Collision-probability constrained PRM for a manipulator with base pose uncertainty

Huang, Yifeng; Gupta, Kalyan Kali Sen

doi:10.1109/iros.2009.5354168

Cited by 22 publications

(16 citation statements)

References 19 publications

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“…A standard measure in finding a safe path is to define a probability of collision with an obstacle. Resulting paths can be chosen that balance optimality and risk [22,23]. In [24], an optimal path is found subject to a maximum allowable probability of collision (typically called a "chance constraint").…”

Section: Planning With Uncertaintymentioning

confidence: 99%

Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation

Agha–mohammadi

Paull

et al. 2015

Robotics: Science and Systems XI

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Abstract-Long-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow without bound, and as temporal scale grows, the number of measurements will grow without bound. In this work, we present the first known approach to tackle both of these issues.The approach has two stages. First, a subset of the variables (focused variables) is selected that are most useful for a particular task. Second, a task-agnostic and principled method (focused inference) is proposed to select a subset of the measurements that maximizes the information over the focused variables. The approach is then applied to the specific task of robot navigation in an obstacle-laden environment. A landmark selection method is proposed to minimize the probability of collision and then select the set of measurements that best localizes those landmarks. It is shown that the two-stage approach outperforms both only selecting measurement and only selecting landmarks in terms of minimizing the probability of collision. The performance improvement is validated through detailed simulation and real experiments on a Pioneer robot.

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Section: Planning With Uncertaintymentioning

confidence: 99%

Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation

Agha–mohammadi

Paull

et al. 2015

Robotics: Science and Systems XI

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“…Motion planning for articulated robot arms is typically done with sampling based motion planners, Rapidly-Exploring Random Tree [3] being a classical approach. These planners can only guarantee a single path to be collision free, unless planning for a set of particles representing uncertainty in a starting pose [4]. Even though a number of attempts have been made to speed up path planning, including both tree structure optimization [5] and sampling methods [6], [7], it is still expensive to plan paths for many particles.…”

Section: Related Workmentioning

confidence: 99%

Adaptive motion planning in bin-picking with object uncertainties

Iversen

Ellekilde

Miró

2017

2017 17th International Conference on Control, Automation and Systems (ICCAS)

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Doing motion planning for bin-picking with object uncertainties requires either a re-grasp of picked objects or an online sensor system. Using the latter is advantageous in terms of computational time, as no time is wasted doing an extra pick and place action. It does, however, put extra requirements on the motion planner, as the target position may change on-the-fly. This paper solves that problem by using a state adjusting Partial Observable Markov Decision Process, where the state space is modified between runs, to better fit earlier solved problems. The approach relies on a set of waypoints, containing information about which parts of the state space may contain feasible solutions. Waypoints are pushed around the state space by observing which states in the neighborhood lead to successfully solved problems. Two bin-picking scenarios are modeled with the proposed method. One scenario in which the system receives an object pose update while moving towards the place position. Another where the update includes the object type being grasped out of a fixed number of options, each class to be deposited in a different place. When an online POMDP solver is utilized, the state adjusting POMDP is improving performance by up to 28% on execution times compared to a not adjusted POMDP.

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“…Applied to this setting, MCR finds the path that minimizes probability of collision. Huang and Gupta (2009) considered a related problem of chanceconstrained optimal planning, and they present an approximate planner for computing the minimum length path on a given roadmap that exceeds a collision probability threshold [9].…”

Section: Applicationsmentioning

confidence: 99%

The minimum constraint removal problem with three robotics applications

Hauser

2013

The International Journal of Robotics Research

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This paper formulates a new Minimum Constraint Removal (MCR) motion planning problem in which the objective is to remove the fewest geometric constraints necessary to connect a start and goal state with a free path. It describes a probabilistic roadmap motion planner for MCR in continuous configuration spaces that operates by constructing increasingly refined roadmaps, and efficiently solves discrete MCR problems on these networks. A number of new theoretical results are given for discrete MCR, including a proof that it is NP-hard by reduction from SET-COVER. Two search algorithms are described that perform well in practice. The motion planner is proven to produce the optimal MCR with probability approaching 1 as more time is spent, and its convergence rate is improved with various efficient sampling strategies. It is demonstrated on three example applications: generating human-interpretable excuses for failure, motion planning under uncertainty, and rearranging movable obstacles.

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Collision-probability constrained PRM for a manipulator with base pose uncertainty

Cited by 22 publications

References 19 publications

Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation

Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation

Adaptive motion planning in bin-picking with object uncertainties

The minimum constraint removal problem with three robotics applications

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