A hybrid technique for path planning of humanoid robot NAO in static and dynamic terrains

Kashyap, Abhishek; Parhi, Dayal R.; Muni, Manoj Kumar; Pandey, Krishna Kant

doi:10.1016/j.asoc.2020.106581

Cited by 50 publications

(19 citation statements)

References 40 publications

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“…Before independent choreography, the robot should first determine what dance postures to choreograph (Kashyap et al, 2020a ). By defining dance postures, the robot can retain basic dance features of human dance moves (García and Diogo, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Influence of Human–Computer Interaction-Based Intelligent Dancing Robot and Psychological Construct on Choreography

Yang

2022

Front. Neurorobot.

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To study the influence of Artificial Intelligence (AI) on dancing robots in choreography, this paper introduces the biped-humanoid robot-imagined choreography model alongside the Psychological Space Construction (Psychological Construct) and Human–Computer Interaction (HCI). The proposed model is based on deep learning and imitating human thinking and is capable of imagining new dance elements. Finally, simulation experiments are designed to verify the model's effectiveness. Dance professionals are invited to evaluate the robot-imagined dance posture. The results show that the proposed model can vividly imitate human dancers and imagine and create new dance movements. The average basic feature retention and innovation scores of 30 new dance elements imagined on the L1 (head) are 7.29 and 7.64, respectively. By comparison, similar scores on 30 new elements in L2 (upper-body) are 7.73 and 7.40, respectively. Therefore, the proposed intelligent robot-imagined choreography model can help the dancing robot choreograph more finely and improve the choreography efficiency. The research results have significant practical value for dance teaching.

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Section: Methodsmentioning

confidence: 99%

Influence of Human–Computer Interaction-Based Intelligent Dancing Robot and Psychological Construct on Choreography

Yang

2022

Front. Neurorobot.

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“…binary directions) that can be transmitted from the central system to the master robot, and from the master robot to the slave robot, with low overhead and latency. Although some more recent and complex options have been implemented in NAO robots Kashyap et al (2020) or Muni et al (2021) , due to the reasons stated in Section 2 we opted to use the Dijkstra Shortest Path algorithm presented in Ably et al (2016) . This method generates a tree of shortest paths from the starting point to all other points in the plane.…”

Section: Methodsmentioning

confidence: 99%

“…More recently, Kashyap et al (2020) presented a hybrid technique for path planning in static and dynamic terrains using NAO humanoid robots. Authors proposed the hybridization of two techniques: Dynamic Window Approach (DWA) and Teaching-Learning Based Optimization (TLBO).…”

Section: Related Workmentioning

confidence: 99%

Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning

et al. 2022

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Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub, airports, or for evacuation of disaster zones. Many methodologies have been explored to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have become more popular in this kind of scenarios, given their robustness and scalability. In this paper, we implement an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be capable of solving the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we showcase some initial experimentation in a pair of robots with different mechanical characteristics. Further implementations of this work include communicating the robots for other tasks, such as teaching assistance, remote classes, and other innovations in higher education.

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“…The most commonly used method for trajectory tracking is the dynamic window approach (DWA). By hybridizing with a teaching-learning based technique, it is possible to achieve the endpoint to be reached by the AGV, as Kashyap et al studied [21]. This additionally provides the ability to avoid obstacles without stopping.…”

Section: Path Followingmentioning

confidence: 99%

Dynamical Analysis of a Navigation Algorithm

et al. 2021

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There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

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A hybrid technique for path planning of humanoid robot NAO in static and dynamic terrains

Cited by 50 publications

References 40 publications

Influence of Human–Computer Interaction-Based Intelligent Dancing Robot and Psychological Construct on Choreography

Influence of Human–Computer Interaction-Based Intelligent Dancing Robot and Psychological Construct on Choreography

Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning

Dynamical Analysis of a Navigation Algorithm

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