Remote Mobile Object Control Under Time-Varying Network Latency
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Solution Overview
Problem
Existing remote control systems for mobile objects in large-scaled networks fail to account for time-varying probability distributions of transmission latencies, leading to unstable behavior due to simplified assumptions of independent and identical distributions, which are not valid in dynamic network environments.
Innovation Solution
A remote control apparatus that estimates time-changed probability distributions of transmission latencies, generates target trajectories based on surrounding information, and sets control gains to stabilize mobile objects, using a transmission latency distribution estimation unit, trajectory generating unit, and mobile object control unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If transmission latencies are assumed to follow independent and identical distribution (i.i.d.) for simplified control gain design, then the control system design becomes simpler, but the control stability deteriorates in large-scaled networks where latency distributions are time-varying
Solution Approach 1:
The patent applies dynamics by transitioning from a static i.i.d. latency assumption to a dynamic time-varying latency distribution model. The control gain is designed to adapt to changing latency characteristics in large-scaled networks, where the probability distribution of transmission latencies varies over time due to network conditions, obstacles, and distance factors.
Solution Approach 2:
The patent changes the parameters of the latency model from fixed i.i.d. distribution parameters to time-varying distribution parameters. By modeling latency as a stochastic process with time-dependent probability distributions, the system can adjust control gains to match actual network conditions, resolving the contradiction between design simplicity and control stability.
2Ease of operation
If time-invariant latency distribution assumptions are used, then mathematical manipulation of latencies is simplified, but the accuracy of control predictions deteriorates in dynamic network environments
Solution Approach 1:
The patent introduces dynamics into the latency model by using time-varying probability distributions instead of static distributions. This allows the system to capture the evolving nature of network latencies in large-scaled environments while maintaining mathematical tractability through stochastic process theory.
Solution Approach 2:
The patent implements feedback by continuously monitoring actual transmission latencies and updating the time-varying distribution model accordingly. This feedback mechanism enables the system to adapt to changing network conditions, improving prediction accuracy while maintaining a structured mathematical framework for control design.
3Reliability
If small-scaled network assumptions are made, then the i.i.d. latency hypothesis holds and control stability is maintained, but the system cannot handle large-scaled networks with frequent path changes
Solution Approach 1:
The patent creates a universal control framework that works across different network scales. By using time-varying stochastic models instead of scale-specific assumptions, the system can handle both small-scaled and large-scaled networks, adapting to the specific characteristics of each environment while maintaining control stability.
Solution Approach 2:
The patent changes the fundamental parameters of the latency model from scale-dependent i.i.d. assumptions to scale-independent time-varying distributions. This parameter transformation enables the system to maintain control stability across different network scales, from small local networks to large Internet-scale networks with frequent path changes.
Data Source
AI summary
A remote control apparatus controls at least one mobile object, and includes: a transmission latency distribution estimation unit estimating transmission latency distribution information based on transmission latency information of a transmission path obtained in advance and transmission latency information obtained online, the transmission latency distribution information including a time-changed probability distribution of transmission latencies; a trajectory generating unit generating a target trajectory of the mobile object, based on surrounding information around the mobile object; and a mobile object control unit generating a controlled amount of the mobile object, based on the transmission latency distribution information, the target trajectory, and mobile object information, wherein the mobile object control unit includes: a gain setting unit setting a control gain, based on the transmission latency distribution information; and a controlled amount computation unit generating the controlled amount, based on the target trajectory, the control gain, and the mobile object information.