Showing papers on "Motion planning published in 1995"

Control of chained systems application to path following and time-varying point-stabilization of mobile robots

Abstract: Chain form systems have recently been introduced to model the kinematics of a class of nonholonomic mechanical systems. The first part of the study is centered on control design and analysis for nonlinear systems which can be converted to the chain form. Solutions to various control problems (open-loop steering, partial or complete state feedback stabilization) are either recalled, generalized, or developed. In particular, globally stabilizing time-varying feedbacks are derived, and a discussion of their convergence properties is provided. Application to the control of nonholonomic wheeled mobile robots is described in the second part of the study by considering the case of a car pulling trailers. >

Control of a nonholonomic mobile robot: backstepping kinematics into dynamics

Abstract: A dynamical extension that makes possible the integration of a kinematic controller and a torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and asymptotic stability is guaranteed by Lyapunov theory. Moreover, 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. A general structure for controlling a mobile robot results that can accommodate different control techniques ranging from a conventional computed-torque controller, when all dynamics are known, to adaptive controllers.

A sensor-based navigation for a mobile robot using fuzzy logic and reinforcement learning

Abstract: The proposed navigator consists of an avoidance behavior and goal-seeking behavior. Two behaviors are independently designed at the design stage and then combined them by a behavior selector at the running stage. A behavior selector using a bistable switching function chooses a behavior at each action step so that the mobile robot can go for the goal position without colliding with obstacles. Fuzzy logic maps the input fuzzy sets representing the mobile robot's state space determined by sensor readings to the output fuzzy sets representing the mobile robot's action space. Fuzzy rule bases are built through the reinforcement learning which requires simple evaluation data rather than thousands of input-output training data. Since the fuzzy rules for each behavior are learned through a reinforcement learning method, the fuzzy rule bases can be easily constructed for more complex environments. In order to find the mobile robot's present state, ultrasonic sensors mounted at the mobile robot are used. The effectiveness of the proposed method is verified by a series of simulations. >

Trajectory generation for the N-trailer problem using Goursat normal form

Abstract: Develops the machinery of exterior differential forms, more particularly the Goursat normal form for a Pfaffian system, for solving nonholonomic motion planning problems, i.e., motion planning for systems with nonintegrable velocity constraints. The authors use this technique to solve the problem of steering a mobile robot with n trailers. The authors present an algorithm for finding a family of transformations which will convert the system of rolling constraints on the wheels of the robot with n trailers into the Goursat canonical form. Two of these transformations are studied in detail. The Goursat normal form for exterior differential systems is dual to the so-called chained-form for vector fields that has been studied previously. Consequently, the authors are able to give the state feedback law and change of coordinates to convert the N-trailer system into chained-form. Three methods for planning trajectories for chained-form systems using sinusoids, piecewise constants, and polynomials as inputs are presented. The motion planning strategy is therefore to first convert the N-trailer system into Goursat form, use this to find the chained-form coordinates, plan a path for the corresponding chained-form system, and then transform the resulting trajectory back into the original coordinates. Simulations and frames of movie animations of the N-trailer system for parallel parking and backing into a loading dock using this strategy are included. >

A complete navigation system for goal acquisition in unknown environments

Abstract: Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, the authors developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect and avoid obstacles. The global system uses an incremental path planning algorithm to optimally replan the global path for each detected obstacle. A control arbiter steers the robot to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment.

Two manipulation planning algorithms

Abstract: This paper addresses the motion planning problem for a robot in presence of movable objects. Motion planning in this context appears as a constrained instance of the coordinated motion planning problem for multiple movable bodies. Indeed, a solution path in the configuration space of the robot and all movable objects is a sequence of transit paths where the robot moves alone and transfer paths where a movable object follows the robot. A major problem is to find the set of configurations where the robot has to grasp or release objects. The paper gives an overview of a general approach which consists in building a manipulation graph whose connected components characterize the existence of solutions. Two planners developed at LAAS-CNRS illustrate how the general formulation can be instantiated in specific cases.

Coordinated motion planning for multiple car-like robots using probabilistic roadmaps

Abstract: Presents a new approach to the multi-robot path planning problem, where a number of robots are to change their positions through feasible motions in the same static environment. The approach is probabilistically complete. That is, any solvable problem is guaranteed to be solved within a finite amount of time. The method, which is an extension of previous work on probabilistic single-robot planners, is very flexible in the sense that it can easily be applied to different robot types. In this paper the authors apply it to non-holonomic car-like robots, and present experimental results which show that the method is powerful and fast.

