Ioan Alexandru Sucan

The Open Motion Planning Library

The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).

MoveIt! [ROS Topics]

R obots are increasingly finding applications in domains where they have to work in close proximity to humans. Industrial robotic applications are starting to examine the possibility of robots and humans as coworkers, sharing tasks and workspace. Autonomous robotic cars operating on crowded streets and freeways have to share space with pedestrians and cyclists in addition to other vehicles. Domestic robots, in particular mobile manipulation systems, will be confronted with cluttered, messy environments where obstacles exist at every corner, and people are continuously moving in and out of the workspace of the robots. Robots working in human environments clearly have to be aware of their surroundings andmust actively attempt to avoid collisions with humans and other obstacles. MoveIt! is a set of software packages integrated with the Robot Operating System (ROS) and designed specifically to provide such capabilities, especially for mobile manipulation. MoveIt! will allow robots to build up a representation of their environment using data fused from three-dimensional (3-D) and other sensors, generate motion plans that effectively and safely move the robot around in the environment, and execute the motion plans while constantly monitoring the environment for changes.

Real-time perception-guided motion planning for a personal robot

This paper presents significant steps towards the online integration of 3D perception and manipulation for personal robotics applications. We propose a modular and distributed architecture, which seamlessly integrates the creation of 3D maps for collision detection and semantic annotations, with a real-time motion replanning framework. To validate our system, we present results obtained during a comprehensive mobile manipulation scenario, which includes the fusion of the above components with a higher level executive.

Research paper: Sampling-based robot motion planning: Towards realistic applications

This paper presents some of the recent improvements in sampling-based robot motion planning. Emphasis is placed on work that brings motion-planning algorithms closer to applicability in real environments. Methods that approach increasingly difficult motion-planning problems including kinodynamic motion planning and dynamic environments are discussed. The ultimate goal for such methods is to generate plans that can be executed with few modifications in a real robotics mobile platform.

A generic infrastructure for benchmarking motion planners

Randomized planners, search-based planners, potential-field approaches and trajectory optimization based motion planners are just some of the types of approaches that have been developed for motion planning. Given a motion planning problem, choosing the appropriate algorithm to use is a daunting task even for experts since there has been relatively little effort in comparing the plans generated by the different approaches, for different problems. In this paper, we present a set of benchmarks and the associated infrastructure for comparing different types of motion planning approaches and algorithms. The benchmarks are specifically designed for robotics and include typical indoor human environments. We present example motion planning problems for single arm tasks. Our infrastructure is designed to be easily extensible to allow for the addition of new planning approaches, new robots, new environments and new metrics. We present results comparing the performance of several motion planning algorithms to validate the use of these benchmarks.

Anytime solution optimization for sampling-based motion planning

Recent work in sampling-based motion planning has yielded several different approaches for computing good quality paths in high degree of freedom systems: path shortcutting methods that attempt to shorten a single solution path by connecting non-consecutive configurations, a path hybridization technique that combines portions of two or more solutions to form a shorter path, and asymptotically optimal algorithms that converge to the shortest path over time. This paper presents an extensible meta-algorithm that incorporates a traditional sampling-based planning algorithm with offline path shortening techniques to form an anytime algorithm which exhibits competitive solution lengths to the best known methods and optimizers. A series of experiments involving rigid motion and complex manipulation are performed as well as a comparison with asymptotically optimal methods which show the efficacy of the proposed scheme, particularly in high-dimensional spaces.

On the implementation of single-query sampling-based motion planners

Single-query sampling-based motion planners are an efficient class of algorithms widely used today to solve challenging motion planning problems. This paper exposes the common core of these planners and presents a tutorial for their implementation. A set of ideas extracted from algorithms existing in the literature is presented. In addition, lower level implementation details that are often skipped in papers due to space limitations are discussed. The purpose of the paper is to improve our understanding of single-query sampling-based motion planners and motivate our community to explore avenues of research that lead to significant improvements of such algorithms.

Real-time collision detection and distance computation on point cloud sensor data

Most prior techniques for proximity computations are designed for synthetic models and assume exact geometric representations. However, real robots construct representations of the environment using their sensors, and the generated representations are more cluttered and less precise than synthetic models. Furthermore, this sensor data is updated at high frequency. In this paper, we present new collision- and distance-query algorithms, which can efficiently handle large amounts of point cloud sensor data received at real-time rates. We present two novel techniques to accelerate the computation of broad-phase data structures: 1) we present a progressive technique that incrementally computes a high-quality dynamic AABB tree for fast culling, and 2) we directly use an octree representation of the point cloud data as a proximity data structure. We assign a probability value to each leaf node of the tree, and the algorithm computes the nodes corresponding to high collision probability. In practice, our new approaches can be an order of magnitude faster than previous methods. We demonstrate the performance of the new methods on both synthetic data and on sensor data collected using a Kinect™ for motion planning for a mobile manipulator robot.

Combining planning techniques for manipulation using realtime perception

We present a novel combination of motion planning techniques to compute motion plans for robotic arms. We compute plans that move the arm as close as possible to the goal region using sampling-based planning and then switch to a trajectory optimization technique for the last few centimeters necessary to reach the goal region. This combination allows fast computation and safe execution of motion plans even when the goals are very close to objects in the environment. The system incorporates realtime sensory inputs and correctly deals with occlusions that can occur when robot body parts block the sensor view of the environment. The system is tested on a 7 degree-of-freedom robot arm with sensory input from a tilting laser scanner that provides 3D information about the environment.

Mobile manipulation: Encoding motion planning options using task motion multigraphs

This paper introduces the concept of a task motion multigraph, a data structure that can be used to reveal a difficulty specific to mobile manipulation: the possibility of planning in different state spaces in order to achieve the same goal. The different options reflect the mobile manipulators ability to use different hardware components to perform a required task. For instance, a humanoid robot can open a door with its left arm or with its right arm. Thus, motion planning can be performed in the left arms state space or in the right arms state space. Given the specification of a task, it is shown how to encode the available motion planning options in a task motion multigraph. An algorithm that computes sequences of motion plans for mobile manipulators using the newly introduced notion is presented and evaluated. The algorithm makes use of information from the task motion multigraph to prioritize the spaces for which motion plans are computed. Experimental results show that reduced planning times can be obtained when considering the available planning options.