A scalable method for parallelizing sampling-based motion planning algorithms

Abstract—This paper describes a scalable method for paral-lelizing sampling-based motion planning algorithms. It subdi-vides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequen-tial) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. I

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

Motion planning for geometric models in data visualization

Interaktivní geometrické modely pro simulaci přírodních jevů (LH11006)Pokročilé grafické a počítačové systémy (SGS-2016-013)A finding of path is an important task in many research areas and it is a common problem solved in a wide range of applications. New problems of finding path appear and complex problems persist, such as a real-time plan- ning of paths for huge crowds in dynamic environments, where the properties according to which the cost of a path is evaluated as well as the topology of paths may change. The task of finding a path can be divided into path planning and motion planning, which implicitly respects the collision with surroundings in the environment. Within the first group this thesis focuses on path planning on graphs for crowds. The main idea is to group members of the crowd by their common initial and target positions and then plan the path for one representative member of each group. These representative members can be navigated by classic approaches and the rest of the group will follow them. If the crowd can be divided into a few groups this way, the proposed approach will save a huge amount of computational and memory demands in dynamic environments. In the second area, motion planning, we are dealing with another problem. The task is to navigate the ligand through the protein or into the protein, which turns out to be a challenging problem because it needs to be solved in 3D with the collision detection

A Scalable Framework for Parallelizing Sampling-Based Motion Planning Algorithms

Motion planning is defined as the problem of finding a valid path taking a robot (or any movable object) from a given start configuration to a goal configuration in an environment. While motion planning has its roots in robotics, it now finds application in many other areas of scientific computing such as protein folding, drug design, virtual prototyping, computer-aided design (CAD), and computer animation. These new areas test the limits of the best sequential planners available, motivating the need for methods that can exploit parallel processing. This dissertation focuses on the design and implementation of a generic and scalable framework for parallelizing motion planning algorithms. In particular, we focus on sampling-based motion planning algorithms which are considered to be the state-of-the-art. Our work covers the two broad classes of sampling-based motion planning algorithms--the graph-based and the tree-based methods. Central to our approach is the subdivision of the planning space into regions. These regions represent sub- problems that can be processed in parallel. Solutions to the sub-problems are later combined to form a solution to the entire problem. By subdividing the planning space and restricting the locality of connection attempts to adjacent regions, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We also describe how load balancing strategies can be applied in complex environments. We present experimental results that scale to thousands of processors on different massively parallel machines for a range of motion planning problems