliability
If environmental monitoring and dynamic threshold adjustment are implemented, then collision avoidance safety and adaptability improve, but the computational load and processing requirements increase
Solution Approach 1:
The patent implements parameter changes by adjusting the proximity threshold based on environmental conditions and object classifications, enabling the system to improve safety through intelligent adaptation while managing computational resources efficiently through targeted processing.
Solution Approach 2:
The system uses feedback from environmental sensors and object detection to dynamically adjust the proximity threshold, creating an efficient closed-loop control mechanism that improves safety while optimizing energy usage by processing information only when necessary for threshold adjustment decisions.
Data Source
Figure 1A~1B
Figure 1C
Figure 2
AI summary
Various embodiments include methods, devices, and robotic vehicle processing devices implementing such methods for automatically adjusting the minimum distance that a robotic vehicle is permitted to approach an object by a collision avoidance system (the "proximity threshold") to compensate for unpredictability in environmental or other conditions that may compromise control or navigation of the robotic vehicle, and/or to accommodate movement unpredictability of the object. Some embodiments enable dynamic adjustments to the proximity threshold to compensate for changes in environmental and other conditions. Some embodiments include path planning that takes into account unpredictability in environmental or other conditions plus movement unpredictability of objects in the environment.