Random Time Delay Effect on Out-of-Sequence Measurements

“…The mean transmission delay was evaluated by a curve with 95% of fitness, revealing that the transmission delay increased to 500ms when the number of agents reached 130. This delay is unacceptable for general applications like the out-of-sequence measurement (OOSM) [20]. In addition, the delay surpassed 5,000ms when the agent count reached 220, and surged up to 10 5 ms level when the latter was 380, which is unacceptable for general control.…”

Section: Figure 9 Mean Transmission Delay Vs the Number Of Agentsmentioning

confidence: 99%

Design and Performance Verification of an Optimized Multi-agent System

Zhang¹,

Li²,

Zhi-hao³

et al. 2019

EJEE

Self Cite

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The multi-agent system (MAS) mainly focuses on group control and network control. The existing studies on the MAS have not clearly defined the system performance, especially network saturation, agent load capacity and data transfer delay. To solve the problem, this paper develops a basic MAS communication framework, and proposes the optimized serial line Internet protocol (O-SLIP). The protocol can be deployed in any agent object and any layer, unifying the communication between the objects in the MAS system, and lays the basis for group control, consistency check, etc. Then, the proposed framework and the O-SLIP were verified through tests on network saturation, agent load capacity and transmission delay. Finally, the author summed up the important issues in the selection and design of MAS communication systems.In summary, many algorithms and control methods have been developed for groups (i.e. swarms or collectives) of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots. However, they are generally validated in simulation only, or on a limited number of robots, for reasons of cost, time, or complexity. The network architecture and its organization are explained in terms of components. What is worse, there is a lack of a widely applicable, protocol-wide communication protocol across the MAS compartment. The performance of the MAS and its communication has not been fully described and discussed, especially under multiple agents. To overcome these problems, this paper designs a communication framework and an optimized serial line Internet protocol (O-SLIP) for the BigPan series MAS system.The remainder of this paper is organized as follows: Section 2 introduces the basic requirements on the MAS design, reviews the main techniques of wireless communication, and explains the reasons for choosing WiFi; Section 3 designs the basic communication network, highlighting the system architecture, socket-based connection, the O-SLIP, as well as message encapsulation and parsing; Section 4 sets up an MAS communication system and details the two key processes: agent registration-logout and terminal control; Section 5 verifies the performance of the proposed system in terms of network saturation, agent load capacity and data transmission delay; Section 6 wraps up this paper with several conclusions. MAS WIRELESS NETWORK ENVIRONMENTThis paper mainly considers common low-power wireless communication techniques like Bluetooth and Wi-Fi. This is because low power consumption is conducive to the service life of the system, while the power and frequency bands of wireless communication are restricted by national laws and regulations. General civil license requires that the power consumption of wireless communication system should fall between 100mW and 500mW.

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“…Then, one may ask what factors determine whether an observation should be discarded. A variety of experiments have demonstrated that increasing randomness in an object’s motion, decreasing observation noise, and increasing the amount of delay of an observation all decrease its contribution to estimation accuracy [15,16,17]. The experiments of [15] suggest that the impact of an observation decreases exponentially with its delay, but it is still obscure why the decrease would be exponential when the motion and measurement uncertainties increase only linearly or quadratically.…”

Section: Introductionmentioning

confidence: 99%

“…Dropping some old observations improves computational efficiency while maintaining nearly-optimal performance [15,16,17]. …”

Section: Introductionmentioning

confidence: 99%

Exploring the Exponentially Decaying Merit of an Out-of-Sequence Observation

Yoder

Baek

Kwon

et al. 2018

Sensors

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It is well known that in a Kalman filtering framework, all sensor observations or measurements contribute toward improving the accuracy of state estimation, but, as observations become older, their impact toward improving estimations becomes smaller to the point that they offer no practical benefit. In this paper, we provide an practical technique for determining the merit of an old observation using system parameters. We demonstrate that the benefit provided by an old observation decreases exponentially with the number of observations captured and processed after it. To quantify the merit of an old observation, we use the filter gain for the delayed observation, found by re-processing all past measurements between the delayed observation and the current time estimate, a high cost task. We demonstrate the value of the proposed technique to system designers using both nearly-constant position (random walk) and nearly-constant velocity (discrete white-noise acceleration, DWNA) cases. In these cases, the merit (that is, gain) of an old observation can be computed in closed-form without iteration. The analysis technique incorporates the state transition function, the observation function, the state transition noise, and the observation noise to quantify the merit of an old observation. Numerical simulations demonstrate the accuracy of these predictions even when measurements arrive randomly according to a Poisson distribution. Simulations confirm that our approach correctly predicts which observations increase estimation accuracy based on their delay by comparing a single-step out-of-sequence Kalman filter with a selective version that drops out-of-sequence observations. This approach may be used in system design to evaluate feasibility of a multi-agent target tracking system, and when selecting system parameters including sensor rates and network latencies.

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Random Time Delay Effect on Out-of-Sequence Measurements

Cited by 8 publications

References 30 publications

Depth Data Reconstruction Based on Gaussian Mixture Model

Depth Data Reconstruction Based on Gaussian Mixture Model

Design and Performance Verification of an Optimized Multi-agent System

Exploring the Exponentially Decaying Merit of an Out-of-Sequence Observation

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