Coordinated motion planning for multiple car-like robots using probabilistic roadmaps

Abstract: Presents a new approach to the multi-robot path planning problem, where a number of robots are to change their positions through feasible motions in the same static environment. The approach is probabilistically complete. That is, any solvable problem is guaranteed to be solved within a finite amount of time. The method, which is an extension of previous work on probabilistic single-robot planners, is very flexible in the sense that it can easily be applied to different robot types. In this paper the authors apply it to non-holonomic car-like robots, and present experimental results which show that the method is powerful and fast.

Assembly maintainability study with motion planning

Abstract: Maintainability is an important issue in design where the accessibility of certain parts is determined for routine maintenance. In the past its study has been largely manual and labor intensive. Either by using physical mockup or computer animation with CAD models of a design, the task relies on a human to provide an access path for the part. In this paper, the authors present an automated approach to replace this manual process. By applying results from and developing extensions to research in motion planning and other fields, the authors demonstrate that an automated maintainability study system is feasible. The authors describe general extensions needed to adapt robotic motion planning techniques in maintainability studies. The authors show results from applying such a system to two classes of industrial application problems.

Randomized query processing in robot path planning

Abstract: : The subject of this paper is the analysis of a randomized preprocessing scheme that has been used for query processing in robot motion planning. The attractiveness of the scheme stems from its general applicability to virtually any motion planning problem, and its empirically observed success. In this paper we initiate a theoretical basis for explaining this empirical success. Under a simple assumption about the configuration space, we show that it is possible to perform a preprocessing step following which queries can be answered quickly. En route, we pose and give solutions to related problems on graph connectivity in the evasiveness model, and art gallery theorems.

Real-time modification of collision-free paths

Abstract: The modification of collision-free paths is proposed as the basis for a new framework to close the gap between global path planning and real-time sensor-based robot control. A physically-based model of a flexible string-like object, called an elastic band, is used to determine the modification of a path. The initial shape of the elastic is the free path generated by a planner. Subjected to artificial forces, the elastic band deforms in real time to a short and smooth path that maintains clearance from the obstacles. The elastic continues to deform as changes in the environment are detected by sensors, enabling the robot to accommodate uncertainties and react to unexpected and moving obstacles. While providing a tight connection between the robot and its environment, the elastic band preserves the global nature of the planned path. The greater part of this thesis deals with the design and implementation of elastic bands, with emphasis on achieving real-time performance even for robots with many degrees of freedom. To achieve these goals, we propose the concept of bubbles of free-space---a region of free-space around a given configuration of the robot generated from distance information. We also develop a novel algorithm for efficiently computing the distance between non-convex objects and a real-time algorithm for calculating a discrete approximation to the time-optimal parameterization of a path. These various developments are combined in a system that demonstrates the elastic band framework for a Puma 560 manipulator.

Dexterous manipulation through rolling

Abstract: Nonholonomic constraints in robotic systems are the source of some difficulties in planning and control; however, they also introduce interesting properties that can be practically exploited. In this paper we consider the design of a robot hand that achieves dexterity (i.e. the ability to arbitrarily locate and reorient manipulated objects) through rolling. Some interesting issues arising in planning and controlling motions of such device are considered, including exact planning for a spherical object and approximate planning for general objects. An experimental prototype of a three-plus-one degree of freedom hand achieving dexterous manipulation capabilities is described along with experimental results from manipulation.

Method for planning/controlling robot motion

Abstract: A computerized method/system is provided for planning motion of a robot within a free space confined by obstacles, from an initial position to a goal position. In executing the method/system, a plan is generated so that the robot can hold and maneuver a workpiece throughout a sequence of bending operations to be performed by a bending apparatus. A plurality of proposed movements to be made by the robot are proposed for an mth movement within a sequence of movements, and at least a portion of the robot and the obstacles that confine the free space are modeled. A determination is made as to whether a collision will occur between the robot and an obstacle for each proposed movement, and a plan is generated including the sequence of movements by choosing for each movement in the sequence of movements a proposed movement that will not result in a collision and that will bring the robot closer to the goal position. In choosing proposed movements, an estimated cost associated with each proposed movement may be taken into account. The estimated cost may be based upon an euclidian distance to the goal position from the position of the robot after the particular proposed movement is made as the mth movement, and/or the estimated cost may be determined as a function of the robot travel time from an (m -1)th movement to the mth movement. Different methods are provided for performing fine motion planning and gross motion planning.

