Aircraft Teleoperation Latency Reduction via Time-Shifted Simulation
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Solution Overview
Problem
Remote control of devices like unmanned aerial systems (UAS) and vehicles faces significant latency issues, which limit rapid control inputs and increase operator error, especially in distant operations, and existing solutions either reduce system controllability by humans or impose penalties on weight, complexity, and cost.
Innovation Solution
An integrated simulation system with a time shift option is used, where pre-existing and real-time data are combined to create a virtual model of the environment, allowing operators to control the device with adjustable time delays and a 'rewind' option for error correction, reducing latency and operator errors while maintaining human control authority.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If control processing tasks are moved onboard the device, then latency is reduced and response speed is improved, but system complexity, weight, and cost increase
Solution Approach 1:
The control system is segmented into multiple components: onboard autonomous control for immediate responses, teleoperation interface for human oversight, and simulated environment for training. This segmentation allows the system to achieve fast response times through local processing while distributing complexity across separate modules rather than concentrating all functions in one complex onboard system.
Solution Approach 2:
The simulated environment pre-computes control strategies and scenarios before actual operation. By preparing control algorithms and response patterns in advance through simulation, the system reduces the computational burden during real-time operation, thereby reducing latency without requiring excessively complex onboard processing hardware.
2Speed
If control processing tasks are moved onboard the device, then latency is reduced, but the extent of human control authority is reduced
Solution Approach 1:
The control architecture dynamically adjusts the balance between autonomous operation and human control based on operational context. The system can switch between fully autonomous mode for routine tasks, teleoperation mode for complex scenarios, and hybrid mode for transitional situations. This dynamic flexibility ensures fast response times while preserving human authority when needed.
Solution Approach 2:
The teleoperation interface provides continuous feedback to the operator about system state and autonomous decisions. This feedback loop maintains human control authority by keeping the operator informed and able to intervene, while the autonomous system handles time-critical responses within the feedback cycle.
3Reliability
If a margin of safety is operated to reduce operator error, then reliability is improved, but productivity decreases
Solution Approach 1:
The autonomous control system performs self-monitoring and self-correction of operational parameters to maintain safety margins. By automating safety-critical functions such as collision avoidance and parameter boundary enforcement, the system reduces reliance on operator vigilance while maintaining high productivity, as the autonomous system operates continuously without fatigue or distraction.
Solution Approach 2:
The simulated environment pre-trains control algorithms on edge cases and failure scenarios before deployment. This beforehand preparation builds robustness into the autonomous system, reducing operator error potential while maintaining productivity, as the system has already learned safe operating procedures through virtual practice.
4Ease of operation
If terrain following operation is performed with limited vertical acceleration capability, then ease of operation is improved, but manufacturing precision of flight path decreases
Solution Approach 1:
The system pre-computes terrain-following trajectories in the simulated environment, accounting for vertical acceleration constraints and terrain complexity. By planning the flight path in advance with full knowledge of vehicle capabilities and terrain features, the system achieves precise flight paths without requiring complex real-time control adjustments, thereby maintaining both ease of operation and manufacturing precision.
Data Source
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AI summary
A method to control a device may include forming an integrated simulation model of an actual environment in which the device is or will be operating. The integrated simulation model may be formed using pre-existing data and real-time data related to the actual environment. The method may also include presenting a simulation including a representation of the device operable in the integrated simulation model of the actual environment and allowing control of operation of the simulation of the device in the integrated simulation model of the actual environment. The method may further include controlling operation of the device in the actual environment using the simulation of the representation of the device in the integrated simulation model of the actual environment.