Coordinate Invariant User-Guided Constrained Path Planning with Reactive Rapidly Expanding Plane-Oriented Escaping Trees

Laha, Riddhiman; Sun, Ruiai; Wu, Wei; Mahalingam, Dasharadhan; Chakraborty, Nilanjan; Figueredo, Luis Felipe da Cruz; Haddadin, Sami

doi:10.1109/icra46639.2022.9812014

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…Safety has a direct impact in terms of efficiency in collaborative environments [1], e.g., the Speed and Separation Monitoring (SSM) proposed by the ISO-TS 15066 requires that the robot speed is modulated based on the agents' distance to keep a protective separation [2]. Over the years, researchers have tackled the safety-vsefficiency trade-off by acting on the motion planning and control levels (e.g., with impedance control [3], [4], safetyaware path planning [5], [6], and fast replanning [7]). However, a decision-making level that is unaware of safety aspects may lead to frequent interference between the agents and increase the risk of collisions and, therefore, the frequency of safety stops and re-plannings.…”

Section: Introductionmentioning

confidence: 99%

Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

Sandrini,

Faroni,

Pedrocchi

2023

Preprint

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Modern hybrid robot cells leverage heterogeneous agents to provide agile production solutions. Effective agent coordination is crucial to avoid inefficiencies and potential hazards for human operators working among robots. This paper proposes a new human-aware task allocation and scheduling model based on Mixed Integer Non-Linear Programming (MINLP) to optimize efficiency and safety during task planning, scheduling, and allocation. The approach introduces a synergy index that encodes the coupling effects between pairs of tasks executed in parallel by the agents. These terms are learned from previous process executions by means of a Bayesian linear estimation. The task planning model is enhanced by the knowledge of synergy terms to adapt the nominal duration of the plan to consider the effect of the operator’s presence. Simulations and experimental results demonstrate the effectiveness of the proposed method in obtaining a proactive human-aware solution starting from the task planning level. The proposed model reduces process execution time and achieves solutions with less agent interference, more considerable human-robot distance, and, thus, safer for agents.

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Section: Introductionmentioning

confidence: 99%

Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

Sandrini,

Faroni,

Pedrocchi

2023

Preprint

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“…For rigid-body motion, approaches were proposed where the trajectories are either generated in a left-invariant way, meaning independent of the world reference frame, or in a right-invariant way, meaning independent of the body reference frame [13]. Invariance was also taken into account for the adaptation of reference or demonstrated rigid-body trajectories [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Invariant Descriptors of Motion and Force Trajectories for Interpreting Object Manipulation Tasks in Contact

Vochten,

Mohammadi,

Verduyn

et al. 2023

IEEE Trans. Robot.

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Invariant descriptors of point and rigid-body motion trajectories have been proposed in the past as representative task models for motion recognition and generalization. Currently, no invariant descriptor exists for representing force trajectories, which appear in contact tasks. This article introduces invariant descriptors for force trajectories by exploiting the duality between motion and force. Two types of invariant descriptors are presented depending on whether the trajectories consist of screw or vector coordinates. Methods and software are provided for robustly calculating the invariant descriptors from noisy measurements using optimal control. Using experimental human demonstrations of 3-D contour following and peg-on-hole alignment tasks, invariant descriptors are shown to result in task representations that do not depend on the calibration of reference frames or sensor locations. The tuning process for the optimal control problems is shown to be fast and intuitive. Similar to motions in free space, the proposed invariant descriptors for motion and force trajectories may prove useful for the recognition and generalization of constrained motions, such as during object manipulation in contact.

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Smooth Real-Time Motion Planning Based on a Cascade Dual-Quaternion Screw-Geometry MPC

Teimoorzadeh,

Silva,

Figueredo

et al. 2024

Springer Proceedings in Advanced Robotics

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Coordinate Invariant User-Guided Constrained Path Planning with Reactive Rapidly Expanding Plane-Oriented Escaping Trees

Cited by 8 publications

References 39 publications

Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

Invariant Descriptors of Motion and Force Trajectories for Interpreting Object Manipulation Tasks in Contact

Smooth Real-Time Motion Planning Based on a Cascade Dual-Quaternion Screw-Geometry MPC

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