Neural Network Approach to Feature Sensitive Motion Planning

Motion planning (MP) is the problem of finding a valid path (e.g., collision free) from a start to a goal state for a movable object. MP is a complex problem with a myriad of applications, ranging from robotics, to computer-aided design, to computational biology. Sampling-based planning deals with MP’s complexity by constructing a graph which approximates the planning space. Different sampling based planners have been developed to tackle specific scenarios, but none of these is best for every scenario, e.g., cluttered vs. free space vs narrow passage. Thus, adaptive methods were created to combine different samplers effectively to solve more complex and heterogeneous environments. Adaptive methods have been proposed that learn the best sampler for the entire space or that partition the space into simple and discrete region types, which are suited for particular samplers. These methods do not solve the problem of environments containing multiple complex areas that are difficult to automatically partition. In this thesis, we propose an alternative approach using neural networks to create an adaptive method that does not require regions. We replace the concept of regions with a visibility distribution, how “free” a node is, allowing our method to work for a wider range of interesting problems. Experiments show significant improvement in speed compared to methods that attempt to use a single sampler for a complex environment

Geometric Approximations and their Application to Motion Planning

Geometric approximation methods are a preferred solution to handle complexities (such as a large volume or complex features such as concavities) in geometric objects or environments containing them. Complexities often pose a computational bottleneck for applications such as motion planning. Exact resolution of these complexities might introduce other complexities such as unmanageable number of components. Hence, approximation methods provide a way to handle these complexities in a manageable state by trading off some accuracy. In this dissertation, two novel geometric approximation methods are studied: aggregation hierarchy and shape primitive skeleton. The aggregation hierarchy is a hierarchical clustering of polygonal or polyhedral objects. The shape primitive skeleton provides an approximation of bounded space as a skeleton of shape primitives. These methods are further applied to improve the performance of motion planning applications. We evaluate the methods in environments with 2D and 3D objects. The aggregation hierarchy groups nearby objects into individual objects. The hierarchy is created by varying the distance threshold that determines which objects are nearby. This creates levels of detail of the environment. The hierarchy of the obstacle space is then used to create a decom-position of the complementary space (i.e, free space) into a set of sampling regions to improve the efficiency and accuracy of the sampling operation of the sampling based motion planners. Our results show that the method can improve the efficiency (10 − 70% of planning time) of sampling based motion planning algorithms. The shape primitive skeleton inscribes a set of shape primitives (e.g., sphere, boxes) inside a bounded space such that they represent the skeleton or the connectivity of the space. We apply the shape primitive skeletons of the free space and obstacle space in motion planning problems to improve the collision detection operation. Our results also show the use of shape primitive skeleton in both spaces improves the performance of collision detectors (by 20 − 70% of collision detection time) used in motion planning algorithms. In summary, this dissertation evaluates how geometric approximation methods can be applied to improve the performance of motion planning methods, especially, sampling based motion planning method

Uniform Sampling Framework for Sampling Based Motion Planning and Its Applications to Robotics and Protein Ligand Binding

Sampling-based motion planning aims to find a valid path from a start to a goal by sampling in the planning space. Planning on surfaces is an important problem in many research problems, including traditional robotics and computational biology. It is also a difficult research question to plan on surfaces as the surface is only a small subspace of the entire planning space. For example, robots are currently widely used for product assembly. Contact between the robot manipulator and the product are required to assemble each piece precisely. The configurations in which the robot fingers are in contact with the object form a surface in the planning space. However, these configurations are only a small proportion of all possible robot configurations. Several sampling-based motion planners aim to bias sampling to specific surfaces, such as Cobst surfaces, as needed for tasks requiring contact, or along the medial axis, which maximizes clearance. While some of these methods work well in practice, none of them are able to provide any information regarding the distribution of the samples they generate. It would be interesting and useful to know, for example, that a particular surface has been sampled uniformly so that one could argue regarding the probability of finding a path on that surface. Unfortunately, despite great interest for nearly two decades, it has remained an open problem to develop a method for sampling on such surfaces that can provide any information regarding the distribution of the resulting samples. Our research focuses on solving this open problem and introduces a framework that is guaranteed to uniformly sample any surface in Cspace. Instead of explicitly constructing the target surfaces, which is generally intractable, our uniform sampling framework only requires detecting intersections between a line segment and the target surface, which can often be done efficiently. Intuitively, since we uniformly distribute the line segments, the intersections between the segments and the surfaces will also be uniformly distributed. We present two particular instances of the framework: Uniform Obstacle-based PRM (UOBPRM) that uniformly samples Cobst surfaces, and Uniform Medial-Axis PRM (UMAPRM) that uniformly samples the Cspace medial axis. We provide a theoretical analysis for this framework that establishes uniformity and probabilistic completeness and also the probability of sampling in narrow passages. We show applications of this uniform sampling framework in robotics (both UOBPRM and UMAPRM) and in biology (UOBPRM). We are able to solve some difficult motion planning problems more efficiently than other sampling methods, including PRM, OBPRM, Gaussian PRM, Bridge Test PRM, and MAPRM. Moreover, we show that UOBPRM and UMAPRM have similar computational overhead as other approaches. UOBPRM is used to study the ligand binding affinity ranking problem in computational biology. Our experimental results show that UOBPRM is a potential technique to rank ligand binding affinity which can be further applied as a cost-saving tool for pharmaceutical companies to narrow the search for drug candidates

Planning manipulation movements of a dual-arm system considering obstacle removing

The paper deals with the problem of planning movements of two hand-arm robotic systems, considering the possibility of using the robot hands to remove potential obstacles in order to obtain a free access to grasp a desired object. The approach is based on a variation of a Probabilistic Road Map that does not rule out the samples implying collisions with removable objects but instead classifies them according to the collided obstacle(s), and allows the search of free paths with the indication of which objects must be removed from the work-space to make the path actually valid; we call it Probabilistic Road Map with Obstacles (PRMwO). The proposed system includes a task assignment system that distributes the task among the robots, using for that purpose a precedence graph built from the results of the PRMwO. The approach has been implemented for a real dual-arm robotic system, and some simulated and real running examples are presented in the paper. (C) 2014 Elsevier B.V. All rights reserved.Postprint (published version