Improvement of collision detection using Time Elastic Band algorithm

Dai, Wanyu; Ma, Xianghua

doi:10.1145/3512576.3512592

2021 the 9th International Conference on Information Technology: IoT and Smart City 2021

DOI: 10.1145/3512576.3512592

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Improvement of collision detection using Time Elastic Band algorithm

Wanyu Dai

Xianghua Ma

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2023

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A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios

Arce,

Solano,

Beltrán

2023

Sensors

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At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that—according to the application and the robot’s characteristics—it is recommended to use each of them, based on different conditions.

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A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios

Arce,

Solano,

Beltrán

2023

Sensors

View full text Add to dashboard Cite

show abstract

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Improvement of collision detection using Time Elastic Band algorithm

Cited by 1 publication

References 3 publications

A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios

A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios

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