Showing papers on "Mobile robot published in 1998"

Behavior-based formation control for multirobot teams

Abstract: New reactive behaviors that implement formations in multirobot teams are presented and evaluated. The formation behaviors are integrated with other navigational behaviors to enable a robotic team to reach navigational goals, avoid hazards and simultaneously remain in formation. The behaviors are implemented in simulation, on robots in the laboratory and aboard DARPA's HMMWV-based unmanned ground vehicles. The technique has been integrated with the autonomous robot architecture (AuRA) and the UGV Demo II architecture. The results demonstrate the value of various types of formations in autonomous, human-led and communications-restricted applications, and their appropriateness in different types of task environments.

ALLIANCE: an architecture for fault tolerant multirobot cooperation

Abstract: ALLIANCE is a software architecture that facilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled subtasks that may have ordering dependencies. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robot's own internal states. ALLIANCE is a fully distributed, behaviour-based architecture that incorporates the use of mathematically-modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup.

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

Abstract: This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach

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.

Controlling formations of multiple mobile robots

Abstract: We investigate feedback laws used to control multiple robots moving together in a formation. We propose a method for controlling formations that uses only local sensor-based information, in a leader-follower motion. We use methods of feedback linearization to exponentially stabilize the relative distance and orientation of the follower, and show that the zero dynamics of the system are also (asymptotically) stable. We demonstrate in simulation the use of these algorithms to control six robots moving around an obstacle. These types of control laws can be used to control arbitrarily large numbers of robots moving in very general types of formations.

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.

VFH+: reliable obstacle avoidance for fast mobile robots

Abstract: This paper presents further improvements on the earlier vector field histogram (VFH) method developed by Borenstein-Koren (1991) for real-time mobile robot obstacle avoidance. The enhanced method, called VFH+, offers several improvements that result in smoother robot trajectories and greater reliability. VFH+ reduces some of the parameter tuning of the original VFH method by explicitly compensating for the robot width. Also added in VFH+ is a better approximation of the mobile robot trajectory, which results in higher reliability.

Frontier-based exploration using multiple robots

Abstract: 1 ABSTRACT Frontier-based exploration directs mobile robots to regions on the boundary between unexplored space and space that is known to be open Previously, we have demonstrated that frontier-based exploration can be used to map indoor environments where walls and obstacles may be in arbitrary orientations In this paper, we show how frontier-based exploration can be extended to multiple robots In our approach, robots share perceptual information, but maintain separate global maps, and make independent decisions about where to explore This approach enables robots to make use of information from other robots to explore more effectively, but it also allows the team to be robust to the loss of individual robots We have implemented our multirobot exploration system on real robots, and we demonstrate that they can explore and map office environments as a team

An Architecture for Autonomy

Abstract: An autonomous robot offers a challenging and ideal field for the study of intelligent architectures. Autonomy within a rational be havior could be evaluated by the robot's effectiveness and robust ness in carrying out tasks in different and ill-known environments. It raises major requirements on the control architecture. Further more, a robot as a programmable machine brings up other archi tectural needs, such as the ease and quality of its specification and programming.This article describes an integrated architecture that allows a mobile robot to plan its tasks—taking into account temporal and domain constraints, to perform corresponding actions and to con trol their execution in real-time—while being reactive to possible events. The general architecture is composed of three levels: a de cision level, an execution level, and a functional level. The latter is composed of modules that embed the functions achieving sensor- data processing and effector control. The decision level is goal and event driven, a...

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.

Underactuated mechanical systems

Abstract: In this chapter we discuss the control of underactuated mechanical systems. Underactuated mechanical systems have fewer control inputs than degrees of freedom and arise in applications, such as space and undersea robots, mobile robots, flexible robots, walking, branchiating, and gymnastic robots. The Lagrangian dynamics of these systems may contain feedforward nonlinearities, non-minimum phase zero dynamics, nonholonomic constraints, and other properties that place this class of systems at the forefront of research in nonlinear control [22, 15]. A complete understanding of the control of these systems is therefore lacking. We will discuss the application of geometric nonlinear control, as well as methods based on passivity and energy for stabilization and tracking control. We will survey some of the existing results and point to open research problems.

A task description language for robot control

Abstract: Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task tree representation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot.

Artificial intelligence and mobile robots: case studies of successful robot systems

Abstract: Introduction - mobile robots - a proving ground for AI, R. Peter Bonasso et al. Part 1 Mapping and navigation: introduction, David Kortenkamp map learning and high-speed navigation in RHINO, Sebastian Thrun et al integrating high-speed obstacle avoidance, global path planning and vision sensing on a mobile robot, David Kortenkamp et al Dervish - an office-navigating robot, Ilah Nourbackhsh Xavier - a robot navigation architecture based on partially observable Markov decision process models, Sven Koenig and Reid G. Simmons. Part 2 Vision for mobile robots: introduction, Robin Murphy and David Kortenkamp the Polly system, Ian Horswill coordination and control of sensing for mobility using action-oriented perception, Robin R. Murphy vehicle guidance architecture for combined lane tracking and obstacle avoidance, Bill Schiller et al. Part 3 Mobile robot architectures: introduction, R. Peter Bonasso three-layer architectures, Erann Gat the Saphira architecture for autonomous mobile robots, Kurt Konolige and Karen Myers the animate agent architecture, R. James Firby et al cooperative multiagent robotic systems, Ronald C. Arkin and Tucker Balch toward advanced mobile robots for manufacturing, Huosheng Hu and Michael Brady the "Phoenix" autonomous underwater vehicle, Don Brutzmann et al.

