Lydia E. Kavraki

Probabilistic roadmaps for path planning in high-dimensional configuration spaces

A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).

Practical robust localization over large-scale 802.11 wireless networks

We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of human labor to build a detailed signal map, we can train our system by spending less than one minute per office or region, walking around with a laptop and recording the observed signal intensities of our buildings unmodified base stations. We actually collected over two minutes of data per office or region, about 28 man-hours of effort. Using less than half of this data to train the localizer, we can localize a user to the precise, correct location in over 95% of our attempts, across the entire building. Even in the most pathological cases, we almost never localize a user any more distant than to the neighboring office. A user can obtain this level of accuracy with only two or three signal intensity measurements, allowing for a high frame rate of localization results. Furthermore, with a brief calibration period, our system can be adapted to work with previously unknown user hardware. We present results demonstrating the robustness of our system against a variety of untrained time-varying phenomena, including the presence or absence of people in the building across the day. Our system is sufficiently robust to enable a variety of location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures.

Robotics-based location sensing using wireless ethernet

A key subproblem in the construction of location-aware systems is the determination of the position of a mobile device. This paper describes the design, implementation and analysis of a system for determining position inside a building from measured RF signal strengths of packets on an IEEE 802.11b wireless Ethernet network. Previous approaches to location awareness with RF signals have been severely hampered by non-linearity, noise and complex correlations due to multi-path effects, interference and absorption. The design of our system begins with the observation that determining position from complex, noisy and non-linear signals is a well-studied problem in the field of robotics. Using only off-the-shelf hardware, we achieve robust position estimation to within a meter in our experimental context and after adequate training of our system. We can also coarsely determine our orientation and can track our position as we move. By applying recent advances in probabilistic inference of position and sensor fusion from noisy signals, we show that the RF emissions from base stations as measured by off-the-shelf wireless Ethernet cards are sufficiently rich in information to permit a mobile device to reliably track its location.

Path planning using lazy PRM

Describes an approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner. Our algorithm builds a roadmap in the configuration space, whose nodes are the user-defined initial and goal configurations and a number of randomly generated nodes. Neighboring nodes are connected by edges representing paths between the nodes. In contrast with PRMs, our planner initially assumes that all nodes and edges in the roadmap are collision-free, and searches the roadmap at hand for a shortest path between the initial and the goal node. The nodes and edges along the path are then checked for collision. If a collision with the obstacles occurs, the corresponding nodes and edges are removed from the roadmap. Our planner either finds a new shortest path, or first updates the roadmap with new nodes and edges, and then searches for a shortest path. The above process is repeated until a collision-free path is returned. Lazy PRM is tailored to efficiently answer single planning queries, but can also be used for multiple queries. Experimental results presented in the paper show that our lazy method is very efficient in practice.

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).

Randomized preprocessing of configuration for fast path planning

This paper presents a new approach to path planning for robots with many degrees of freedom (DOF) operating in known static environments. The approach consists of a preprocessing and a planning stage. Preprocessing, which is done only once for a given environment, generates a network of randomly, but properly selected, collision-free configurations (nodes). Planning then connects any given initial and final configurations of the robot to two nodes of the network and computes a path through the network between these two nodes. Experiments show that after paying the preprocessing cost (on the order of hundreds of seconds), planning is extremely fast (on the order of a fraction of a second for many difficult examples involving a 10-DOF robot). The approach is particularly attractive for many-DOF robots which have to perform many successive point-to-point motions in the same environment.<<ETX>>

Analysis of probabilistic roadmaps for path planning

Provides an analysis of a path planning method which uses probabilistic roadmaps. This method has proven very successful in practice, but the theoretical understanding of its performance is still limited. Assuming that a path /spl gamma/ exists between two configurations a and b of the robot, we study the dependence of the failure probability to connect a and b on (i) the length of /spl gamma/, (ii) the distance function of /spl gamma/ from the obstacles, and (iii) the number of nodes N of the probabilistic roadmap constructed. Importantly, our results do not depend strongly on local irregularities of the configuration space, as was the case with previous analysis. These results are illustrated with a simple but illuminating example. In this example, we provide estimates for N, the principal parameter of the method, in order to achieve failure probability within prescribed bounds. We also compare, through this example, the different approaches to the analysis of the planning method.

On the feasibility of using wireless ethernet for indoor localization

IEEE 802.11b wireless Ethernet is becoming the standard for indoor wireless communication. This paper proposes the use of measured signal strength of Ethernet packets as a sensor for a localization system. We demonstrate that off-the-shelf hardware can accurately be used for location sensing and real-time tracking by applying a Bayesian localization framework.

Sampling-based motion planning with temporal goals

This paper presents a geometry-based, multi-layered synergistic approach to solve motion planning problems for mobile robots involving temporal goals. The temporal goals are described over subsets of the workspace (called propositions) using temporal logic. A multi-layered synergistic framework has been proposed recently for solving planning problems involving significant discrete structure. In this framework, a high-level planner uses a discrete abstraction of the system and the exploration information to suggest feasible high-level plans. A low-level sampling-based planner uses the physical model of the system, and the suggested high-level plans, to explore the state-space for feasible solutions. In this paper, we advocate the use of geometry within the above framework to solve motion planning problems involving temporal goals. We present a technique to construct the discrete abstraction using the geometry of the obstacles and the propositions defined over the workspace. Furthermore, we show through experiments that the use of geometry results in significant computational speedups compared to previous work. Traces corresponding to trajectories of the system are defined employing the sampling interval used by the low-level algorithm. The applicability of the approach is shown for second-order nonlinear robot models in challenging workspace environments with obstacles, and for a variety of temporal logic specifications.

Randomized path planning for linkages with closed kinematic chains

We extend randomized path planning algorithms to the case of articulated robots that have closed kinematic chains. This is an important class of problems, which includes applications such as manipulation planning using multiple open-chain manipulators that cooperatively grasp an object and planning for reconfigurable robots in which links might be arranged in a loop to ease manipulation or locomotion. Applications also exist in areas beyond robotics, including computer graphics, computational chemistry, and virtual prototyping. Such applications typically involve high degrees of freedom and a parameterization of the configurations that satisfy closure constraints is usually not available. We show how to implement key primitive operations of randomized path planners for general closed kinematics chains. These primitives include the generation of random free configurations and the generation of local paths. To demonstrate the feasibility of our primitives for general chains, we show their application to recently developed randomized planners and present computed results for high-dimensional problems.