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

Democratizing Innovation

The process of user-centered innovation: how it can benefit both users and manufacturers and how its emergence will bring changes in business models and in public policy.Innovation is rapidly becoming democratized. Users, aided by improvements in computer and communications technology, increasingly can develop their own new products and services. These innovating users—both individuals and firms—often freely share their innovations with others, creating user-innovation communities and a rich intellectual commons. In Democratizing Innovation, Eric von Hippel looks closely at this emerging system of user-centered innovation. He explains why and when users find it profitable to develop new products and services for themselves, and why it often pays users to reveal their innovations freely for the use of all.The trend toward democratized innovation can be seen in software and information products—most notably in the free and open-source software movement—but also in physical products. Von Hippel's many examples of user innovation in action range from surgical equipment to surfboards to software security features. He shows that product and service development is concentrated among "lead users," who are ahead on marketplace trends and whose innovations are often commercially attractive.Von Hippel argues that manufacturers should redesign their innovation processes and that they should systematically seek out innovations developed by users. He points to businesses—the custom semiconductor industry is one example—that have learned to assist user-innovators by providing them with toolkits for developing new products. User innovation has a positive impact on social welfare, and von Hippel proposes that government policies, including R&D subsidies and tax credits, should be realigned to eliminate biases against it. The goal of a democratized user-centered innovation system, says von Hippel, is well worth striving for. An electronic version of this book is available under a Creative Commons license