Optimal, sampling-based manipulation planning

Schmitt, Philipp; Neubauer, W.; Feiten, Wendelin; Wurm, Kai M.; Wichert, Georg von; Burgard, Wolfram

doi:10.1109/icra.2017.7989390

Cited by 44 publications

(16 citation statements)

References 19 publications

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“…A naïve solution consists in identifying horizontal surfaces in the environment and placing the object flat on the surface where there is enough space. This technique is, for instance, commonly employed in manipulation planning works which focus on planning complex sequences of pick-and-place operations rather than individual placements [4]- [8].…”

Section: A Placing Objectsmentioning

confidence: 99%

“…These poses vary in x, y translation within the contact region and rotation by θ around the z-axis going through the reference point located at x, y, z r . The unionŜ = Algorithm 1: High-level Placement Planner 1 Ms, Gs ← ∅, ∅ // Storage of internal state 2 τ, ξ best , G ← ⊥, −∞, ∅ 3 while not TERMINATE() 4 Gn, Gs ← SAMPLEGOALS(gmax, ξ best , Gs) (1)) r∈R,f ∈FŜ (r, f ) of all these sets is then a parameterized approximation of S that we can search for feasible placements.…”

Section: A Defining Potential Contactsmentioning

confidence: 99%

See 1 more Smart Citation

Object Placement Planning and optimization for Robot Manipulators

Haustein

Hang

Stork

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We address the problem of motion planning for a robotic manipulator with the task to place a grasped object in a cluttered environment. In this task, we need to locate a collisionfree pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot manipulator and c) optimizes a user-given placement objective. Because of the placement objective, this problem is more challenging than classical motion planning where the target pose is defined from the start. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning for the robot manipulator with a novel hierarchical search for suitable placement poses. We evaluate our approach on a dualarm robot for two different placement objectives, and observe its effectiveness even in challenging scenarios.

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Section: A Placing Objectsmentioning

confidence: 99%

Section: A Defining Potential Contactsmentioning

confidence: 99%

Object Placement Planning and optimization for Robot Manipulators

Haustein

Hang

Stork

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

show abstract

“…Maeda et al [10] and Miyazawa et al [11] sampled the manipulation states in a configuration space where a sequence of modes existed and the modes' transitions were prior defined. Some planners have been proposed for creating the modes' roadmap to guide the manipulation sequencing [8,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Modal Planning for Cooperative Non-Prehensile Manipulation by Mobile Robots

2019

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If we define a mode as a set of specific configurations that hold the same constraint, and if we investigate their transitions beforehand, we can efficiently probe the configuration space by using a manipulation planner. However, when multiple mobile robots together manipulate an object by using the non-prehensile method, the candidates for the modes and their transitions become enormous because of the numerous contacts among the object, the environment, and the robots. In some cases, the constraints on the object, which include a combination of robot contacts and environmental contacts, are incapable of guaranteeing the object's stability. Furthermore, some transitions cannot appear because of geometrical and functional restrictions of the robots. Therefore, in this paper, we propose a method to narrow down the possible modes and transitions between modes by excluding the impossible modes and transitions from the viewpoint of statics, kinematics, and geometry. We first generated modes that described an object's contact set from the robots and the environment while ignoring their exact configurations. Each multi-contact set exerted by the robots and the environment satisfied the condition necessary for the force closure on the object along with gravity. Second, we listed every possible transition between the modes by determining whether or not the given robot could actively change the contacts with geometrical feasibility. Finally, we performed two simulations to validate our method on specific manipulation tasks. Our method can be used in various cases of non-prehensile manipulations by using mobile robots. The mode transition graph generated by our method was used to efficiently sequence the manipulation actions before deciding the detailed configuration planning.

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“…For the first type of movement, the trajectory is often known, along which the object of manipulation should move. There are a large number of methods designed for planning this type of movement [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Motion Planning Method for Two Anthropomorphic Manipulators

Петренко¹,

Тебуева²,

Antonov³

et al. 2019

Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)

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The development of methods for planning joint movements of two anthropomorphic manipulators in a common operational space becomes relevant. This is due to the development of anthropomorphic robots. Existing methods have relatively large computational complexity. They cannot be used on platforms with limited computing resources. This article proposes an approximate analytical method for planning of joint movements of two anthropomorphic manipulators for point-to-point movements. The analytical method is based on the geometric interpretation of the kinematic redundancy of anthropomorphic manipulators. The method includes three stages. At the first stage, the movement of the wrists of the manipulators is planned, at the second stage elbow joints is planned, and at the third stage end effectors is planned. In the event of a possible collision of manipulators, correction of their path is carried out by "pushing". Its computational complexity is about thousand operations. Simulation of the proposed method showed its efficiency. This method has prospects for implementation in existing manipulator control systems.

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Optimal, sampling-based manipulation planning

Cited by 44 publications

References 19 publications

Object Placement Planning and optimization for Robot Manipulators

Object Placement Planning and optimization for Robot Manipulators

Modal Planning for Cooperative Non-Prehensile Manipulation by Mobile Robots

Cooperative Motion Planning Method for Two Anthropomorphic Manipulators

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