Self-Learning Navigation Module for Autonomous Control Without Networks
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Solution Overview
Problem
Manned and unmanned vehicles face challenges in maneuvering and navigation, especially when communication with a base station is lost, as existing technologies do not effectively integrate operator instructions for autonomous operation.
Innovation Solution
The GENISYS navigation system, which is retro-fittable in various vehicles, captures and integrates operator instructions, including audio, manual, and electrical commands, converting them into executable data/algorithms, and utilizes a combination of sensors and intelligence for self-learning and autonomous navigation, even in the absence of communication networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If operator instructions are captured and integrated as self-learning algorithms, then autonomous navigation capability is improved, but device complexity increases
Solution Approach 1:
The GENISYS navigation system is divided into distinct functional modules: operator instruction capture module, self-learning algorithm integration module, sensor data processing module, and autonomous navigation control module. This segmentation allows each module to perform its specific function independently, managing complexity through modular architecture while achieving high autonomous navigation capability.
Solution Approach 2:
The patent introduces a self-learning algorithm as an intermediary layer between operator instructions and vehicle control systems. This intermediary processes and translates operator instructions into executable navigation algorithms, enabling autonomous operation without directly increasing the complexity of core control systems.
2Reliability
If the system operates without communication network, then reliability in loss of communication situations is improved, but loss of information increases
Solution Approach 1:
The system performs preliminary actions by capturing and storing operator instructions as self-learning algorithms before communication is lost. The GENISYS system pre-processes operator inputs during periods of communication availability, converting them into autonomous navigation algorithms that can operate independently when communication networks fail, thus maintaining reliability without losing critical operational information.
Solution Approach 2:
The navigation system implements self-service capabilities through self-learning algorithms that enable the vehicle to autonomously process sensor data and make navigation decisions without external communication. The system serves itself by internally processing information and executing decisions based on learned algorithms, eliminating dependence on continuous communication networks.
3Adaptability or versatility
If multiple types of operator instructions are captured and integrated, then adaptability is improved, but device complexity increases
Solution Approach 1:
The GENISYS navigation system implements a universal instruction capture mechanism that handles multiple types of operator inputs (audio commands, manual controls, electrical signals, radio commands) through a single integrated interface. This multi-functional approach allows the system to adapt to various input types without requiring separate processing systems for each instruction type, thereby improving adaptability while managing complexity through consolidation.
Data Source
AI summary
Navigation system (300) for land, air, marine or submarine vehicle (302), comprising a remote control workstation (301) with Manual control mode (310), Mission Planning mode (330) and tactical control mode (360) initiating command-and-control options; a navigation module (100) retrofittably disposed on the vehicle (302); a plurality of perception sensors (318) disposed on the vehicle (302); the system (300) receives manual, electrical, radio and audio commands of human operator (305) in the manual control (310) and mission planning mode (330) and converts them to dataset for training a navigation model having a navigational algorithm. The perception sensors (318) generate dataset for self-learning in real time in manual control mode (310), mission control mode (330) and tactical control mode (360); the navigational system (300) autonomously navigates with presence of communication network (390) and in absence of communication network (390).