Showing papers on "Mobile robot published in 2003"

Robotic mapping: a survey

Abstract: This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping. It describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems. The history of robotic mapping is also detailed, along with an extensive list of open research problems.

Matching robot appearance and behavior to tasks to improve human-robot cooperation

Abstract: A robot's appearance and behavior provide cues to the robot's abilities and propensities. We hypothesize that an appropriate match between a robot's social cues and its task improve the people's acceptance of and cooperation with the robot. In an experiment, people systematically preferred robots for jobs when the robot's humanlikeness matched the sociability required in those jobs. In two other experiments, people complied more with a robot whose demeanor matched the seriousness of the task.

An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements

Abstract: The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems [J.-S. Gutman et al., 1999]. This paper presents a novel algorithm that combines Rao-Blackwellized particle filtering and scan matching. In our approach scan matching is used for minimizing odometric errors during mapping. A probabilistic model of the residual errors of scan matching process is then used for the resampling steps. This way the number of samples required is seriously reduced. Simultaneously we reduce the particle depletion problem that typically prevents the robot from closing large loops. We present extensive experiments that illustrate the superior performance of our approach compared to previous approaches.

People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters

Abstract: One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects. Our method applies Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot. The approach has been implemented and tested on a real robot using laser-range data. We present experiments illustrating that our algorithm is able to robustly keep track of multiple people. The experiments furthermore show that the approach outperforms other techniques developed so far.

Perspectives on standardization in mobile robot programming: the Carnegie Mellon Navigation (CARMEN) Toolkit

Abstract: In this paper we describe our open-source robot control software, the Carnegie Mellon Navigation (CARMEN) Toolkit. The ultimate goals of CARMEN are to lower the barrier to implementing new algorithms on real and simulated robots and to facilitate sharing of research and algorithms between different institutions. In order for CARMEN to be as inclusive of various research approaches as possible, we have chosen not to adopt strict software standards, but to instead focus on good design practices. This paper outlines the lessons we have learned in developing these practices.

Robust sound source localization using a microphone array on a mobile robot

Abstract: The hearing sense on a mobile robot is important because it is omnidirectional and it does not require direct line-of-sight with the sound source. Such capabilities can nicely complement vision to help localize a person or an interesting event in the environment. To do so the robot auditory system must be able to work in noisy, unknown and diverse environmental conditions. In this paper, we present a robust sound source localization method in three-dimensional space using an array of 8 microphones. The method is based on time delay of arrival estimation. Results show that a mobile robot can localize in real time different types of sound sources over a range of 3 meters and with a precision of 3/spl deg/.

The bridge test for sampling narrow passages with probabilistic roadmap planners

Abstract: Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but narrow passages in a robot's configuration space create significant difficulty for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which boosts the sampling density inside narrow passages. The bridge test relies on simple tests of local geometry and can be implemented efficiently in high-dimensional configuration spaces. The strengths of the bridge test and uniform sampling complement each other naturally and are combined to generate the final hybrid sampling strategy. Our planner was tested on point robots and articulated robots in planar workspaces. Preliminary experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.

Theory and evaluation of human robot interactions

Abstract: Human-robot interaction (HRI) for mobile robots is still in its infancy. Most user interactions with robots have been limited to tele-operation capabilities where the most common interface provided to the user has been the video feed from the robotic platform and some way of directing the path of the robot. For mobile robots with semiautonomous capabilities, the user is also provided with a means of setting way points. More importantly, most HRI capabilities have been developed by robotics experts for use by robotics experts. As robots increase in capabilities and are able to perform more tasks in an autonomous manner we need to think about the interactions that humans will have with robots and what software architecture and user interface designs can accommodate the human in-the-loop. We also need to design systems that can be used by domain experts but not robotics experts. This paper outlines a theory of human-robot interaction and proposes the interactions and information needed by both humans and robots for the different levels of interaction, including an evaluation methodology based on situational awareness.

DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks

Abstract: We present a novel, laser range finder based algorithm for simultaneous localization and mapping (SLAM) for mobile robots. SLAM addresses the problem of constructing an accurate map in real time despite imperfect information about the robot's trajectory through the environment. Unlike other approaches that assume predetermined landmarks (and must deal with a resulting dataassociation problem) our algorithm is purely laser based. Our algorithm uses a particle filter to represent both robot poses and possible map configurations. By using a new map representation, which we call distributed particle (DP) mapping, we are able to maintain and update hundreds of candidate maps and robot poses efficiently. The worst-case complexity of our algorithm per laser sweep is log-quadratic in the number of particles we maintain and linear in the area swept out by the laser. However, in practice our run time is usually much less than that. Our technique contains essentially no assumptions about the environment yet it is accurate enough to close loops of 60m in length with crisp, perpendicular edges on corridors and minimal or no misalignment errors.

