Autonomous Robot Motion Prediction for Adaptive Path Planning
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Solution Overview
Problem
Autonomous robotic devices face challenges in accurately predicting their motion and navigating environments due to limitations in mathematical modeling and parameter estimation, which affects their ability to operate effectively.
Innovation Solution
A robotic device equipped with a chassis, wheels, motors, sensors, a camera, and processors that captures spatial data, generates movement paths, predicts new states using a motion model based on previous states and sensor readings, and updates paths to exclude previously predicted locations, employing techniques such as recurrent neural networks and Kalman filters for improved prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mathematical modeling is used to predict robotic device motion, then the system structure remains simple, but prediction accuracy deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/mathematical motion prediction models with a machine learning-based prediction system. The robotic device uses sensors to collect motion data and employs machine learning algorithms to predict future positions and orientations, substituting the conventional physics-based mathematical modeling approach with a data-driven intelligent system that achieves higher prediction accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the robotic device continuously collects sensor data about its actual motion, compares predicted positions with actual positions, and uses this feedback to refine and update its motion prediction model. This closed-loop feedback system allows the device to adapt and improve prediction accuracy over time by learning from discrepancies between predicted and actual motion.
2Adaptability or versatility
If the robotic device follows a predetermined movement path, then navigation is simple, but adaptability to environmental changes deteriorates
Solution Approach 1:
The patent transforms the static, predetermined path planning approach into a dynamic, adaptive path generation system. The robotic device uses machine learning to continuously predict its future states and dynamically adjusts its movement path based on predicted positions, sensor readings, and environmental feedback. This dynamic approach allows the device to adapt to changing environments while maintaining manageable system complexity through intelligent algorithms.
3Measurement precision
If the robotic device uses frequent sensor updates for accurate prediction, then prediction accuracy improves, but energy consumption increases
Solution Approach 1:
The patent applies preliminary action by having the robotic device predict its future states in advance using machine learning models before actually executing movements or taking sensor readings. The device uses these predictions to plan upcoming actions and only performs sensor updates and model recalibrations when necessary, rather than continuously. This preliminary prediction capability allows the device to maintain high prediction accuracy while reducing frequent sensor updates and associated energy consumption.
Data Source
AI summary
Provided is a robotic device including a medium storing instructions that when executed by one or more processors effectuate operations including: capturing, with a camera, spatial data of surroundings; generating, with the one or more processors, a movement path based on the spatial data; predicting, with the one or more processors, a new predicted state of the robotic device including at least a predicted position of the robotic device, wherein predicting the new predicted state includes: capturing, with at least one sensor, movement readings of the robotic device; predicting, with the one or more processors, the new predicted state using a motion model of the robotic device based on a previous predicted state of the robotic device and the movement readings; and updating, with the one or more processors, the movement path to exclude locations of the movement path that the robotic device has previously been predicted to be positioned.