Robot Navigation Using Static Sensor Networks in Unknown Environments
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Solution Overview
Problem
Traditional sensor-network guided robot navigation in unknown environments is cumbersome, requiring hand-tuned parameters, pre-processing steps, and isolating perception, planning, and control modules, which hinders efficient navigation and is challenging to implement in new environments.
Innovation Solution
A two-stage machine learning approach is used to train a navigation system, where a neural network model predicts the direction to a target object using data from a network of static sensors, and then guides a navigating device to the target using reinforcement learning, without requiring global positioning information or pre-calibration, allowing for efficient navigation in various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor-network guided navigation is implemented, then navigation guidance can be provided, but the system requires hand-tuned parameters and pre-processing steps increasing complexity
Solution Approach 1:
The patent merges perception, planning, and control modules into a unified deep reinforcement learning framework. The DQN agent integrates target detection, path planning, and robot control into a single end-to-end learning system, eliminating the need for separate hand-tuned modules and reducing overall system complexity while maintaining navigation reliability.
Solution Approach 2:
The patent replaces traditional mechanical navigation systems (GPS, manual path planning, separate control modules) with an intelligent software-based deep reinforcement learning system. The DQN agent learns navigation policies through training on sensor data, substituting complex mechanical and algorithmic systems with a unified learning-based approach that adapts to environments without hand-tuning.
2Ease of manufacture
If perception, planning, and control modules are isolated, then each module can be independently developed, but positive feedback among modules is hindered and control becomes challenging
Solution Approach 1:
The patent combines isolated perception, planning, and control modules into a unified DQN framework where all functions are integrated within a single neural network architecture. The agent receives sensor inputs, processes them through shared layers, and outputs control commands, enabling seamless information flow and positive feedback among previously separate functions.
3Measurement precision
If GPS or anchor systems are used for position estimation, then accurate positioning can be achieved, but the system requires external infrastructure and cannot operate in GPS-denied environments
Solution Approach 1:
The patent enables the robot to estimate its own position and orientation using only onboard sensors and visual odometry algorithms, without relying on external GPS or anchor infrastructure. The system processes camera images to track feature points and compute robot pose, making the navigation system self-sufficient and adaptable to GPS-denied environments while maintaining positioning accuracy.
4Ease of operation
If pre-formulated dynamic models are used for control, then control commands can be computed, but the model requires accurate environmental knowledge and is difficult to adapt to new environments
Solution Approach 1:
The patent replaces static pre-formulated dynamic models with a dynamic deep reinforcement learning system that adapts to different environments through training. The DQN agent learns control policies that automatically adjust to environmental variations, obstacle configurations, and robot dynamics, providing both ease of operation through automated control and high adaptability to new environments without requiring manual model reconfiguration.
Data Source
AI summary
Broadly speaking, embodiments of the present techniques provide methods and systems for robot navigation in an unknown environment. In particular, the present techniques provide a navigation system comprising a navigating device and a sensor network comprising a plurality of static sensors. The sensor network is trained to predict a direction to a target object, and the navigating device is trained to reach the target object as efficiently as possible using information obtained from the sensor network.