HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

S, Phani Teja; Alami, Rachid

doi:10.1109/ro-man47096.2020.9223463

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…Where 1 0 T , 2 1 T , 3 2 T are obtained by substituting the values of the DH parameters in the general transformation matrix. We can establish the kinematic relationship between the position information of wrist joint and other joints of upper limb according to (1). Therefore, next we focus on the prediction of wrist joint position.…”

Section: The Foundation Knowledge a The Kinematics Model Of Human Upp...mentioning

confidence: 99%

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Robust Human Upper-Limbs Trajectory Prediction Based on Gaussian Mixture Prediction

Zhang

et al. 2023

IEEE Access

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Accurate prediction of human motion trajectory can improve the security of human-robot cooperation. Due to the unstructured nature of collaborative workspace and the uncertainty of sensor sensing data, the trajectory prediction accuracy of traditional prediction algorithms is low, and the uncertainty is difficult to estimate. Aiming at the complex characteristics of human upper limb movement patterns, this paper proposes a robust upper limb end trajectory prediction algorithm. The robust Gaussian mixture model was used to model the trajectory of human upper limb movement, and the statistical values of the future trajectory were obtained by combining Gaussian mixture regression. The advantage of this algorithm is that the prediction result is not only the predicted value of the position, but also the probability distribution of all possible future motion trajectories of the upper limb. The position prediction information in a specific motion mode can be obtained by using probability and statistical distribution characteristics. The algorithm is tested on both public and private datasets. Experimental results show that this method can predict human trajectories well.

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Section: The Foundation Knowledge a The Kinematics Model Of Human Upp...mentioning

confidence: 99%

“…Many fields have benefited by applying human-robot collaboration (HRC) strategies since the creation of collaborative robots that can safely share tasks with humans [1], [2]. In the military field, cooperative robots are expected to work with military personnel in order for soldiers to better understand the dangers of the battlefield [3].…”

Section: Introductionmentioning

confidence: 99%

Robust Human Upper-Limbs Trajectory Prediction Based on Gaussian Mixture Prediction

Zhang

et al. 2023

IEEE Access

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“…Most studies that address issues related to autonomous robots concentrate on simulating crowds and individuals by depicting them as points [13], or they do not offer a wide range of appearance models [14], [15]. Unfortunately, these works suffer from a lack of perception information, making it highly challenging to create comprehensive solutions that integrate perception and robotic control.…”

Section: Related Workmentioning

confidence: 99%

A Self-Adaptive Gallery Construction Method for Open-World Person Re-Identification

Casao

Azagra

Murillo

et al. 2023

Sensors

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Person re-identification, or simply re-id, is the task of identifying again a person who has been seen in the past by a perception system. Multiple robotic applications, such as tracking or navigate-and-seek, use re-identification systems to perform their tasks. To solve the re-id problem, a common practice consists in using a gallery with relevant information about the people already observed. The construction of this gallery is a costly process, typically performed offline and only once because of the problems associated with labeling and storing new data as they arrive in the system. The resulting galleries from this process are static and do not acquire new knowledge from the scene, which is a limitation of the current re-id systems to work for open-world applications. Different from previous work, we overcome this limitation by presenting an unsupervised approach to automatically identify new people and incrementally build a gallery for open-world re-id that adapts prior knowledge with new information on a continuous basis. Our approach performs a comparison between the current person models and new unlabeled data to dynamically expand the gallery with new identities. We process the incoming information to maintain a small representative model of each person by exploiting concepts of information theory. The uncertainty and diversity of the new samples are analyzed to define which ones should be incorporated into the gallery. Experimental evaluation in challenging benchmarks includes an ablation study of the proposed framework, the assessment of different data selection algorithms that demonstrate the benefits of our approach, and a comparative analysis of the obtained results with other unsupervised and semi-supervised re-id methods.

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“…Furthermore, proactive co-navigation tasks have already been well studied. Indeed, in [4], the authors propose a reactive trajectory planner for robot which takes into account the human predicted motions and goals to handle humanrobot co-navigation. The estimation of the human movement is based on its current velocity and three different navigation modes are presented to make the robot move forward without colliding with the human.…”

Section: Introductionmentioning

confidence: 99%

Human Trajectory Prediction Model and Its Coupling With a Walking Pattern Generator of a Humanoid Robot

Maroger

Ramuzat

Stasse

et al. 2021

IEEE Robot. Autom. Lett.

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In order to smoothly perform interactions between a humanoid robot and a human, knowledge about the human locomotion can be efficiently used. Indeed, in a human-robot collaboration, a prediction model of the human behaviour allows the robot to act proactively. In this paper, an optimal control based model predicting the human Center of Mass (CoM) trajectory during gait is presented. A Walking Pattern Generator (WPG) based on non-linear model predictive control is, then, introduced in order to generate the robot CoM and footsteps along the predicted trajectory. The combination of the human trajectory prediction model and this new WPG aims to allow the robot to proactively walk along with a human instead of passively follow him. These models have been tested in simulation on Gazebo on a TALOS humanoid robot model using measured human trajectories. To perform the CoM and foot trajectories computed by the WPG, a real-time whole-body controller is used. This controller is a Quadratic Program which solves the inverse dynamics of the robot at torque level.

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HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

Cited by 8 publications

References 20 publications

Robust Human Upper-Limbs Trajectory Prediction Based on Gaussian Mixture Prediction

Robust Human Upper-Limbs Trajectory Prediction Based on Gaussian Mixture Prediction

A Self-Adaptive Gallery Construction Method for Open-World Person Re-Identification

Human Trajectory Prediction Model and Its Coupling With a Walking Pattern Generator of a Humanoid Robot

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