Retrofitted GSE Autonomy Assembly for Collision-Aware Path Control
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Solution Overview
Problem
There is a need for enhanced systems and methods to retrofit logistics ground support equipment (GSE) for improved autonomous operation, particularly to prevent collisions and enhance performance in busy and hazardous air operations environments, while reducing false positives and improving maintenance monitoring.
Innovation Solution
The retrofitting of cargo tug control systems with control actuators, sensors, and data acquisition systems, including proprioceptive and exteroceptive sensors, and a control system that uses vehicle dynamics and data preprocessing processors to optimize paths and control motion, leveraging contextual environmental data and adaptive control systems for improved engine performance and spatial awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing logistics ground support equipment is retrofitted with sensors and control systems for autonomous operation, then the level of automation is improved, but the device complexity increases
Solution Approach 1:
The autonomous operation system is segmented into distinct functional modules: proprioceptive sensors for internal state monitoring, exteroceptive sensors for environmental perception, control processors for decision-making, and actuators for execution. This modular segmentation allows each component to be independently optimized and maintained, managing overall system complexity while achieving high automation.
Solution Approach 2:
The retrofitted control system is designed with multi-functional capabilities that can perform multiple tasks: path optimization, collision avoidance, engine performance monitoring, and maintenance prediction. This universality reduces the need for separate specialized systems, thereby improving automation without proportionally increasing complexity.
2Measurement precision
If retrofitted sensors and control systems are added to monitor operating parameters and environment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Multiple sensor types (proprioceptive and exteroceptive) are merged into a unified sensing system that feeds into a single control processor. This consolidation integrates diverse measurement functions while managing complexity through centralized data processing and unified control architecture.
Solution Approach 2:
The sensor system implements continuous feedback loops where proprioceptive sensors monitor internal vehicle state and exteroceptive sensors monitor environmental conditions. This feedback enables real-time measurement and adjustment of operating parameters, improving precision while using standardized sensor-controller interfaces to manage complexity.
3Extent of automation
If control actuators are added to autonomously alter motion and control elements, then the extent of automation is improved, but ease of operation deteriorates
Solution Approach 1:
The control system is designed to autonomously manage motion control without requiring manual intervention. The actuators self-regulate vehicle movement, steering, and operational elements based on sensor input and pre-programmed logic, enabling the system to serve itself and reducing the operational burden on human operators.
Solution Approach 2:
The control system dynamically adjusts actuator commands based on real-time sensor data and changing operational conditions. This dynamic control allows the system to adapt to varying situations automatically, improving autonomous operation while maintaining ease of use through intelligent adaptation rather than rigid control protocols.
4Productivity
If data preprocessing and path optimization are performed using control processors, then productivity is improved, but use of energy increases
Solution Approach 1:
The control processors perform data preprocessing and path optimization at selective intervals and only when necessary, rather than continuously. This partial action approach processes critical data in real-time while deferring or simplifying less critical computations, improving productivity without proportionally increasing energy consumption.
Solution Approach 2:
The system dynamically adjusts processing parameters such as computation frequency, data sampling rates, and optimization complexity based on operational context. During high-productivity phases, processing intensity increases; during steady-state operation, processing is reduced. This parameter adaptation balances productivity gains with energy consumption constraints.
Data Source
AI summary
An improved retrofit assembly for ground support equipment providing autonomous operation of the logistics ground support equipment (GSE). The assembly has a refit control system attached to the GSE (with a vehicle dynamics control processor and a data preprocessing control processor), retrofitted proprioceptive sensors coupled to the vehicle dynamics control processor monitoring operating parameters and characteristics of the GSE, retrofitted exteroceptive sensors coupled to the data preprocessing control processor that monitor an exterior environment of the GSE, and refit actuators for different control elements on the GSE and to autonomously alter motion of the GSE. The refit control system programmatically receives sensor data from the proprioceptive sensors and exteroceptive sensors; optimizes a path for the GSE based upon the different sensor data; and activates at least one of the actuators according to the optimized path for the GSE.