Obstacle-avoiding intelligent algorithm for quad wheel robot path navigation

Mukherjee, Subhradip; Kumar, R.; Borah, Siddhanta

doi:10.1108/ijius-12-2019-0074

Cited by 7 publications

(11 citation statements)

References 22 publications

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“…Due to its advantages of simple operation and simple structure, intelligent detection robots are widely used in detection in various fields, which can effectively reduce the risk of manual detection and have high application value [16]. The emergence of intelligent detection robots has promoted the development of socialization, accelerated the rate of productivity, and effectively solved the drawbacks of manual detection [17]. However, at present, there are problems such as low control accuracy and control trajectory offset in the motion control of intelligent detection robots, a large number of researchers in this field design control systems based on motion trajectory data [18].…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

Farisi

2023

Journal of Sensors

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In order to further optimize the precision and efficiency of intelligent robot navigation system control, an IoT intelligent robot motion control system based on the improved ResNet model is proposed. Based on the deep learning method, using the Faster R-CNN target detection architecture and the ResNet50 convolutional neural network, the network is trained according to the characteristics of the operation target of the distribution line maintenance robot system. On this basis, combined with the binocular vision ranging principle, the coordinates of the job target in the camera coordinate system are measured, and the coordinates are converted into the robot base coordinate system through hand-eye calibration, so as to complete the spatial positioning of the job target. The results showed that the errors of the binocular measurement methods adopted by the system are all within 1%. Conclusion. The method can well adapt to the complex background of the operation scene, the change of illumination, and the partial occlusion of the target and can meet the requirements of the distribution line maintenance robot for the measurement and positioning of the target space.

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Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

Farisi

2023

Journal of Sensors

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“…The comparative studies of different algorithms in environment4 in terms of percentage The proposed algorithm has 8.13%, 3.35%, 11.32%, 25.32% and 24.06% lower deviation in travel time and 0.40%, 0.34%, 0.48%, 0.77% and 0.83% lower deviation in path length compared to OAIA [7] , DFMB [9] , SAWOA [11] , MLD [19] and SRN [20] algorithms in environment5.…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 96%

“…We have also calculated the percentage deviations of different algorithms in environment1. The result in environment1 shows that, the proposed algorithm has 9.31%, 4.80%, 12.61%, 22.46% and 18.36% lower deviation in travel time and 0.53%, 0.39%, 0.63%, 1.06% and 1.33% lower deviation in path length compared to OAIA [7] , DFMB [9] , SAWOA [11] , MLD [19] and SRN [20] approaches. The comparative studies of different algorithms in environment2 are presented in table 8.…”

Section: Table 4 Different Environments For Simulationmentioning

confidence: 99%

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A Leader Follower Algorithm Based Path Navigation Approach for Multiple Autonomous Vehicles

Mukherjee,

Kumar,

Borah

2023

Preprint

Self Cite

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Multi-agent path navigation in unknown environments is a challenging task where an effective device-to-device communication-based algorithm needs to be designed with minimum error. A leader-follower algorithm has been proposed in this literature for multi-agent communication. The proposed algorithm has improved the percentage deviation in travel time and the percentage deviation in path length. The proposed algorithm is designed in two parts. One part is for leader path navigation, and the other is used to update the followers. The obstacle avoidance mechanism is used in the first part of the algorithm. The computation speed of the algorithm is mainly dependent on the guidance that the leader provides to the follower agent. Path update from the leader side to the follower side is the most important task in this proposed method. One leader and two follower agents were considered in simulation environments to establish the feasibility of the algorithm. In the simulation, each follower agent was made to commute with a few seconds' time gap after the leader had started. After several simulations, the proposed approach has a 2.82% average deviation in travel time and a 31% average deviation in path length. The average success rate of the proposed technique in different environments was found equal to 66%. Studies show that the accuracy of this technique is more than 54% on average.

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“…An intelligent controller with an obstacle avoidance mechanism is used for safe path navigation of a quad wheel robot in a hazardous environment (Mukherjee et al, 2020). One D Ã Lite algorithm has been implied with a low-level controller in a rough terrain condition (Sebastian and Ben-Tzvi, 2019).…”

Section: Introductionmentioning

confidence: 99%

An intelligent fast controller for autonomous wheeled robot path navigation in challenging environments

Mukherjee

Kumar

Borah

2022

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Purpose This paper aims to incorporate one intelligent particle swarm optimization (IPSO) controller to realize an optimum path in unknown environments. In this paper, the fitness function of IPSO is designed with intelligent design parameters, solving the path navigation problem of an autonomous wheeled robot towards the target point by avoiding obstacles in any unknown environment. Design/methodology/approach This controller depends on randomly oriented positions with all other position information and a fitness function. Evaluating the position’s best values, this study gets the local best values, and finally, the global best value is updated as the current value after comparing the local best values. Findings The path navigation of the proposed controller has been compared with particle swarm optimization algorithm, BAT algorithm, flower pollination algorithm, invasive weed algorithm and genetic algorithm in multiple challenging environments. The proposed controller shows the percent deviation in path length near 14.54% and the percent deviation in travel time near 4% after the simulation. IPSO is applied to optimize said parameters for path navigation of the wheeled robot in different simulation environments. Originality/value A hardware model with a 32-bit ARM board interfaced with a global positioning system (GPS) module, an ultrasonic module and ZigBee wireless communication module is designed to implement IPSO. In real-time, the IPSO controller shows the percent deviation in path length near 9%.

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Obstacle-avoiding intelligent algorithm for quad wheel robot path navigation

Cited by 7 publications

References 22 publications

[Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

[Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

A Leader Follower Algorithm Based Path Navigation Approach for Multiple Autonomous Vehicles

An intelligent fast controller for autonomous wheeled robot path navigation in challenging environments

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