A complete navigation system for goal acquisition in unknown environments

Abstract: Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, we have developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect obstacles and recommend steering commands to ensure robot safety. These obstacles are passed to the global system which stores them in a map of the environment. With each addition to the map, the global system uses an incremental path planning algorithm to optimally replan the global path and recommend steering commands to reach the goal. An arbiter combines the steering recommendations to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment.

Obstacle detection with omnidirectional image sensor HyperOmni Vision

Abstract: Described here is an image sensor with a hyperboloidal mirror for vision based navigation of a mobile robot. Its name is HyperOmni Vision. This sensing system can acquire an omnidirectional view around the robot, in real-time, with use of a hyperboloidal mirror. The authors show a prototype of a mobile robot system with HyperOmni Vision and a method for estimating the motion of the robot and finding unknown obstacles.

Robust real-time lane and road detection in critical shadow conditions

Abstract: This paper presents the vision-based road detection system currently installed onto the MOB-LAB land vehicle. Based on a geometrical transform and on a fast morphological processing, the system is capable to detect road markings even in extremely severe shadow allows to achieve a processing rate of about 17 Hz.

Random Networks in Configuration Space for Fast Path Planning

Abstract: In the main part of this dissertation we present a new path planning method which computes collision-free paths for robots of virtually any type moving among stationary obstacles. This method proceeds according to two phases: a preprocessing phase and a query phase. In the preprocessing phase, a probabilistic network is constructed and stored as a graph whose nodes correspond to collision-free configurations and edges to feasible paths between these configurations. These paths are computed using a fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the network; the network is then searched for a path joining these two nodes. The method is general and easy to implement. Increased efficiency can be achieved by tailoring some of its components (e.g. the local planner) to the considered robots. We apply the method to 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 ($\approx$150 MIPS), after relatively short preprocessing times (a few dozen to a few hundred seconds). In the second part of this dissertation, we present a new method for computing the obstacle map used in motion planning algorithms. The method computes a convolution of the workspace and the robot using the Fast Fourier Transform (FFT). It is particularly promising for workspaces with many and/or complicated obstacles. Furthermore, it is an inherently parallel method that can significantly benefit from existing experience and hardware on the FFT. In the third part of this dissertation, we consider a problem from assembly planning. In assembly planning we are interested in generating feasible sequences of motions that construct a mechanical product from its individual parts. The problem addressed is the following: given a planar assembly of polygons, decide if there is a proper subcollection of them that can be removed as a rigid body without colliding with or disturbing the other parts of the assembly. We prove that this problem is NP-complete. The result extends to several interesting variants of the problem.

Multi-robot cooperation through incremental plan-merging

Abstract: This paper presents an approach we have recently developed for multi-robot cooperation. It is based on a paradigm where robots incrementally merge their plans into a set of already coordinated plans. This is done through exchange of information about their current state and their future actions. This leads to a generic framework which can be applied to a variety of tasks and applications. The paradigm, called plan-merging paradigm, is presented and illustrated through its application to planning, execution and control of a large fleet of a autonomous mobile robots for load transport tasks in a structured environment.

Intelligent system for generating and executing a sheet metal bending plan

Abstract: An intelligent sheet metal bending system is disclosed, having a cooperative generative planning system. A planning module interacts with several expert modules to develop a bending plan. The planning module utilizes a state-space search algorithm. Computerized methods are provided for selecting a robot gripper and a repo gripper, and for determining the optimal placement of such grippers as they are holding a workpiece being formed by the bending apparatus. Computerized methods are provided for selecting tooling to be used by the bending apparatus, and for determining a tooling stage layout. An operations planning method is provided which allows the bending apparatus to be set up concurrently while time-consuming calculations, such as motion planning, are performed. An additional method or system is provided for positioning tooling stages by using a backgage guide member which guides placement of a tooling stage along the die rail of the bending apparatus. A method is provided for learning motion control offset values, and for eliminating the need for superfluous sensor-based control operations once the motion control offset values are known. The planning system may be used for facilitating functions such as design and assembly system, which may perform designing, costing, scheduling and/or manufacture and assembly.

Fuzzy Logic Wall Following of a Mobile Robot Based on the Concept of General Perception

Abstract: This paper presents a new approach to the wall following problem of a mobile robot. Local path planning is based on a so-called concept of general perception, which means that the robot is guided by a representation of its perception only. No map of the environment is used and walls and obstacles are not modelled either. A fuzzy controller then uses the information provided by the concept of general perception to guide the robot along walls of arbitrary shape and around obstacles which are treated as part of a wall, unless the distance between obstacle and wall allows a safe passage. This paper first introduces the concept of general perception and then explains the fuzzy controller in detail. All membership functions and the complete rule base are provided. The concept of general perception together with the fuzzy controller were tested on a real robot performing wall following and obstacle avoidance missions and some of the ensuing experimental results are presented at the end of the paper.

