9 research outputs found
An Extensible Benchmarking Infrastructure for Motion Planning Algorithms
Sampling-based planning algorithms are the most common probabilistically
complete algorithms and are widely used on many robot platforms. Within this
class of algorithms, many variants have been proposed over the last 20 years,
yet there is still no characterization of which algorithms are well-suited for
which classes of problems. This has motivated us to develop a benchmarking
infrastructure for motion planning algorithms. It consists of three main
components. First, we have created an extensive benchmarking software framework
that is included with the Open Motion Planning Library (OMPL), a C++ library
that contains implementations of many sampling-based algorithms. Second, we
have defined extensible formats for storing benchmark results. The formats are
fairly straightforward so that other planning libraries could easily produce
compatible output. Finally, we have created an interactive, versatile
visualization tool for compact presentation of collected benchmark data. The
tool and underlying database facilitate the analysis of performance across
benchmark problems and planners.Comment: Submitted to IEEE Robotics & Automation Magazine (Special Issue on
Replicable and Measurable Robotics Research), 201
Experience-Based Planning with Sparse Roadmap Spanners
We present an experienced-based planning framework called Thunder that learns
to reduce computation time required to solve high-dimensional planning problems
in varying environments. The approach is especially suited for large
configuration spaces that include many invariant constraints, such as those
found with whole body humanoid motion planning. Experiences are generated using
probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which
provides asymptotically near-optimal coverage of the configuration space,
making storing, retrieving, and repairing past experiences very efficient with
respect to memory and time. The Thunder framework improves upon past
experience-based planners by storing experiences in a graph rather than in
individual paths, eliminating redundant information, providing more
opportunities for path reuse, and providing a theoretical limit to the size of
the experience graph. These properties also lead to improved handling of
dynamically changing environments, reasoning about optimal paths, and reducing
query resolution time. The approach is demonstrated on a 30 degrees of freedom
humanoid robot and compared with the Lightning framework, an experience-based
planner that uses individual paths to store past experiences. In environments
with variable obstacles and stability constraints, experiments show that
Thunder is on average an order of magnitude faster than Lightning and planning
from scratch. Thunder also uses 98.8% less memory to store its experiences
after 10,000 trials when compared to Lightning. Our framework is implemented
and freely available in the Open Motion Planning Library.Comment: Submitted to ICRA 201
Accounting for Uncertainty in Simultaneous Task and Motion Planning Using Task Motion Multigraphs
This paper describes an algorithm that considers uncertainty while solving the simultaneous task and motion planning (STAMP) problem. Information about uncertainty is transferred to the task planning level from the motion planning level using the concept of a task motion multigraph (TMM). TMMs were introduced in previous work to improve the efficiency of solving the STAMP problem for mobile manipulators. In this work, Markov Decision Processes are used in conjunction with TMMs to select sequences of actions that solve the STAMP problem such that the resulting solutions have higher probability of feasibility. Experimental evaluation indicates significantly improved probability of feasibility for solutions to the STAMP problem, compared to algorithms that ignore uncertainty information when selecting possible sequences of actions. At the same time, the efficiency due to TMMs is largely maintained
Teaching Motion Planning Concepts to Undergraduate Students
Motion planning is a central problem in robotics. Although it is an engaging topic for undergraduate students, it is difficult to teach, and as a result, the material is often only covered at an abstract level. Deep learning could be achieved by having students implement and test different algorithms. However, there is usually no time within a single class to have students completely implement several motion planning algorithms as they require the development of many lower-level data structures. We present an ongoing project to develop a teaching module for robotic motion planning centered around an integrated software environment. The module can be taught early in the undergraduate curriculum, after students have taken an introductory programming class