Showing papers on "Motion planning published in 1998"

Rapidly-exploring random trees : a new tool for path planning

Abstract: We introduce the concept of a Rapidly-exploring Random Tree (RRT) as a randomized data structure that is designed for a broad class of path planning problems. While they share many of the bene cial properties of existing randomized planning techniques, RRTs are specifically designed to handle nonholonomic constraints (including dynamics) and high degrees of freedom. An RRT is iteratively expanded by applying control inputs that drive the system slightly toward randomly-selected points, as opposed to requiring point-to-point convergence, as in the probabilistic roadmap approach. Several desirable properties and a basic implementation of RRTs are discussed. To date, we have successfully applied RRTs to holonomic, nonholonomic, and kinodynamic planning problems of up to twelve degrees of freedom.

Motion Planning in Dynamic Environments Using Velocity Obstacles

Abstract: This paper presents a method for robot motion planning in dynamic environments. It consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the cur rent positions and velocities of the robot and obstacles. It is a first- order method, since it does not integrate velocities to yield positions as functions of time.The avoidance maneuvers are generated by selecting robot ve locities outside of the velocity obstacles, which represent the set of robot velocities that would result in a collision with a given obstacle that moves at a given velocity, at some future time. To ensure that the avoidance maneuver is dynamically feasible, the set of avoidance velocities is intersected with the set of admissible velocities, defined by the robot's acceleration constraints. Computing new avoidance maneuvers at regular time intervals accounts for general obstacle trajectories.The trajectory from start to goal is computed by searching a tree of feasible avoidance maneuve...

Robot Motion Planning and Control

Abstract: Guidelines in nonholonomic motion planning for mobile robots.- Geometry of nonholonomic systems.- Optimal trajectories for nonholonomic mobile robots.- Feedback control of a nonholonomic car-like robot.- Probabilistic path planning.- Collision detection algorithms for motion planning.

Control of a nonholonomic mobile robot using neural networks

Abstract: A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. Online NN weight tuning algorithms do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.

Obstacle avoidance in a dynamic environment: a collision cone approach

Abstract: A novel collision cone approach is proposed as an aid to collision detection and avoidance between irregularly shaped moving objects with unknown trajectories. It is shown that the collision cone can be effectively used to determine whether collision between a robot and an obstacle (both moving in a dynamic environment) is imminent. No restrictions are placed on the shapes of either the robot or the obstacle, i.e., they can both be of any arbitrary shape. The collision cone concept is developed in a phased manner starting from existing analytical results that enable prediction of collision between two moving point objects. These results are extended to predict collision between a point and a circular object, between a point and an irregularly shaped object, between two circular objects, and finally between two irregularly shaped objects. Using the collision cone approach, several strategies that the robot can follow in order to avoid collision, are presented. A discussion on how the shapes of the robot and obstacles can be approximated in order to reduce computational burden is also presented. A number of examples are given to illustrate both collision prediction and avoidance strategies of the robot.

Real-time map building and navigation for autonomous robots in unknown environments

Abstract: An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.

Mobile Robot Localisation Using Active Vision

Abstract: Active cameras provide a mobile robot with the capability to fixate and track features over a wide field of view. However, their use emphasises serial attention focussing on a succession of scene features, raising the question of how this should be best achieved to provide localisation information. This paper describes a fully automatic system, able to detect, store and track suitable landmark features during goal-directed navigation. The robot chooses which of the available set of landmarks to track at a certain time to best improve its position knowledge, and decides when it is time to search for new features. Localisation performance improves on that achieved using odometry alone and shows significant advantages over passive structure-from-motion techniques. Rigorous consideration is given to the propagation of uncertainty in the estimation of the positions of the robot and scene features as the robot moves, fixates and shifts fixation. The paper shows how the estimates of these quantities are inherently coupled in any map-building system, and how features can reliably be re-found after periods of neglect, mitigating the “motion drift” problem often encountered in structure-from-motion algorithms.

