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Based on this, a neuromodulatory neural network model was proposed and it was utilized as a mobile robot controller.
By studying various complex applications – including engineering optimization, power systems fault diagnosis, mobile robot controller design, and complex biological systems involving data modeling and process interactions – the book also extends the capabilities of membrane systems models with features such as formal verification techniques, evolutionary approaches, and fuzzy reasoning methods.
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Adaptation of mobile robot controllers commonly requires the computation of optimal points of operation.
Both simulation and experimental results on a PIONEER 2DX mobile robot are presented, showing a good performance of the four proposed mobile robot controllers.
This paper describes how adequate hardware implementations of the Particle Swarm Optimization (PSO) algorithm can be useful for real time adaptation of mobile robot controllers.
In control theory, some industrial application results were presented in [4], and the type 2 fuzzy logic controller (FLC) was applied to three domains: industrial control, mobile robot control, and ambient intelligent environment control.
Mobile robot and controller communicate with Wi-Fi (Wireless Fidelity) communication (socket communication).
Because SNNs convey both temporal and spatial information, they are more suitable for mobile robots׳ controller design.
To satisfy these special demands in the design of an autonomous mobile robot, a motion controller, based on the dynamical behavior of a known discrete chaotic system, the Logistic map, is presented in this paper.
Android operating system based tablet computers (controller) control mobile robot remotely and one android interface program is designed for remote control.
Hurdle avoidance controller ensures collision-free motion of mobile robot, while goal reaching controller helps the mobile robot in reaching the destination.
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