Accounting for Part Pose Estimation Uncertainties during Trajectory Generation for Part Pick-Up Using Mobile Manipulators

Thakar, Shantanu; Rajendran, P.; Annem, Vivek; Kabir, Ariyan M.; Gupta, Satyandra K.

doi:10.1109/icra.2019.8793501

Cited by 25 publications

(8 citation statements)

References 39 publications

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“…Despite abundant research in trajectory planning for mobile robots and robotic arms, few works focused on coupling both systems' control. It was shown that coupling the base and the robotic arm motion leads to a considerable reduction of total operational time and smoother motions [1], [2]. Nevertheless, these methods were designed for static environments and did not allow real-time collision avoidance.…”

Section: B Collision Avoidance For Mobile Manipulatorsmentioning

confidence: 99%

“…Motion for both parts then need to be synchronized or are executed sequentially. Both synchronization and sequencing limit the ability of the robot to perform complex tasks, and it was shown that decoupled and sequenced approaches show significantly higher operational times [1], [2]. Yet, solving the wholebody trajectory optimization problem is challenging due to a large number of degrees of freedom (e.g., 10 for the robot used in our experiments and shown in Fig.…”

Section: Introductionmentioning

confidence: 96%

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Coupled Mobile Manipulation via Trajectory Optimization with Free Space Decomposition

Spahn

Brito

Alonso–Mora

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Section: B Collision Avoidance For Mobile Manipulatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Coupled Mobile Manipulation via Trajectory Optimization with Free Space Decomposition

Spahn

Brito

Alonso–Mora

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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“…Thakar et al [30] considered the manipulator motion planning problem for a mobile manipulator to pick and transport a target object, where the picking is performed while the base is moving. They considered accounting for the pose estimation of the target object in [31], however, picking from a moving base is still relatively sensitive to the base position uncertainty and is not robust in real world implementation. Moreover, for multiple pick-and-place tasks considered in this paper, the mobile manipulator is expected to pick up multiple objects stored within a small region, it is very difficult for the mobile manipulator to finish the task within a short period of time, in this sense, picking while moving is not practical.…”

Section: Related Workmentioning

confidence: 99%

Planning a Sequence of Base Positions for a Mobile Manipulator to Perform Multiple Pick-and-Place Tasks

Xu¹,

Domae²,

Ueshiba³

et al. 2020

Preprint

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In this paper, we present a planner that plans a sequence of base positions for a mobile manipulator to efficiently and robustly collect objects stored in distinct trays. We achieve high efficiency by exploring the common areas where a mobile manipulator can grasp objects stored in multiple trays simultaneously and move the mobile manipulator to the common areas to reduce the time needed for moving the mobile base. We ensure robustness by optimizing the base position with the best clearance to positioning uncertainty so that a mobile manipulator can complete the task even if there is a certain deviation from the planned base positions. Besides, considering different styles of object placement in the tray, we analyze feasible schemes for dynamically updating the base positions based on either the remaining objects or the target objects to be picked in one round of the tasks. In the experiment part, we examine our planner on various scenarios, including different object placement: (1) Regularly placed toy objects; (2) Randomly placed industrial parts; and different schemes for online execution: (1) Apply globally static base positions; (2) Dynamically update the base positions. The experiment results demonstrate the efficiency, robustness and feasibility of the proposed method.Note to Practitioners-The presented project uses mobile manipulators to fetch and supply parts in an automotive assembly factory. Mechanical parts at these sites are usually regularly or randomly placed in supply trays. The project develops methods to explore robust mobile base positions where the objects with different placement styles and from different trays can be reached by a mobile manipulator. The mobile manipulators could perform efficient and high-quality pick-and-place operations by navigating to the explored positions. The developed methods also discuss the dynamic updates the base positions following picking process to further increase system robustness. The presented project is expected to shed light on the deployment of mobile manipulators to collect parts at large manufacturing factories.

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“…Finishing (Bhatt et al, 2019; Kabir et al, 2016, 2018c), painting (Chen et al, 2016), inspection (Yau and Menq, 1995), fiber placement (Malhan et al, 2019a,b, 2018, 2020; Shembekar et al, 2018; Shirinzadeh et al, 2007), sheet forming and handling (Li and Ceglarek, 2002), transportation (Colombo et al, 2019; Kabir et al, 2020; Thakar et al, 2018, 2019a, 2020a,b), grasping (Kumbla et al, 2017, 2018; Thakar et al, 2019b), wire harnessing (Hermansson et al, 2013; Shah and Shah, 2016), machine tending (Al-Hussaini et al, 2020; Annem et al, 2019), and 3D printing (Bhatt et al, 2018) are some of the operations where relative motion of objects can be encoded as path constraints. These path constraints or the motion of the objects can be generated using traditional motion planners (Elber and Cohen, 1994; Kuffner and LaValle, 2000).…”

Section: Introductionmentioning

confidence: 99%

Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

Kabir

Thakar

Malhan

et al. 2021

The International Journal of Robotics Research

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We present an approach to generate path-constrained synchronous motion for the coupled ensemble of robots. In this article, we refer to serial-link manipulators and mobile bases as robots. We assume that the relative motion constraints among the objects in the environment are given. We represent the motion constraints as path constraints and pose the problem of path-constrained synchronous trajectory generation as a non-linear optimization problem. Our approach generates configuration space trajectories for the robots to manipulate the objects such that the given motion constraints among the objects are satisfied. We present a method that formulates the problem as a discrete parameter optimization problem and solves it using successive constraint refinement techniques. The method adaptively selects the parametric representation of the configuration variables for a given scenario. It also generates an approximate solution as the starting point for the successive constraint refinement stages to reduce the computation time. We discuss in detail why successive constraint refinement strategies are useful for solving this class of problems. We demonstrate the effectiveness of the proposed method on challenging test cases in simulation and physical environments with high-degree-of-freedom robotic systems.

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Accounting for Part Pose Estimation Uncertainties during Trajectory Generation for Part Pick-Up Using Mobile Manipulators

Cited by 25 publications

References 39 publications

Coupled Mobile Manipulation via Trajectory Optimization with Free Space Decomposition

Coupled Mobile Manipulation via Trajectory Optimization with Free Space Decomposition

Planning a Sequence of Base Positions for a Mobile Manipulator to Perform Multiple Pick-and-Place Tasks

Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

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