Time-Optimal Trajectory Planning With Interaction With the Environment

Petrone, Vincenzo; Ferrentino, Enrico; Chiacchio, Pasquale

doi:10.1109/lra.2022.3191813

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…its impracticability in tracking a force reference. In fact, as demonstrated in [12], achieving this objective necessitates that an accurate kinematic and dynamic description of the environment is available, a challenging endeavor given the inherent difficulty in devising a reliable model [13]. Hence, delivering force-tracking capabilities to impedance controllers is an aspect of unquestionable importance for the maximization of the performance of robotenvironment interaction tasks.…”

Section: B Related Work and Motivationmentioning

confidence: 99%

Optimized Residual Action for Interaction Control with Learned Environments

Petrone,

Puricelli,

Ferrentino

et al. 2024

Preprint

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In industrial settings, robotic tasks often require interaction with various objects, necessitating compliant manipulation to prevent damage while accurately tracking reference forces. To this aim, interaction controllers are typically employed, but they need either human tinkering for parameter tuning or precise environmental modeling. Both these aspects can be problematic, as the former is a time-consuming procedure, and the latter is unavoidably affected by approximations, hence being prone to failure during the actual application. Addressing these challenges, current research focuses on devising high-performance force controllers. Along this line, this work introduces ORACLE (Optimized Residual Action for Interaction Control with Learned Environments), a novel force control approach. Utilizing neural networks, ORACLE predicts robot-environment interaction forces, which are then used in an optimal residual action controller to locally correct actions from a base force controller, minimizing the expected force-tracking error. Tested on a real Franka Emika Panda robot, ORACLE demonstrates superior force tracking performance compared to state-of-the-art controllers, with a short setup time.

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Section: B Related Work and Motivationmentioning

confidence: 99%

Optimized Residual Action for Interaction Control with Learned Environments

Petrone,

Puricelli,

Ferrentino

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…the impracticability to track a force reference. In fact, as demonstrated in [9], this objective can be achieved only if an accurate description of the environment is available; since such a reliable model is typically hard to devise [10], it is of unquestionable importance for the maximization of the performance of robot-environment interaction tasks to deliver force-tracking capabilities to impedance controllers. Indeed, our work contributes to the development of novel strategies in this context.…”

Section: B Related Workmentioning

confidence: 99%

“…Moreover, having detailed a procedure to capture the robotenvironment interaction dynamics permits the inclusion of this knowledge in a trajectory planning scenario, such as [10]. Providing a more complete understanding of the contact dynamics in the planning phase could result in a better trajectory construction, improving its reliability and feasibility prior to its execution.…”

Section: B Future Workmentioning

confidence: 99%

Optimized Residual Action for Interaction Control with Learned Environments

Puricelli¹,

Pozzi²,

Petrone³

et al. 2023

Preprint

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Robotic tasks featuring interaction with other bodies are increasingly required in industrial contexts. The manipulators need to interact with the environment in a compliant way to avoid damage, but, at the same time, are often required to accurately track a reference force. To this aim, interaction controllers are typically employed, but they either need human tinkering for parameter tuning or precise modeling of the environment the robot will interact with. The former is a time-consuming procedure, while the latter is necessarily affected by approximations, which often lead to failure during the actual application. Both these aspects are problematic if it were often necessary to change the contact environment.Current research is concentrating on devising high-performance force controllers that are simple to tune and quick to adapt to changing environments. Along this line, this work proposes a novel control strategy, that we term ORACLE (Optimized Residual Action for interaction Control with Learned Environments). It exploits an ensemble of neural networks to estimate the force generated by the robot-environment interaction. This estimate is input to an optimal residual action controller that locally corrects the main action, output of a base force controller, which guarantees stability. The ORACLE strategy has been implemented and tested in the MuJoCo dynamic simulator and in a real-case scenario, both foreseeing a Franka Emika Panda robot used as a test platform. A reduction in terms of force tracking error is achieved by deploying the proposed strategy, with a short setup time.

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“…There are several alternative approaches to the TOTP problem beyond the three groups mentioned above [ 23 , 24 , 25 , 26 , 27 , 28 ]. Nevertheless, these approaches also neglect the third-order constraint and do not perform planning in Cartesian space.…”

Section: Introductionmentioning

confidence: 99%

A Cartesian-Based Trajectory Optimization with Jerk Constraints for a Robot

Fan

Jia

Zhang

et al. 2023

Entropy

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To address the time-optimal trajectory planning (TOTP) problem with joint jerk constraints in a Cartesian coordinate system, we propose a time-optimal path-parameterization (TOPP) algorithm based on nonlinear optimization. The key insight of our approach is the presentation of a comprehensive and effective iterative optimization framework for solving the optimal control problem (OCP) formulation of the TOTP problem in the (s,s˙)-phase plane. In particular, we identify two major difficulties: establishing TOPP in Cartesian space satisfying third-order constraints in joint space, and finding an efficient computational solution to TOPP, which includes nonlinear constraints. Experimental results demonstrate that the proposed method is an effective solution for time-optimal trajectory planning with joint jerk limits, and can be applied to a wide range of robotic systems.

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Time-Optimal Trajectory Planning With Interaction With the Environment

Cited by 10 publications

References 16 publications

Optimized Residual Action for Interaction Control with Learned Environments

Optimized Residual Action for Interaction Control with Learned Environments

Optimized Residual Action for Interaction Control with Learned Environments

A Cartesian-Based Trajectory Optimization with Jerk Constraints for a Robot

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