Cloud Automation: Precomputing Roadmaps for Flexible Manipulation

Bekris, Kostas E.; Shome, Rahul; Krontiris, Athanasios; Dobson, Andrew

doi:10.1109/mra.2015.2401291

Cited by 32 publications

(15 citation statements)

References 21 publications

(35 reference statements)

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“…The current dataset does not focus on the case ofpartially occluded objects, where a pose estimation process may be used to evaluate the pose of both the occluding and the occluded objects so as to assist rearrangement manipulation algorithms [33], [34]. Such problems can be potentially benefited by the utilization of cloud computation in order to improve performance and deal with the inferent uncertainty in the pose estimation and manipulation processes [35].…”

Section: Discussionmentioning

confidence: 99%

A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place

Rennie

Shome

Bekris

et al. 2016

IEEE Robot. Autom. Lett.

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154

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An important logistics application of robotics involves manipulators that pick-and-place objects placed in warehouse shelves. A critical aspect of this task corresponds to detecting the pose of a known object in the shelf using visual data. Solving this problem can be assisted by the use of an RGBD sensor, which also provides depth information beyond visual data. Nevertheless, it remains a challenging problem since multiple issues need to be addressed, such as low illumination inside shelves, clutter, texture-less and reflective objects as well as the limitations of depth sensors. This paper provides a new rich dataset for advancing the state-of-the-art in RGBD-based 3D object pose estimation, which is focused on the challenges that arise when solving warehouse pick-and-place tasks. The publicly available dataset includes thousands of images and corresponding ground truth data for the objects used during the first Amazon Picking Challenge at different poses and clutter conditions. Each image is accompanied with ground truth information to assist in the evaluation of algorithms for object detection. To show the utility of the dataset, a recent algorithm for RGBD-based pose estimation is evaluated in this paper. Given the measured performance of the algorithm on the dataset, this paper shows how it is possible to devise modifications and improvements to increase the accuracy of pose estimation algorithms. This process can be easily applied to a variety of different methodologies for object pose detection and improve performance in the domain of warehouse pick-and-place.

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Section: Discussionmentioning

confidence: 99%

A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place

Rennie

Shome

Bekris

et al. 2016

IEEE Robot. Autom. Lett.

Self Cite

154

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“…It is interesting to evaluate how different object placements may result in different probability of success during real-world execution. From a system's point of view it is interesting to investigate how to quickly compute such rearrangement paths through the use of cloud-based computation [36].…”

Section: Discussionmentioning

confidence: 99%

Efficiently solving general rearrangement tasks: A fast extension primitive for an incremental sampling-based planner

Krontiris

Bekris

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Manipulating multiple movable obstacles is a hard problem that involves searching high-dimensional C-spaces. A milestone method for this problem was able to compute solutions for monotone instances. These are problems where every object needs to be transferred at most once to achieve a desired arrangement. The method uses backtracking search to find the order with which objects should be moved. This paper first proposes an approximate but significantly faster alternative for monotone rearrangement instances. The method defines a dependency graph between objects given minimum constraint removal paths (MCR) to transfer each object to its target. From this graph, the approach discovers the order of moving objects by performing topological sorting without backtracking search. The approximation arises from the limitation to consider only MCR paths, which minimize, however, the number of conflicts between objects. To solve non-monotone instances, this primitive is incorporated in a higher-level incremental search algorithm for general rearrangement planning, which operates similar to Bi-RRT. Given a start and a goal object arrangement, tree structures of reachable new arrangements are generated by using the primitive as an expansion procedure. The integrated solution achieves probabilistic completeness for the general non-monotone case and based on simulated experiments it achieves very good success ratios, solution times and path quality relative to alternatives.

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“…A synergistic roadmap pre-computation scheme proposed by Bekris et al [44] uses cloud hardware for pre-computation of a high quality roadmap, which is then shared with a local computer for time-sensitive planning operations.…”

Section: B Parallelisation and Cloud Computingmentioning

confidence: 99%

Recent progress on sampling based dynamic motion planning algorithms

Short

Pan

Larkin

et al. 2016

2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored. Review of Sampling Based Dynamic Motion Planning AlgorithmsAndrew Short, Zengxi Pan, Nathan Larkin and Stephen van DuinAbstract-This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored.

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Cloud Automation: Precomputing Roadmaps for Flexible Manipulation

Cited by 32 publications

References 21 publications

A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place

A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place

Efficiently solving general rearrangement tasks: A fast extension primitive for an incremental sampling-based planner

Recent progress on sampling based dynamic motion planning algorithms

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