An experimental comparison of localization methods

Abstract: Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filtering techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize several different types of environments, and add noise to both the dead-reckoning and the sensors. Analysis shows that, roughly, the scan-matching techniques are more efficient and accurate, but Markov localization is better able to cope with large amounts of noise. These results suggest hybrid methods that are efficient, accurate and robust to noise.

Multi-robot cooperation in the MARTHA project

Abstract: The MARTHA project objectives are the control and the management of a fleet of autonomous mobile robots for transshipment tasks in harbors, airports and marshalling yards. One of the most challenging and key problems of the MARTHA project is multi-robot cooperation. A general concept for the control of a large fleet of autonomous mobile robots has been developed, implemented and validated in the framework of the MARTHA project. This is the first study in the autonomous mobile robot field to add multi-robot cooperation capabilities to such a large fleet of robots.

Map learning and high-speed navigation in RHINO

Abstract: This chapter surveys basic methods for learning maps and high speed autonomous navigation for indoor mobile robots. The methods have been developed in our lab over the past few years, and most of them have been tested thoroughly in various indoor environments. The chapter is targeted towards researchers and engineers who attempt to build reliable mobile robot navigation software.

The self-reconfiguring robotic molecule

Abstract: We discuss a robotic module called a molecule. Molecules can be the basis for building self-reconfiguring robots. They support multiple modalities of locomotion and manipulation. We describe the design, functionality, and control of the molecule. We show how a set of molecules can aggregate as active three-dimensional structures that can move and change shape. Finally, we discuss our molecule experiments.

Development of an Autonomous Quadruped Robot for Robot Entertainment

Abstract: In this paper, we present Robot Entertainment as a new field of the entertainment industry using autonomous robots. For feasibility studies of Robot Entertainment, we have developed an autonomous quadruped robot, named MUTANT, as a pet-type robot. It has four legs, each of which has three degree-of-freedom, and a head which also has three degree-of-freedom. Micro camera, stereo microphone, touch sensors, and other sensor systems are coupled with newly developed behavior generation system, which has emotion module as its major components, and generates high complex and interactive behaviors. Agent architecture, real-world recognition technologies, software component technology, and some dedicated devices such as Micro Camera Unit, were developed and tested for this purpose. From the lessons learned from the development of MUTANT, we refined the design concept of MUTANT to derive requirements for a general architecture and a set of interfaces of robot systems for entertainment applications. Through these feasibility studies, we consider entertainment applications a significant target at this moment from both scientific and engineering points of view.

Dempster-Shafer theory for sensor fusion in autonomous mobile robots

Abstract: This article discusses Dempster-Shafer (DS) theory in terms of its utility for sensor fusion for autonomous mobile robots. It exploits two little used components of DS theory: the weight of conflict metric and the enlargement of the frame of discernment. The weight of conflict is used to measure the amount of consensus between different sensors. A lack of consensus leads the robot to either compensate within certain limits or investigate the problem further, adding robustness to the robot's operation. Enlarging the frame of discernment allows a modular decomposition of evidence. This decomposition offers the advantages of perceptual abstraction, and permits expert knowledge about the domain to be embedded in the frames of discernment, simplifying the construction and maintenance of the knowledge base. Six experiments using this Dempster-Shafer framework are presented. Data from four types of sensor data were collected by a mobile robot and fused with the sensor fusion effects (SFX) architecture.

Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing

Abstract: This paper describes a vision-based navigation method in an indoor environment for an autonomous mobile robot which can avoid obstacles. In this method, the self-localization of the robot is achieved by a model-based vision system, and nonstop navigation is realized by a retroactive position correction system. Stationary obstacles are avoided with single-camera vision and moving obstacles are detected with ultrasonic sensors. We report on experiments in a hallway using the YAMABICO 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.

Bayesian Landmark Learning for Mobile Robot Localization

Abstract: To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.

Mobile robot exploration and map-building with continuous localization

Abstract: Our research addresses how to integrate exploration and localization for mobile robots. A robot exploring and mapping an unknown environment needs to know its own location, but it may need a map in order to determine that location. In order to solve this problem, we have developed ARIEL, a mobile robot system that combines frontier based exploration with continuous localization. ARIEL explores by navigating to frontiers, regions on the boundary between unexplored space and space that is known to be open. ARIEL finds these regions in the occupancy grid map that it builds as it explores the world. ARIEL localizes by matching its recent perceptions with the information stored in the occupancy grid. We have implemented ARIEL on a real mobile robot and tested ARIEL in a real-world office environment. We present quantitative results that demonstrate that ARIEL can localize accurately while exploring, and thereby build accurate maps of its environment.

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

Abstract: This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likeliho...

Territorial multi-robot task division

Abstract: This work demonstrates the application of the distributed behavior-based approach to generating a multirobot controller for a group of mobile robots performing a clean-up and collection task. The paper studies a territorial approach to the task in which the robots are assigned individual territories that can be dynamically resized if one of the robots malfunctions, permitting the completion of the task. The described controller is implemented on a group of four IS Robotics R2e mobile robots. Using a collection of experimental robot data, we empirically derive and demonstrate most effective foraging in our domain, and show the decline of performance of the space division strategy with increased group size.