Towards Behavior Control for Evolutionary Robot Based on RL with ENN

Yang, Jian; Zhuang, Yongyong; Li, Chunguang

doi:10.5772/53992

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…An error-related potential(ErrP) and an event-related activity in the human electroencephalography (EEG) are used as an intrinsically generated implicit feedback (reward) for reinforcement learning. The EEG-based human feedback in RL can successfully used to implicitly improve gesture-based robot control during human-robot interaction [19,9]. The signal strengths of EEG, ECoG, LFP and spikes extracted from different brain-computer interfaces vary greatly (Fig.…”

Section: Brain-computer Interface Theory and Technologymentioning

confidence: 99%

To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI

Yang¹,

Peng²

2020

Preprint

Self Cite

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One of the ambitions of artificial intelligence is to root artificial intelligence deeply in basic science while developing brain-inspired artificial intelligence platforms that will promote new scientific discoveries. The challenges are essential to push artificial intelligence theory and applied technologies research forward. This paper presents the grand challenges of artificial intelligence research for the next 20 years which include: (i ) to explore the working mechanism of the human brain on the basis of understanding brain science, neuroscience, cognitive science, psychology and data science; (ii ) how is the electrical signal transmitted by the human brain? What is the coordination mechanism between brain neural electrical signals and human activities? (iii ) to root brain-computer interface (BCI) and brain-muscle interface (BMI) technologies deeply in science on human behaviour; (iv ) making research on knowledge-driven visual commonsense reasoning (VCR), develop a new inference engine for cognitive network recognition (CNR); (v ) to develop high-precision, multi-modal intelligent perceptrons; (vi ) investigating intelligent reasoning and fast decision-making systems based on knowledge graph (KG). We believe that the frontier theory innovation of AI, knowledge-driven modeling methodologies for commonsense reasoning, revolutionary innovation and breakthroughs of the novel algorithms and new technologies in AI, and developing responsible AI should be the main research strategies of AI scientists in the future.

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Section: Brain-computer Interface Theory and Technologymentioning

confidence: 99%

To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI

Yang¹,

Peng²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Another application was the development of an automated indoor robot where the robot follows a predefined path as it also avoids obstacles along its path. These robots are used in hospitals, homes, hotels, and recreational centers [18] for path control and tracking [19]- [23]. A systemic adaptation that incorporated fuzzy logic into DC converters was developed by [24].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic track control of an automated lawnmower

Ajayi

Ishola

Ige

et al. 2022

IJRA

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Automation of agricultural and horticultural operations keeps received great attention for over a decade. The control parameters adopted depend on the location and characteristics of likely obstacles and navigation requirements. An automated lawnmower (ALM) with fuzzy logic control is presented in this study. Fuzzy logic was chosen to improve a previous work which was controlled via Bluetooth. Three ultrasonic sensors and two proximity sensors served as the eyes of the ALM for navigation and obstacle avoidance while the cutting blade was made of stainless steel and controlled by a brushless direct current (BLDC) motor. A fuzzy algorithm was implemented on an Arduino controller with the inputs and outputs as directional instructions. Obstacle avoidance was achieved by setting a range of values for the sensors interpreted by the fuzzy logic for the corresponding output in the form of navigations. Three trials tests were conducted on the ALM on a 5 m2 portion of land with an average grass height of 0.09 m. The average cut area was 4.46 m2, therefore achieving an efficiency of 89.2%, which is highly productive. It was observed that the power consumption was minimal compared to the previous design because at the end of the three trials 46% of the battery was left after over 3 hours of operation.

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“…However, it is difficult for robots because they lack this ability. The robot navigation method has been classified into the hybrid behavior [1,2] and the behavior-based methods [3][4][5][6][7][8]. In the hybrid behavior, Seraji and Howard [1] used a distance sensor and a camera for detecting the environment around a robot.…”

Section: Introductionmentioning

confidence: 99%

“…A new reinforcement-learning wall-following fuzzy controller is proposed and adapts to any environment. Last, according to distance information of sensors that switch wall-following control and goal seeking control, Yang et al [6] proposed a new switch strategy that determines obstacle avoidance and goal seeking in an unknown environment, in accordance with the Q-learning in which the mobile robot learns to determine obstacle avoidance and goal seeking. Moreover, they used a neural network to determine the current environment through weight calculation of the left-and right-wheel speeds of the mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control

2020

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This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control. During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot’s left- and right-wheel speeds. A fitness function in reinforcement learning is defined to evaluate the navigation control performance of AFNN. The proposed MSABC algorithm improves the poor exploitation disadvantage in the traditional artificial bee colony (ABC) and adopts the mutation strategies of a differential evolution to balance exploration and exploitation. To escape in special environments, a manual wall-following fuzzy logic controller (WF-FLC) is designed. The experimental results show that the proposed MSABC method has improved the performance of average fitness, navigation time, and travel distance by 79.75%, 33.03%, and 10.74%, respectively, compared with the traditional ABC method. To prove the feasibility of the proposed controller, experiments were carried out on the actual PIONEER 3-DX mobile robot, and the proposed navigation control method was successfully completed.

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Towards Behavior Control for Evolutionary Robot Based on RL with ENN

Cited by 6 publications

References 33 publications

To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI

To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI

Fuzzy logic track control of an automated lawnmower

Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control

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