Rapid collision detection by dynamically aligned DOP-trees

Abstract: Based on a general hierarchical data structure, we present a fast algorithm for exact collision detection of arbitrary polygonal rigid objects. Objects consisting of hundreds of thousands of polygons can be checked for collision at interactive rates. The pre-computed hierarchy is a tree of discrete oriented polytopes (DOPs). An efficient way of realigning DOPs during the traversal of such trees allows us to use simple interval tests for determining the overlap between DOPs. The data structure is very efficient in terms of memory and construction time. Extensive experiments with synthetic and real-world CAD data have been carried out to analyze the performance and memory usage of the data structure. A comparison with oriented bounding box (OBB) trees indicates that DOP-trees are as efficient in terms of collision query time and more efficient in memory usage and construction time.

Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach

Abstract: Localization is one of the fundamental problems of mobile robots. In order to efficiently perform useful tasks such as office delivery, mobile robots must know their position in their environment. Existing approaches can be distinguished according to the type of localization problem they are designed to solve. Tracking techniques aim at monitoring the robot's position. They assume that the position is initially known and cannot recover from situations in which they lost track of the robot's position. Global localization techniques on the other hand, are able to estimate the robot's position under complete uncertainty. We present the dynamic Markov localization technique as a uniform approach to position estimation, which is able (1) to globally estimate the position of the robot, (2) to efficiently track its position whenever the robot's certainty is high, and (3) to detect and recover from localization failures. The approach has been implemented and intensively tested in real-world environments. We present several experiments illustrating the strength of our method.

Multilevel Path Planning for Nonholonomic Robots Using Semiholonomic Subsystems

Abstract: We present a new and complete multilevel approachfor solving path- planning problems for nonholonomic robots. At the first level, a path is found that disrespects (some of) the nonholonomic constraints. At each of the next levels, a new path is generated by transformation of the path generated at the previous level. The transformation is such that more nonholonomic constraints are respected than at the previous level. At the final level, all nonholonomic constraints are respected.We present two techniques for these transformations. The first, which we call the pick and link technique, repeatedly picks pieces from the given path, and tries to replace them by more feasible ones. The second technique restricts the free configuration space to a "tube" around the given path, and a road map that captures the free-space connectivity within this tube is constructed by the prob abilistic path planner. From this road map we retrieve a new, more feasible path.In the intermediate levels, we plan paths for what we ref...

Dextrous manipulation by rolling and finger gaiting

Abstract: Many practical dextrous manipulation tasks involve large-scale motion of the grasped object while maintaining a stable grasp. To plan such task, one must control both the motion of the object and the contact locations, while also adhering to the workspace constraints typical of multi-fingered hands. We integrate the relevant theories of contact kinematics, nonholonomic motion planning, coordinated object manipulation, grasp stability and finger gaits to develop a general framework for dextrous manipulation planning. To illustrate our approach, the framework is applied to the problem of manipulating a sphere with three hemi-spherical fingertips. The simulation results are presented.

GRAMMPS: a generalized mission planner for multiple mobile robots in unstructured environments

Abstract: For a system of cooperative mobile robots to be effective in real-world applications it must be able to efficiently execute a wide class of complex tasks in potentially unknown and unstructured environments. Previous research in multi-robot systems has either been limited to relatively structured domains or to small classes of feasible missions. This paper describes a field-capable system called GRAMMPS which addresses this problem by coupling a general-purpose interpreted grammar for task definition with dynamic planning techniques. GRAMMPS supports a general class of local navigation systems and heterogeneous groups of robots, providing optimal execution of missions given current world knowledge. Simulations illustrating the capabilities of this system are provided. Results showing successful runs of this system on two autonomous off-road vehicles are also given.

Trajectory planning in a dynamic workspace: a 'state-time space' approach

Abstract: This paper addresses trajectory planning in a dynamic workspace, i.e. motion planning for a robot subject to dynamic constraints and moving in a workspace with moving obstacles. First the novel con...

Finding landmarks for mobile robot navigation

Abstract: Localization addresses the problem of determining the position of a mobile robot from sensor data. This paper presents an algorithm, called BaLL, which enables a mobile robot to learn a set of landmarks used in localization and to learn how to recognize them using artificial neural networks. BaLL is based on a statistical localization approach. It is applicable to a large variety of sensors and environments. Experiments with a mobile robot equipped with sonar sensors and a camera illustrate that BaLL identifies highly useful landmarks.

