Efficient motion planning for manipulation robots in environments with deformable objects

This paper focuses on the motion planning problem of an extensible elastic rod in collision-free or contact space. The rod is assumed to be handled by grippers either at one or both extremities. Furthermore, during manipulation, the grasped end may change. We show that the use of both quasi-static and dynamic models can be coupled efficiently with sampling-based methods. Sampling directly in the submanifold of static equilibrium and contact-free configurations allows to take advantage of the dynamic model to improve the exploration of the state space. In this way, thanks to the contact information (point, forces, direction, number of contacts), the exploration of the RRT approach can be improved. We present a new RRT-SLIDE algorithm which guides the roadmap extension with a sliding contact mode based on some principles of human reasoning. We show that our approach is probabilistically complete. We also demonstrate the necessity of considering contacts on complex scenarios with several simulation experiments. Besides its performances, our algorithm does not require further tuning phase for a new scenario. Note to Practitioners-This work was done under the industrial project Flecto (ANR-Digital Models). It aims at solving the assembly/disassembly task for a rod while satisfying the elasticity parameters of its material. For industrial applications, the resolution time is a critical point. On the one hand, probabilistic motion planning methods require to efficiently build a roadmap of valid rod configurations. On the other hand, accurate rod modeling implies the use of a simulator based on the finite element method. Nevertheless, the very large size of the roadmap, that leads to a high number of calls to the simulator, is conflicting with the high computational cost of finite-element based simulation. To overcome this problem, one solution is to reduce the number of simulator calls. This can be achieved by sampling the free space with an efficient parameterization and by limiting the use of the simulator to roadmap extension in the free space or in the contact space. We introduce heuristics based on contact information returned by the simulator to reduce significantly the computational time. One of the main advantages of our algorithm is that it does not require any tuning phase for each scenario. Although we do not solve the more general gripper manipulation planning problem, this approach could be used as a first step before computing the motion of the grippers. In the framework of our project, we did not consider disassembling operations implying undoing rod knots. Consequently, we do not take friction into account in our approach. In order to handle knots, it would be necessary to have a physics simulator that could handle friction for deformable rods.

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“…Using The system of equations given by (6) and (9), which relateṡ p(0) andp(0) to coordinates a has clearly a unique solution.…”

Section: Appendixmentioning

confidence: 99%

“…We therefore restricted our study to the case of a deformable rod amidst rigid obstacles. The case of rigid object manipulation in a deformable environment, as studied in [6], will not be addressed here. In this research project, we do not consider industrial cases where it is necessary to undo knots.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning for an Elastic Rod Using Contacts

Roussel

Fernbach

Taïx

2020

IEEE Trans. Automat. Sci. Eng.

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“…Work on controlling deformable objects to tie knots dates back as far as Inoue and Inaba's 1985 paper [14]. Many of 2015 IEEE International Conference on Robotics and Automation (ICRA) Washington State Convention Center Seattle, Washington, May 26-30, 2015 these approaches to deformable object manipulation require a fairly detailed model or simulation of the deformable object [10], [25].…”

Section: Related Workmentioning

confidence: 99%

“…Several approaches attempt to overcome these challenges through motion planning [9], [17], [22], [24], [27], but these methods rely on accurate simulations of deformation. While suitable for tasks that can be accomplished without needing high precision, these methods are not wellsuited for tasks that require the object to be precisely aligned with environment features, such as threading a needle.…”

Section: Introductionmentioning

confidence: 99%

An online method for tight-tolerance insertion tasks for string and rope

Wang

Berenson

Balkcom

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents a fast tight-tolerance threading technique for string and rope. Instead of relying on simulations of these deformable objects to plan a path or compute control actions, we control the movement of the string with a virtual magnetic vector field emanating from the narrow openings we wish to thread through. We compute an approximate Jacobian to move the tip of the string through the vector field and propose a method to promote alignment of the head of the string to the opening. We also propose a method for re-grasping the string based on the relationship between the string's configuration, the orientation of the opening, and direction of gravity. This re-grasping method in conjunction with our controller can be used to thread the string through a sequence of openings. We evaluated our method in simulation (with simulated sensor noise) and on the Da Vinci surgical robot. Our results suggest that our method is quite robust to errors in sensing, and is capable of real-world threading tasks with the da Vinci robot, where the diameter of the string (3.5mm) and opening (4.9mm) differ by only 1.4 mm.978-1-4799-6923-4/15/$31.00 ©2015 IEEE

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“…In both cases, the planner that does not consider the deformation costs would lead to significant deformation (left images) whereas our approach results in less deformation and still short paths (right images). Videos of the robot executing these trajectories in a deformation simulation and more examples can be found on our website [4].…”

Section: Example Trajectoriesmentioning

confidence: 99%

Efficient motion planning for manipulation robots in environments with deformable objects

Frank

Stachniss

Abdo

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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The ability to plan their own motions and to reliably execute them is an important precondition for autonomous robots. In this paper, we consider the problem of planning the motion of a mobile manipulation robot in the presence of deformable objects. Our approach combines probabilistic roadmap planning with a physical deformation simulation system. Since the physical deformation simulation is computationally demanding, we use efficient Gaussian process regression to estimate the deformation cost for individual objects based on training examples. We generate the training data by employing a simulation system in a preprocessing step. Consequently, no simulations are needed during runtime. We implemented and tested our approach on a mobile manipulation robot. Our experiments show that the robot is able to accurately predict and thus consider the deformation cost its manipulator introduces to the environment during motion planning. Simultaneously, the computation time is substantially reduced compared to a system that employs physical simulations online.

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Efficient motion planning for manipulation robots in environments with deformable objects

Cited by 17 publications

References 15 publications

Motion Planning for an Elastic Rod Using Contacts

Motion Planning for an Elastic Rod Using Contacts

An online method for tight-tolerance insertion tasks for string and rope

Efficient motion planning for manipulation robots in environments with deformable objects

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