Mobile Manipulation in Unstructured Environments: Perception, Planning, and Execution

Chitta, Sachin; Jones, E. Gil; Ciocarlie, Matei; Hsiao, Kaijen

doi:10.1109/mra.2012.2191995

Cited by 103 publications

(34 citation statements)

References 20 publications

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“…First we analyze the motion generation capabilities of two state-of-the-art mobile manipulation systems [5,6]. Both systems handle unstructured environments by constructing an environmental representation during task execution.…”

Section: A Mobile Manipulation Planning Systemsmentioning

confidence: 99%

Sensor-based, task-constrained motion generation under uncertainty

Sieverling

Kuhnen

Brock

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Mobile manipulation targets applications in dynamic and unstructured environments. Motion generation methods suitable for these applications must account for endeffector task constraints, must reason about environment uncertainty, i.e. the fact that the exact state of the dynamic environment cannot be known to the robot, and should do so only using their on-board sensors. We present the ExpectedShortest-Path Elastic Roadmap (ESPER) planner as a motion generation method suitable for mobile manipulation. It integrates task-constrained, whole-body, reactive motion generation in high-dimensional configuration space with reasoning about uncertainty. In our experiments, we generate task-consistent motion in uncertain environments on a real-world mobile manipulator only relying on on-board sensors.

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Section: A Mobile Manipulation Planning Systemsmentioning

confidence: 99%

Sensor-based, task-constrained motion generation under uncertainty

Sieverling

Kuhnen

Brock

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…A very prominent example is the Personal Robot 2 (PR2), developed by Willow Garage [3]. It is equipped with two 7 DoF compliant arms and a parallel gripper with touch sensor matrices on the gripper tips.…”

Section: Related Workmentioning

confidence: 99%

Efficient 3D object perception and grasp planning for mobile manipulation in domestic environments

Stückler

Steffens

Holz

et al. 2013

Robotics and Autonomous Systems

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“…Further localization was performed by pressing the spatula against the pan. In this task, and others that used the Willow Garage PR2, feedback from tactile sensors were used to react to perceived poor grasps (Chitta et al 2012), resetting the grasp planning if the grasp was poorer than expected. The division of more sophisticated tasks for the PR2 are handled through a hierarchical state machine SMACH (Bohren 2011) that are constructed a-priori.…”

Section: Related Workmentioning

confidence: 99%

Model-based autonomous system for performing dexterous, human-level manipulation tasks

Hudson

Jain

et al. 2013

Auton Robot

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This article presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation Software (ARM-S) program. Performing human-level manipulation tasks is achieved through a novel combination of perception in uncertain environments, precise tool use, forceful dualarm planning and control, persistent environmental tracking, and task level verification. Deliberate interaction with the environment is incorporated into planning and control strategies, which, when coupled with world estimation, allows for refinement of models and precise manipulation. The system takes advantage of sensory feedback immediately with little open-loop execution, attempting true autonomous reasoning and multi-step sequencing that adapts in the face of changing and uncertain environments. A tire change scenario utilizing human tools, discussed throughout the article, is used to described the system approach. A second scenario of cutting a wire is also presented, and is used to illustrate system component reuse and generality.

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Mobile Manipulation in Unstructured Environments: Perception, Planning, and Execution

Cited by 103 publications

References 20 publications

Sensor-based, task-constrained motion generation under uncertainty

Sensor-based, task-constrained motion generation under uncertainty

Efficient 3D object perception and grasp planning for mobile manipulation in domestic environments

Model-based autonomous system for performing dexterous, human-level manipulation tasks

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