Languages, behaviors, hybrid architectures, and motion control

Abstract: In this paper we put forward a framework that integrates features of reactive planning models with modern control-theory-based approaches to motion control of robots. We introduce a motion description language, MDLe, inspired by Roger Brockett’s MDL, that provides a formal basis for robot programming using behaviors, and at the same time permits incorporation of kinematic and dynamic models of robots given in the form of differential equations. In particular, behaviors for robots are formalized in terms of kinetic state machines, a motion description language, and the interaction of the kinetic state machine with realtime information from (limited range) sensors. This formalization allows us to create a mathematical basis for the study of such systems, including techniques for integrating sets of behaviors. In addition we suggest cost functions for comparing both atomic and compound behaviors in various environments. We demonstrate the use of MDLe in the area of motion planning for nonholonomic robots. Such models impose limitations on stabilizability via smooth feedback; piecing together open-loop and closed-loop trajectories becomes essential in these circumstances, and MDLe enables one to describe such piecing together in a systematic manner. A reactive planner using the formalism of this discussion is described. We demonstrate obstacle avoidance with limited range sensors as a test of this planner.

Vision-based motion planning and exploration algorithms for mobile robots

Abstract: This paper considers the problem of systematically exploring an unfamiliar environment in search of one or more recognizable targets. The proposed exploration algorithm is based on a novel representation of environments containing visual landmarks, called the boundary place graph. This representation records the set of recognizable objects (landmarks) that are visible from the boundary of each configuration space obstacle. The exploration algorithm constructs the boundary place graph incrementally from sensor data. Once the robot has completely explored an environment, it can use the constructed representation to carry out further navigation tasks. We provide a necessary and sufficient condition under which the algorithm is guaranteed to discover all landmarks. This algorithm has been implemented on our mobile robot platform RJ, and results from these experiments are presented.

Framed-quadtree path planning for mobile robots operating in sparse environments

Abstract: Mobile robots operating in vast outdoor unstructured environments often only have incomplete maps and must deal with new objects found during traversal. Path planning in such sparsely occupied regions must be incremental to accommodate new information, and, must use efficient representations. In previous work we have developed an optimal method D* to plan paths when the environment is not known ahead of time, but, rather is discovered as the robot moves around. To date, D* has been applied to a uniform grid representation for obstacles and free space. In this paper we propose the use of D* with framed quadtrees to improve the efficiency of planning paths in sparse environments. The new system has been tested in simulation as well on an autonomous jeep, equipped with local obstacle avoidance capabilities. We show how the use of framed quadtrees improves performance in terms of path length, computation speed, and memory requirements.

A hybrid collision avoidance method for mobile robots

Abstract: Proposes a hybrid approach to the problem of collision avoidance for indoor mobile robots. The /spl mu/DWA (model-based dynamic window approach) integrates sensor data from various sensors with information extracted from a map of the environment, to generate collision-free motion. A novel integration rule ensures that with high likelihood, the robot avoids collisions with obstacles not detectable with its sensors, even if it is uncertain about its position. The approach was implemented and tested extensively as part of an installation, in which a mobile robot gave interactive tours to visitors of the "Deutsches Museum Bonn." Here our approach was essential for the success of the entire mission, because a large number of ill-shaped obstacles prohibited the use of purely sensor-based methods for collision avoidance.

Goal-Directed Navigation for Animated Characters Using Real-Time Path Planning and Control

Abstract: This paper presents a new technique for computing collision-free navigation motions from task-level commands for animated human characters in interactive virtual environments. The algorithm implementation utilizes the hardware rendering pipeline commonly found on graphics accelerator cards to perform fast 2D motion planning. Given a 3D geometric description of an animated character and a level-terrain environment, collision-free navigation paths can be computed between initial and goal locations at interactive rates. Speed is gained by leveraging the graphics hardware to quickly project the obstacle geometry into a 2D bitmap for planning. The bitmap may be searched by any number of standard dynamic programming techniques to produce a final path. Cyclic motion capture data is used along with a simple proportional derivative controller to animate the character as it follows the computed path. The technique has been implemented on an SGI Indigo2 workstation and runs at interactive rates. It allows for real-time modification of the goal locations and obstacle positions for multiple characters in complex environments composed of more than 15,000 triangles.

