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Enhancing Randomized Motion Planners with Haptic Hints

Project Personnel:Nancy Amato

Although Randomized Motion Planners perform well on most of the problems, there still exist some problems that are beyond the capabilities of random motion planners (due to computational or time limitations). It is true that automatic methods are very good at computations which human operators find cumbersome and/or overly time consuming, e.g., detailed computations necessary to fully determine a continuous path. However, they sometimes fail to discover some `critical' configurations which leads a solution. In contrast, such configurations are often naturally apparent to a human observer. In this work, we consider how to incorporate the strengths of both human operators and automatic planning methods. We have studied problems involving rigid bodies moving among static obstacles, such as parts in mechanical assemblies, and ligand binding which is of interest in drug design. Our studies use the PHANToM haptic device by SensAble Technologies, Inc..

We developed a simple roadmap visualization technique so that the human operator can see the the weakness of the roadmap generated by the automated planner. Later, the operator can use a haptic interface to improve this roadmap. The roadmap configurations are represented as scaled version of the robot in order be able to show not only translation but also orientation. These configurations can be either represented as wired robots or transparent robots. The connections between these configurations, i.e., the roadmap configuration then can be represented as lines. Different components of the roadmap would have different colors to classify them correctly. The figure has one tube as the obstacle (the larger one) and another tube as the robot.

After observing the roadmap, the user may improve the roadmap by generating either a configuration which the automated planner failed to find or a path to connect a disconnected part. Although there are numerous input methods (like keyboard, mouse, space-ball etc.) the best results can be achieved by using a haptic interface since the user would have a feeling of the environment. In order to simplify the user's task it is also possible to relax some constraints in the environment. The easiest way is to let the user be able to collect some collided path instead of collision free path. We called this path as approximate path . Later some algorithms may have been used to transform this approximate path to some collision free paths.

The following figures show such an example. The robot in this example is the elbow shaped object. The goal is to move the robot inside the hole. The user had collected an approximate path to help the planner, later the planner used transformed this path to a collision free path. The configuration in the left is user generated. The configuration in the right is the same configuration pushed to free space. You can watch user generated path and computer enhanced path from our experiments.

In this work we also investigated an Iterative Pushing method, i.e., we solved an easier problem. Then we used the path generated for the easy version as an approximate path for harder problem. We repeated these steps until we reached the original problem.

One of the challenges in haptic research is the very fast (i.e., ~1kHz) update requirements for force feedback. This limits the possible applications to very simple environments. However, we used a heuristic algorithm to approximate the force feedback, hence, achieved a realistic feedback even in the complex environments.

Enhancing Randomized Motion Planners: Exploring with Haptic Hints, O. Burchan Bayazit, Guang Song, Nancy M. Amato, Autonomous Robots, 10(2):163-174, 2001. Also, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 529-536, Apr 2000. Also, Technical Report, TR99-021, Parasol Laboratory, Department of Computer Science, Texas A&M University, Oct 1999.
Proceedings(pdf, abstract)

Ligand Binding with OBPRM and Haptic User Input, O. Burchan Bayazit, Guang Song, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 954-959, May 2001.
Proceedings(ps, pdf, abstract)

Interactive Dynamic Simulation using Haptic Interaction, Wookho Son, Kyunghwan Kim, Nancy M. Amato, Jeffrey C. Trinkle, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 145-150, Nov 2000.
Proceedings(ps, pdf, abstract)

Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch, O. Burchan Bayazit, Guang Song, Nancy M. Amato, Technical Report, TR00-025, Department of Computer Science and Engineering, Texas A&M University, Oct 2000.
Technical Report(ps, pdf, abstract)

An Interactive Generalized Motion Simulator (GMS) in an Object-Oriented Framework, Wookho Son, Kyunghwan Kim, Nancy M. Amato, In Proc. of Computer Animation (CA), pp. 176-181, May 2000.
Proceedings(ps, pdf, abstract)

Providing Haptic 'Hints' to Automatic Motion Planners, O. Burchan Bayazit, Guang Song, Nancy M. Amato, In Phantom Users Group Work. (PUG), Oct 1999.
Proceedings(ps, pdf, abstract)

Providing Haptic 'Hints' to Automatic Motion Planners, Nancy M. Amato, O. Burchan Bayazit, Kyunghwan Kim, Wookho Son, Guang Song, Technical Report, TR98-026, Department of Computer Science and Engineering, Texas A&M University, Nov 1998.


Supported by NSF, Texas Higher Education Coordinating Board

Project Alumni:O. Burchan Bayazit,Guang Song


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