Collision Detection Training Data for Predictive Robot Avoidance
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Solution Overview
Problem
Existing robotic systems often react too slowly to collisions, and are unable to proactively avoid moving objects, leading to potential collisions and inefficiencies in navigation.
Innovation Solution
A computer-implemented method and system for generating training data sets for machine learning models, which involves detecting collisions using sensor data and packaging this data into training sets for predictive collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robotic device uses reactive collision avoidance with proximity sensors, then the device can detect objects nearby, but the device reacts too slowly and collides with objects
Solution Approach 1:
The system performs preliminary action by proactively predicting potential collision trajectories before collisions occur. The machine learning model analyzes sensor data to forecast future positions of dynamic objects and identifies potential collision paths, allowing the robotic device to take preventive action in advance rather than reacting after detection, thus eliminating the time loss associated with slow reactive responses.
Solution Approach 2:
The system replaces the simple mechanical proximity sensor detection with an intelligent machine learning-based prediction system. Instead of relying solely on physical sensor thresholds, the system uses trained neural networks to process sensor data and predict future collision scenarios, substituting the reactive mechanical response with proactive intelligent prediction to improve reliability while maintaining real-time performance.
2Reliability
If a robotic device operates in a reactive manner with simple sensors, then the device is easy to control, but the device cannot estimate trajectories of moving objects proactively
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a bridge between simple sensor inputs and complex trajectory prediction outputs. The trained neural network serves as an intelligent intermediary that processes raw sensor data and generates predicted collision scenarios, enabling proactive trajectory estimation without requiring complex sensor arrays or control systems, thus achieving enhanced capability while managing system complexity.
3Reliability
If collision data is collected and packaged into training sets, then machine learning models can be trained for prediction, but data processing and model training require additional time and resources
Solution Approach 1:
The system applies preliminary action by pre-collecting and packaging collision data into structured training sets during normal operation. This advance preparation of training data allows machine learning models to be trained offline before deployment, so that during real-time operation, the pre-trained models can quickly process sensor data and generate collision predictions without requiring time-consuming data processing, thus improving prediction accuracy while minimizing real-time processing time.
Data Source
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AI summary
An example method involves obtaining a log of sensor data indicative of an environment during a prior time period, with the log of sensor data including a sequence of image frames, and determining that the log of sensor data relates to a collision involving a physical object in the environment at a particular time within the prior time period. The method also involves, responsive to determining that the log of sensor data relates to the collision, generating a training data set for the collision from the log of sensor data. The training data set for the collision may include multiple image frames of the sequence of image frames that are prior to an image frame in the sequence of image frames that corresponds to the particular time.