Practical Motion Planning in Robotics: Current Approaches and Future Directions

Abstract: From the Publisher: Robot motion planning deals with the ability of robots to plot their own collision-free motion. Recent research emphasis has been focused on developing practical motion planners for real manipulator arms with direct industrial relevance. This book concentrates on practical aspects of motion planning for real manipulator arms and is intended to narrow the gap between research and practice. Taking a comprehensive look at the motion-planning problem the work describes algorithms for obstacle identification and avoidance, collision avoidance, path planning.

SANDROS: a dynamic graph search algorithm for motion planning

Abstract: We present a general search strategy called SANDROS for motion planning, and its applications to motion planning for three types of robots: (1) manipulator; (2) rigid object; and (3) multiple rigid objects. SANDROS is a dynamic-graph search algorithm, and can be described as a hierarchical, nonuniform-multiresolution, best-first search to find a heuristically short motion in the configuration space. The SANDROS planner is resolution complete, and its computation time is commensurate with the problem difficulty measured empirically by the solution-path complexity. For many realistic problems involving a manipulator or a rigid object with six degrees of freedom, its computation times are under 1 minute for easy problems involving wide free space, and several minutes for relatively hard problems.

An analytically tractable potential field model of free space and its application in obstacle avoidance

Abstract: An analytically tractable potential field model of free space is presented. The model assumes that the border of every two dimensional (2D) region is uniformly charged. It is shown that the potential and the resulting repulsion (force and torque) between polygonal regions can he calculated in closed form. By using the Newtonian potential function, collision avoidance between object and obstacle thus modeled is guaranteed in a path planning problem. A local planner is developed for finding object paths going through narrow areas of free space where the obstacle avoidance is most important. Simulation results show that not only does individual object configuration of a path obtained with the proposed approach avoid obstacles effectively, the configurations also connect smoothly into a path.

Planning sub-optimal and continuous-curvature paths for car-like robots

Abstract: Deals with path planning for car-like robots. Usual planners compute paths made of circular arcs tangentially connected by line segments, as these paths are locally optimal. The drawback of these paths is that their curvature profile is not continuous: to follow them precisely, a vehicle must stop and reorient its directing wheels at each curvature discontinuity (transition segment-circle). To remove this limitation, a new path planning problem is proposed: two curvature constraints are added to the classical kinematic constraints taken into account. Thus, the curvature must remain continuous, and its derivative is bounded (as the car-like robot can reorient its directing wheels with a limited speed only). For this problem, the existence of solutions and the characterization of those of optimal length are shown. A method solving the forward-only problem (i.e. the problem for a car moving only forward) is then presented, and this method is compared to the classical one w.r.t. the complexity and computation time, the length of the generated paths and the quality of the tracking.

Multiresolution rough terrain motion planning

Abstract: We describe a new approach to the problem of motion planning for mobile robots on natural rough terrain. Our approach computes a multiresolution representation of the terrain using wavelets, and hierarchically plans the path through sections which are well approximated on coarser levels and relatively smooth. Unlike most methods, the hierarchical approximation errors are used explicitly in a cost function to distinguish preferred terrain sections. The error is computed using the corresponding wavelet coefficients. We also propose a new nonscalar path cost measure based on the sorted terrain costs along the path. This measure can be incorporated into standard global path search algorithms and yields paths which avoid high cost terrain areas when possible. Additional constraints for specific robots can be integrated into this approach for efficient hierarchical motion planning on rough terrain. We present the algorithms and experimental results for real terrain data.

Simultaneous map building and localization for mobile robots: a multisensor fusion approach

Abstract: During mobile robot navigation, position estimates obtained by odometry drift with time, therefore becoming unrealistic and useless. This work enhances the use of external mechanisms by considering a multisensor system, composed of a 2D laser rangefinder and an off-the-shelf CCD camera, which provides redundancy and assures reliability and precision of the observed features. We simultaneously consider both the map building and the localization problems using a state vector approach, which is related to the location estimations of both the robot and the map features, whilst its covariance matrix reflects the relationships between them. Relevance and importance of its off-diagonal elements is demonstrated by their contributions to "backwards estimations" whenever the vehicle returns to places in the navigation area which have been already visited and learned. Real experiments are presented, considering a LabMate mobile robot navigating in an static indoor environment.