ідея - Robotics - # Passive Interaction Control

Constrained Passive Interaction Control: Leveraging Passivity and Safety for Robot Manipulators

Q: How can the proposed framework be adapted for different types of robots beyond manipulators

The proposed framework for constrained passive interaction control can be adapted for different types of robots beyond manipulators by adjusting the formulation of the constraints and control barrier functions to suit the specific characteristics and dynamics of the new robot types. For instance, for mobile robots or drones, the constraints may involve collision avoidance with static or dynamic obstacles in their environment. The boundary functions could be learned using data-driven approaches tailored to the particular geometry and motion capabilities of these robots. Additionally, incorporating environmental factors such as terrain roughness or inclines into the constraint formulations would enable safe navigation and interaction.

Q: What are the potential limitations or drawbacks of implementing joint-space constraints alongside passivity requirements

Implementing joint-space constraints alongside passivity requirements introduces certain limitations and drawbacks that need to be considered. One limitation is increased computational complexity due to the need for calculating higher-order derivatives (such as Hessian matrices) when formulating exponential control barrier functions (ECBFs) with relative degree two. This can lead to slower real-time performance, especially in scenarios where rapid decision-making is crucial. Moreover, enforcing multiple concurrent constraints may result in trade-offs between satisfying all requirements optimally, potentially leading to suboptimal solutions that compromise either safety or passivity guarantees. Another drawback is related to system robustness under uncertainties or disturbances. While the framework ensures safety through constraint satisfaction, variations in external conditions not accounted for during training neural network models for boundary functions could impact controller performance adversely. Therefore, ensuring adaptability and generalization of learned boundary functions across diverse operating conditions becomes critical.

Q: How might advancements in neural networks impact the efficiency and accuracy of learning boundary functions for constraint satisfaction

Advancements in neural networks have significant implications on enhancing efficiency and accuracy when learning boundary functions for constraint satisfaction within robotic systems: Efficiency: Advanced neural network architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) can improve computational efficiency by capturing complex relationships within high-dimensional input spaces more effectively than traditional methods like SVMs or regression models. Generalization: State-of-the-art techniques such as transfer learning allow pre-trained NN models on similar tasks to be fine-tuned quickly on new datasets relevant to learning boundary functions without starting from scratch each time. Adaptability: Neural networks offer flexibility in adapting learned representations based on changing environments or task requirements through online learning mechanisms like continual learning or reinforcement learning strategies. Accuracy: With improved model architectures like transformers enabling better sequence modeling capabilities, NN-based boundary function approximations can achieve higher accuracy levels even with limited training data samples. By leveraging these advancements effectively within the framework's design process, it becomes feasible to develop more robust controllers capable of handling a wider range of scenarios while maintaining stringent safety and passivity standards required in human-robot collaboration settings

Основні поняття

Passivity and safety are crucial for robot manipulators, ensuring stability and safety in physical interactions with humans.

Анотація

Passivity is essential for robots to interact safely with humans physically. This paper proposes a control architecture that ensures safety constraints while maintaining passivity only when feasible. The approach involves constraining an impedance control law with a hierarchical control barrier function quadratic program. Joint space constraints are formulated using data-driven self- and external collision boundary functions. The controller aims to guarantee stability in free motion and contact with passive environments by preserving a passive relation between external forces and robot velocity. Passivity-based controllers leverage energy exchange techniques built on impedance-based control laws, particularly variable impedance control used in human-robot interaction tasks requiring adaptation of the robot's stiffness. Classic impedance control schemes driven by time-indexed reference trajectories can lose passivity during tracking, highlighting the importance of encoding tasks as time-independent velocity fields. The proposed framework combines passivity with joint-space constraints such as joint limits, self-collisions, external collisions, and singularities to ensure safe operation of robot manipulators in human-centric environments.

Статистика

Passivity is necessary for robots to fluidly collaborate and interact with humans physically.
The proposed control architecture guarantees safety constraints while maintaining passivity only when feasible.
Joint space constraints are formulated using efficient data-driven self- and external collision boundary functions.
Passivity ensures stability both in free motion and in contact with passive environments.
Impedance-based control laws leverage energy exchange techniques for stability analysis.

Цитати

"In this work, we posit that a robot operating in a human-centric environment should be passive only when feasible."
"Our approach is validated in simulation and real robot experiments on a 7DoF Franka Research 3 manipulator."
"A more general notion of passivity is the property of a dynamical system to not produce more energy than it receives."

Ключові висновки, отримані з

Constrained Passive Interaction Control

by Zhiquan Zhan... о arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09853.pdf

Глибші Запити

How can the proposed framework be adapted for different types of robots beyond manipulators

The proposed framework for constrained passive interaction control can be adapted for different types of robots beyond manipulators by adjusting the formulation of the constraints and control barrier functions to suit the specific characteristics and dynamics of the new robot types. For instance, for mobile robots or drones, the constraints may involve collision avoidance with static or dynamic obstacles in their environment. The boundary functions could be learned using data-driven approaches tailored to the particular geometry and motion capabilities of these robots. Additionally, incorporating environmental factors such as terrain roughness or inclines into the constraint formulations would enable safe navigation and interaction.

What are the potential limitations or drawbacks of implementing joint-space constraints alongside passivity requirements

Implementing joint-space constraints alongside passivity requirements introduces certain limitations and drawbacks that need to be considered. One limitation is increased computational complexity due to the need for calculating higher-order derivatives (such as Hessian matrices) when formulating exponential control barrier functions (ECBFs) with relative degree two. This can lead to slower real-time performance, especially in scenarios where rapid decision-making is crucial. Moreover, enforcing multiple concurrent constraints may result in trade-offs between satisfying all requirements optimally, potentially leading to suboptimal solutions that compromise either safety or passivity guarantees.
Another drawback is related to system robustness under uncertainties or disturbances. While the framework ensures safety through constraint satisfaction, variations in external conditions not accounted for during training neural network models for boundary functions could impact controller performance adversely. Therefore, ensuring adaptability and generalization of learned boundary functions across diverse operating conditions becomes critical.

How might advancements in neural networks impact the efficiency and accuracy of learning boundary functions for constraint satisfaction

Advancements in neural networks have significant implications on enhancing efficiency and accuracy when learning boundary functions for constraint satisfaction within robotic systems:

Efficiency: Advanced neural network architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) can improve computational efficiency by capturing complex relationships within high-dimensional input spaces more effectively than traditional methods like SVMs or regression models.

Generalization: State-of-the-art techniques such as transfer learning allow pre-trained NN models on similar tasks to be fine-tuned quickly on new datasets relevant to learning boundary functions without starting from scratch each time.

Adaptability: Neural networks offer flexibility in adapting learned representations based on changing environments or task requirements through online learning mechanisms like continual learning or reinforcement learning strategies.

Accuracy: With improved model architectures like transformers enabling better sequence modeling capabilities, NN-based boundary function approximations can achieve higher accuracy levels even with limited training data samples.

By leveraging these advancements effectively within the framework's design process, it becomes feasible to develop more robust controllers capable of handling a wider range of scenarios while maintaining stringent safety and passivity standards required in human-robot collaboration settings

Constrained Passive Interaction Control: Leveraging Passivity and Safety for Robot Manipulators