Map building with mobile robots in dynamic environments

Abstract: The problem of generating maps with mobile robots has received considerable attention over the past years. Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process. Dynamic objects, however, can lead to serious errors in the resulting maps such as spurious objects or misalignments due to localization errors. In this paper we consider the problem of creating maps with mobile robots in dynamic environments. We present a new approach that interleaves mapping and localization with a probabilistic technique to identify spurious measurements. In several experiments we demonstrate that our algorithm generates accurate 2D and 3D in different kinds of dynamic indoor and outdoor environments. We also use our algorithm to isolate the dynamic objects and generate 3D representation of them.

Real-time randomized path planning for robot navigation

Abstract: Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the robot's capabilities. This poses the problem of planning a path through a continuous domain. Several approaches have been used to address this problem each with some limitations, including state discretizations, planning efficiency, and lack of interleaved execution. Rapidly-exploring random trees (RRTs) are a recently developed algorithm on which fast continuous domain path planners can be based. In this work, we build a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost search, which improve replanning efficiency and the quality of generated paths. ERRT is successfully applied to a multi-robot system. Results demonstrate that ERRT is improves efficiency and performs competitively with existing heuristic and reactive real-time path planning approaches. ERRT has shown to offer a major step with great potential for path planning in challenging continuous, highly dynamic domains.

Awareness in human-robot interactions

Abstract: This paper provides a set of definitions that form a framework for describing the types of awareness that humans have of robot activities and the knowledge that robots have of the commands given them by humans. As a case study, we applied this human-robot interaction (HRI) awareness framework to our analysis of the HRI approaches used at an urban search and rescue competition. We determined that most of the critical incidents (e.g., damage done by robots to the test arena) were directly attributable to lack of one or more kinds of HRI awareness.

Collaboration, Dialogue, Human-Robot Interaction

Abstract: Teleoperation can be improved if humans and robots work as partners, exchanging information and assisting one another to achieve common goals. In this paper, we discuss the importance of collaboration and dialogue in human-robot systems. We then present collaborative control, a system model in which human and robot collaborate, and describe its use in vehicle teleoperation.

Stability analysis of M-dimensional asynchronous swarms with a fixed communication topology

Abstract: Coordinated dynamical swarm behavior occurs when certain types of animals forage for food or try to avoid predators. Analogous behaviors can occur in engineering systems (e.g., in groups of autonomous mobile robots or air vehicles). In this paper, we study a model of an M-dimensional (M/spl ges/2) asynchronous swarm with a fixed communication topology, where each member only communicate with fixed neighbors, to provide conditions under which collision-free convergence can be achieved with finite-size swarm members that have proximity sensors, and neighbor position sensors that only provide delayed position information. Moreover, we give conditions under which an M-dimensional asynchronous mobile swarm with a fixed communication topology following an "edge-leader" can maintain cohesion during movements even in the presence of sensing delays and asynchronism. In addition, the swarm movement flexibility is analyzed. Such stability analysis is of fundamental importance if one wants to understand the coordination mechanisms for groups of autonomous vehicles or robots, where intermember communication channels are less than perfect and collisions must be avoided.

Adaptive, non-singular path-following control of dynamic wheeled robots

Abstract: This paper derives a new type of control law to steer the dynamic model of a wheeled robot of unicycle type along a desired spatial path. The methodology adopted for path following control deals explicitly with vehicle dynamics and plant parameter uncertainty. Furthermore, it overcomes stringent initial condition constraints that are present in a number of path following control strategies described in the literature. This is done by controlling explicitly the rate of progression of a "virtual target" to be tracked along the path, thus bypassing the problems that arise when the position of the virtual target is simply defined by the projection of the actual vehicle on that path. The nonlinear adaptive control law proposed yields convergence of the (closed loop system) path following error trajectories to zero. Controller design relies on Lyapunov theory and backstepping techniques. Simulation results illustrate the performance of the control system proposed.

Statistical background subtraction for a mobile observer

Abstract: Statistical background modelling and subtraction has proved to be a popular and effective class of algorithms for segmenting independently moving foreground objects out from a static background, without requiring any a priori information of the properties of foreground objects. We present two contributions on this topic, aimed towards robotics where an active head is mounted on a mobile vehicle. In periods when the vehicle's wheels are not driven, camera translation is virtually zero, and background subtraction techniques are applicable. This is also highly relevant to surveillance and video conferencing. The first part presents an efficient probabilistic framework for when the camera pans and tilts. A unified approach is developed for handling various sources of error, including motion blur, subpixel camera motion, mixed pixels at object boundaries, and also uncertainty in background stabilisation caused by noise, unmodelled radial distortion and small translations of the camera. The second contribution regards a Bayesian approach to specifically incorporate uncertainty concerning whether the background has yet been uncovered by moving foreground objects. This is an important requirement during initialisation of a system. We cannot assume that a background model is available in advance since that would involve storing models for each possible position, in every room, of the robot's operating environment.. Instead the background mode must be generated online, very possibly in the presence of moving objects.

