This paper describes the dynamic window approach to reactive collision avoidance for mobile robots equipped with synchro-drives. The approach is derived directly from the motion dynamics of the robot and is therefore particularly well-suited for robots operating at high speed. It differs from previous approaches in that the search for commands controlling the translational and rotational velocity of the robot is carried out directly in the space of velocities. The advantage of our approach is that it correctly and in an elegantway incorporates the dynamics of the robot. This is done by reducing the search space to the dynamic window, which consists of the velocities reachable within a short time interval. Within the dynamic window the approach only considers admissible velocities yielding a trajectory on which the robot is able to stop safely. Among these velocities the combination of translational and rotational velocity is chosen by maximizing an objective function. The objective function includes a measure of progress towards a goal location, the forward velocity of the robot, and the distance to the next obstacle on the trajectory. In extensive experiments the approach presented here has been found to safely control our mobile robot RHINO with speeds of up to 95 cm/sec, in populated and dynamic environments.

In manufacturing, it is often necessary to orient parts prior to packing or assembly. We say that a planar part is polygonal if its convex hull is a polygon. We consider the following problem: given a list of n vertices describing a polygonal part whose initial orientation is unknown, find the shortest sequence of mechanical gripper actions that is guaranteed to orient the part up to symmetry in its convex hull. We show that such a sequence exists for any polygonal part by giving an O#n log n# algorithm for finding the sequence. Since the gripper actions do not require feedback, this result implies that any polygonal part can be oriented without sensors.

Geometric uncertainty is unavoidable when programming robots for physical applications. We propose a stochastic framework for manipulation planning where plans are ranked on the basis of expected cost. That is, we express the desirability of states and actions with a cost function and describe uncertainty with probability distributions. We illustrate the approach with a new design for a programmable parts feeder, a mechanism that orients two-dimensional parts using a sequence of open-loop mechanical motions. We present a planning algorithm that accepts an n-sided polygonal part as input and, in time O(n²), generates a stochastically optimal plan for orienting the part.

A major thrust of industrial automation today is towards rapidly configuring assembly lines to manufacture products described by a CAD model using parametrized modular components such as conveyer belts, low degrees of freedom robot arms, modular fixtures, flexible parts feeders, light beams, and simple 2D vision systems. A number of challenging algorithmic problems arise with respect to the efficient design, simulation, and implementation of such assembly lines. We focus on the following specific issue arising in a typical application: parts arrive on a conveyer belt and have to be grasped by a robot arm of finite capacity and then delivered to specified delivery points for packaging or other processing. An intelligent choice of the order of picking up the parts can significantly reduce the total distance traveled by the robot arm and, consequently, its cycle time. We model this application as problems of the form: Given n identical parts initially located on a conveyer belt, and a ro...

The generalized Piano Mover's Problem can be extended in several ways, e.g. by taking in account kinematics and dynamic constraints, by allowing obstacles to move slightly, by asking for a path that can be seen as a good pay-off between safest and shortest path, by planning with uncertainty,... All these extensions are usually treated separately from each other in the literature. We will consider a combination of some of these extensions and we will investigate a direction of research that seems to be suitable for such combination. The approach plans in configuration space and is related to the potential field approach. The potential function however shall be replaced by an L 1 -distance-like function on a non-homogeneous higher-dimensional grid. This distance function shall be computed with a wavefront expansion algorithm. The approach seems to suit in a natural way to massively parallel implementation. 1 Introduction This text gives a short overview of the field of Robot Motion ...

Rapidly-Exploring Random Trees (RRTs) [LaValle98] have been the focus of a significant amount of research recently. This simple algorithm provides an efficient randomized technique to tackle the difficult problem of motion planning involving high dimensional spaces. Various extensions and modifications have also been proposed to the basic algorithm [LaValle01]. While results regarding the probabilistic completeness (assurance of finding a path if one exists as the number of nodes approaches infinity) and the convergence rate of the RRT planner exist [LaValle01], there has been very little quantitative evaluation of the RRTs with respect to path quality, number of nodes required to find a solution, and scaling power with increase in dimensionality. This project aims to rectify this deficit through an empirical study of RRTs applied to various environments and differing non-holonomic constraints. This project applies RRTs to a variety of robots, starting with the holonomic

Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmap-based method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sector-based localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while