Real-time collision avoidance for redundant manipulators

Abstract: This paper presents a simple and robust approach to achieving collision avoidance for kinematically redundant manipulators at the control-loop level. The proposed scheme represents the obstacle avoidance requirement as inequality constraints in the manipulator workspace, and ensures that these inequalities are satisfied while the end-effector tracks the desired trajectory. The control scheme is the damped-least-squares formulation of the configuration control approach implemented as a kinematic controller. Computer simulation and experimental results are given for a Robotics Research 7 DOF redundant arm and demonstrate the collision avoidance capability for reaching inside a truss structure. These results confirm that the proposed approach provides a simple and effective method for real-time collision avoidance. >

Toward automatic robot instruction from perception-temporal segmentation of tasks from human hand motion

Abstract: This paper describes work on the temporal segmentation of grasping task sequences based on human hand motion. The segmentation process results in the identification of motion breakpoints separating the different constituent phases of the grasping task. A grasping task is composed of three basic phases: pregrasp phase, static grasp phase, and manipulation phase. We show that by analyzing the fingertip polygon area (which is an indication of the hand preshape) and the speed of hand movement (which is an indication of the hand transportation), we can divide a task into meaningful action segments such as approach object (which corresponds to the pregrasp phase), grasp object, manipulate object, place object, and depart (a special case of the pregrasp phase which signals the termination of the task). We introduce a measure called the volume sweep rate, which is the product of the fingertip polygon area and the hand speed. The profile of this measure is also used in the determination of the task breakpoints. >

On-line map building and navigation for autonomous mobile robots

Abstract: The problem of sensor-based robot motion planning in unknown environments is addressed. The proposed solution approach prescribes the repeated sequence of two fundamental processes: perception and navigation. In the former, the robot collects data from its sensors, builds local maps and integrates them with the global maps so far reconstructed, using fuzzy logic operators. During the navigation process, a planner based on the A* algorithm proposes a path from the current position to the goal. The robot moves along this path until one of two termination conditions is verified namely (i) an unexpected obstructing obstacle is detected, or (ii) the robot is leaving the area in which reliable information has been gathered. Experimental results are presented for a Nomad 200 mobile robot.

3-D path planning in a dynamic environment using an octree and an artificial potential field

Abstract: Proposes an efficient and simple method for finding a collision-free path and orientation for a rigid robot in a dynamic observable 3-D environment for unmanned aerial vehicles (UAVs). The method uses an octree for representing every object (robot and static/dynamic obstacles) in the environment without any distinction of the movability of objects; therefore, all objects are dealt with in the same way. The path of a robot from its starting position to the given goal with arbitrary motion (i.e., translation and rotation) in a 3-D environment is efficiently searched for in successive adjoining regions (octree white cells) by using the potential field generated from each black cell of the octree. The algorithm is simple, so it can easily be accelerated by using parallelization techniques. Experimental results obtained under several conditions, such as a point shaped robot and arbitrarily shaped robots in static/dynamic environments, are reported.

Motion planning for a steering-constrained robot through moderate obstacles

Abstract: Most mobile robots use steering mechanisms to guide their motion. Such mechanisms have stops that constrain the rate at which the robot can change its direction. We study a point robot in the plane subject to such constraints, in the presence of a class of obstacles we call moderate obstacles. We consider the case in which the robot has a reverse gear that allows it to back up, as well as the case when it does not, All our algorithms run in polynomial time and produce paths whose lengths are either optimal or within an additive constant of optimal.

A fuzzy system for indoor mobile robot navigation

Abstract: An autonomous mobile robot (AMR) has to cope with uncertain, incomplete or approximate information. Moreover it has to identify sudden perceptual situations to manoeuvre in real time. This paper describes a fuzzy rule based system (FRBS) approach controlling the movement of an autonomous mobile robot (MORIA). Difficult guiding and controlling properties of the robot are achieved by combining local actions and global strategies within the fuzzy controller. Different behaviors and perceptions are detected with the help of fuzzy rules and stored in fuzzy state variables (FSV). These state variables activate different fuzzy rule sets which in turn change the behavior of the fuzzy controller. >