Evolving mobile robots able to display collective behaviors

Abstract: We present a set of experiments in which simulated robots are evolved for the ability to aggregate and move together toward a light target. By developing and using quantitative indexes that capture the structural properties of the emerged formations, we show that evolved individuals display interesting behavioral patterns in which groups of robots act as a single unit. Moreover, evolved groups of robots with identical controllers display primitive forms of situated specialization and play different behavioral functions within the group according to the circumstances. Overall, the results presented in the article demonstrate that evolutionary techniques, by exploiting the self-organizing behavioral properties that emerge from the interactions between the robots and between the robots and the environment, are a powerful method for synthesizing collective behavior.

Weighted line fitting algorithms for mobile robot map building and efficient data representation

Abstract: This paper presents an algorithm to find the line-based map that best fits sets of two-dimensional range scan data. To construct the map, we first provide an accurate means to fit a line segment to a set of uncertain points via maximum likelihood formalism. This scheme weights each point's influence on the fit according to its uncertainty, which is derived from sensor noise models. We also provide closed-form formulas for the covariance of the line fit, along with methods to transform line coordinates and covariances across robot poses. A Chi-squared based criterion for "knitting" together sufficiently similar lines can be used to merge lines directly (as we demonstrate) or as part of the framework for a line-based SLAM implementation. Experiments using a Sick LMS-200 laser scanner and a Nomad 200 mobile robot illustrate the effectiveness of the algorithm.

Generating whole body motions for a biped humanoid robot from captured human dances

Abstract: The goal of this study is a system for a robot to imitate human dances. This paper describes the process to generate whole body motions which can be performed by an actual biped humanoid robot. Human dance motions are acquired through a motion capturing system. We then extract symbolic representation which is made up of primitive motions: essential postures in arm motions and step primitives in leg motions. A joint angle sequence of the robot is generated according to these primitive motions. Then joint angles are modified to satisfy mechanical constraints of the robot. For balance control, the waist trajectory is moved to acquire dynamics consistency based on desired ZMP. The generated motion is tested on OpenHRP dynamics simulator. In our test, the Japanese folk dance, 'Jongara-bushi', was successfully performed by HRP-1S.

Multi-robot remote driving with collaborative control

Abstract: Multi-robot remote driving has traditionally been a difficult problem. Whenever an operator is forced to divide his limited resources (attention, cognition, etc.) among multiple robots, control becomes complicated and performance deteriorates as a result. Thus, we need to find ways to make command generation and coordination efficient, so that human-robot interaction becomes transparent and tasks are easy to perform. In this paper, we discuss how human-robot collaboration and dialogue provide an effective framework for achieving this.

Solving the robots gathering problem

Abstract: Consider a set of n > 2 simple autonomous mobile robots (decentralized, asynchronous, no common coordinate system, no identities, no central coordination, no direct communication, no memory of the past, deterministic) moving freely in the plane and able to sense the positions of the other robots We study the primitive task of gathering them at a point not fixed in advance (GATHERING PROBLEM) In the literature, most contributions are simulation-validated heuristics The existing algorithmic contributions for such robots are limited to solutions for n ≤ 4 or for restricted sets of initial configurations of the robots In this paper, we present the first algorithm that solves the GATHERING PROBLEM for any initial configuration of the robots

Formation control of nonholonomic mobile robots with omnidirectional visual servoing and motion segmentation

Abstract: We consider the problem of having a team of nonholonomic mobile robots follow a desired leader-follower formation using omnidirectional vision. By specifying the desired formation in the image plane, we translate the control problem into a separate visual servoing task for each follower. We use a rank constraint on the omnidirectional optical flows across multiple frames to estimate the position and velocities of the leaders in the image plane of each follower. We show that the direct feedback-linearization of the leader-follower dynamics suffers from degenerate configurations due to the nonholonomic constraints of the robots and the nonlinearity of the omnidirectional projection model. We therefore design a nonlinear tracking controller that avoids such degenerate configurations, while preserving the formation input-to-state stability. Our control law naturally incorporates collision avoidance by exploiting the geometry of omnidirectional cameras. We present simulations and experiments evaluating our omnidirectional vision-based formation control scheme.