A Novel Model Predictive Trajectory Planner for Efficient and Safe Human-Robot Handovers

Conceitos Básicos

A path-following model predictive control (MPC) formulation is proposed to enable efficient and safe human-robot handovers by incorporating a Gaussian process-based prediction of the handover location and adapting the error bounds during the handover.

Resumo

This work develops a novel trajectory planner for human-robot handovers. The key aspects are:

Path-following MPC formulation: The desired robot motion is specified as a reference path, which the robot's end-effector must follow. This allows explicit control over the time evolution of the robot's motion, such as controlling deviations from the path based on the human motion.

Handover location prediction: A Gaussian process regression model is used to predict the distribution of the handover location based on the measured human hand position and velocity. This prediction is then projected onto the reference path to guide the robot's motion.

Adaptive error bounds: The allowed deviations from the reference path are adapted during the handover to ensure convergence of the robot's end-effector to the human hand position, while maintaining predictable and safe motion.

Motion synchronization: The desired path progress is used to synchronize the human and robot motions, ensuring they reach the handover location simultaneously.

The proposed approach is demonstrated through experiments with a 7-DoF robotic manipulator receiving an object from a human. The results show the effectiveness of the method in enabling efficient and safe human-robot handovers.

Estatísticas

The robot's joint velocity ˙q2 is limited to ˙q2 ≤ ˙q2 ≤ ˙q2.

Citações

"The core message of this article is a novel path-following model predictive control (MPC) formulation that enables efficient and safe human-robot handovers by incorporating a Gaussian process-based prediction of the handover location and adapting the error bounds during the handover."

Principais Insights Extraídos De

Model Predictive Trajectory Planning for Human-Robot Handovers

by Thies Oeleri... às arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07505.pdf

Perguntas Mais Profundas

How could the proposed approach be extended to handle more complex human-robot interaction scenarios, such as multi-object handovers or collaborative task execution

The proposed approach for human-robot handovers can be extended to handle more complex scenarios by incorporating advanced motion planning techniques and adaptive control strategies. For multi-object handovers, the system can be enhanced to track and predict the trajectories of multiple objects simultaneously. This can involve integrating object detection and tracking algorithms to identify and follow the objects during handover tasks. Additionally, the MPC formulation can be modified to account for the dynamics and constraints of multiple objects, enabling the robot to plan and execute handovers efficiently.
In the case of collaborative task execution, the approach can be extended to include collaborative manipulation tasks where the robot and human work together to achieve a common goal. This would require developing algorithms for shared autonomy, where the robot dynamically adjusts its behavior based on human input and task requirements. By incorporating shared autonomy principles, the system can facilitate seamless collaboration between the human and robot, allowing them to work together effectively on complex tasks.

What are the potential limitations of the Gaussian process-based handover location prediction, and how could it be improved to handle more diverse handover situations

While Gaussian process-based handover location prediction offers a robust method for estimating the position of the handover location, it may have limitations in handling diverse handover situations. One potential limitation is the assumption of independence between the components of the handover location, which may not always hold true in real-world scenarios where the position and orientation are correlated. To improve the prediction accuracy, the model could be enhanced by incorporating correlation information between the position and orientation components of the handover location.
Another limitation could be the reliance on training data for the Gaussian process regression model. In situations where the handover trajectories vary significantly or involve novel interactions, the model may struggle to provide accurate predictions. To address this, the model could be updated in real-time based on feedback from the ongoing handover task, allowing it to adapt to changing dynamics and uncertainties during the interaction.

Could the insights from this work on motion synchronization and adaptive error bounds be applied to other areas of human-robot interaction, such as collaborative manipulation or shared autonomy tasks

The insights gained from this work on motion synchronization and adaptive error bounds can indeed be applied to other areas of human-robot interaction beyond handovers. In collaborative manipulation tasks, where the robot and human work together to manipulate objects, the concept of motion synchronization can ensure smooth coordination between the two actors. By synchronizing their motions and adapting error bounds based on the task requirements, the system can enhance collaboration and efficiency in shared manipulation tasks.
Similarly, in shared autonomy tasks where the robot assists the human in completing a task, adaptive error bounds can be utilized to ensure safe and effective collaboration. By dynamically adjusting the error bounds based on the human's actions and the task context, the robot can provide assistance that is both supportive and responsive to the human operator's needs. This can improve the overall performance and user experience in shared autonomy scenarios.