11 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
An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning
Bi-directional search is a widely used strategy to increase the success and
convergence rates of sampling-based motion planning algorithms. Yet, few
results are available that merge both bi-directional search and asymptotic
optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The
objective of this paper is to fill this gap. Specifically, this paper presents
a bi-directional, sampling-based, asymptotically-optimal algorithm named
Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*)
algorithm to bi-directional search while preserving its key properties, chiefly
lazy search and asymptotic optimality through convergence in probability. BFMT*
performs a two-source, lazy dynamic programming recursion over a set of
randomly-drawn samples, correspondingly generating two search trees: one in
cost-to-come space from the initial configuration and another in cost-to-go
space from the goal configuration. Numerical experiments illustrate the
advantages of BFMT* over its unidirectional counterpart, as well as a number of
other state-of-the-art planners.Comment: Accepted to the 2015 IEEE Intelligent Robotics and Systems Conference
in Hamburg, Germany. This submission represents the long version of the
conference manuscript, with additional proof details (Section IV) regarding
the asymptotic optimality of the BFMT* algorith
Asymptotically near-optimal RRT for fast, high-quality, motion planning
We present Lower Bound Tree-RRT (LBT-RRT), a single-query sampling-based
algorithm that is asymptotically near-optimal. Namely, the solution extracted
from LBT-RRT converges to a solution that is within an approximation factor of
1+epsilon of the optimal solution. Our algorithm allows for a continuous
interpolation between the fast RRT algorithm and the asymptotically optimal
RRT* and RRG algorithms. When the approximation factor is 1 (i.e., no
approximation is allowed), LBT-RRT behaves like RRG. When the approximation
factor is unbounded, LBT-RRT behaves like RRT. In between, LBT-RRT is shown to
produce paths that have higher quality than RRT would produce and run faster
than RRT* would run. This is done by maintaining a tree which is a sub-graph of
the RRG roadmap and a second, auxiliary graph, which we call the lower-bound
graph. The combination of the two roadmaps, which is faster to maintain than
the roadmap maintained by RRT*, efficiently guarantees asymptotic
near-optimality. We suggest to use LBT-RRT for high-quality, anytime motion
planning. We demonstrate the performance of the algorithm for scenarios ranging
from 3 to 12 degrees of freedom and show that even for small approximation
factors, the algorithm produces high-quality solutions (comparable to RRG and
RRT*) with little running-time overhead when compared to RRT
Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation
Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning
Fast Marching Methods in path and motion planning: improvements and high-level applications
Mención Internacional en el título de doctorPath planning is defined as the process to establish the sequence of states a system must go through in order to reach a desired state. Additionally, motion planning (or trajectory planning) aims to compute the sequence of motions (or actions) to take the system from one state to another. In robotics path planning can refer for instance to the waypoints a robot should follow through a maze or the sequence of points a robotic arm has to follow in order to grasp an object. Motion planning is considered a more general problem, since it includes kinodynamic constraints. As motion planning is a more complex problem, it is often solved in a two-level approach: path planning in the first level and then a control layer tries to drive the system along the specified path. However, it is hard to guarantee that the final trajectory will keep the initial characteristics. The objective of this work is to solve different path and motion planning problems under a common framework in order to facilitate the integration of the different algorithms that can be required during the nominal operation of a mobile robot. Also, other related areas such as motion learning are explored using this framework. In order to achieve this, a simple but powerful algorithm called Fast Marching will be used. Originally, it was proposed to solve optimal control problems. However, it has became very useful to other related problems such as path and motion planning. Since Fast Marching was initially proposed, many different alternative approaches have been proposed. Therefore, the first step is to formulate all these methods within a common framework and carry out an exhaustive comparison in order to give a final answer to: which algorithm is the best under which situations? This Thesis shows that the different versions of Fast Marching Methods become useful when applied to motion and path planning problems. Usually, high-level problems as motion learning or robot formation planning are solved with completely different algorithms, as the problem formulation are mixed. Under a common framework, task integration becomes much easier bringing robots closer to everyday applications. The Fast Marching Method has also inspired modern probabilistic methodologies, where computational cost is enormously improved at the cost of bounded, stochastic variations on the resulting paths and trajectories. This Thesis also explores these novel algorithms and their performance.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Antonio Giménez Fernández.- Vocal: Isabel Lobato de Faria Ribeir