Showing papers on "Mobile robot published in 2005"

Coordinated multi-robot exploration

Abstract: In this paper, we consider the problem of exploring an unknown environment with a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of the environment. We present an approach for the coordination of multiple robots, which simultaneously takes into account the cost of reaching a target point and its utility. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced. In this way, different target locations are assigned to the individual robots. We furthermore describe how our algorithm can be extended to situations in which the communication range of the robots is limited. Our technique has been implemented and tested extensively in real-world experiments and simulation runs. The results demonstrate that our technique effectively distributes the robots over the environment and allows them to quickly accomplish their mission.

Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling

Abstract: Recently Rao-Blackwellized particle filters have been introduced as effective means to solve the simultaneous localization and mapping (SLAM) problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper we present adaptive techniques to reduce the number of particles in a Rao-Blackwellized particle filter for learning grid maps. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. This drastically decrease the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out re-sampling operations which seriously reduces the problem of particle depletion. Experimental results carried out with mobile robots in large-scale indoor as well as in outdoor environments illustrate the advantages of our methods over previous approaches.

Fast replanning for navigation in unknown terrain

Abstract: Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz' Focussed Dynamic A/sup */ (D/sup */) is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan faster than planning from scratch since it modifies its previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to D/sup */ that determines the same paths and thus moves the robot in the same way but is algorithmically different. D/sup */ Lite is simple, can be rigorously analyzed, extendible in multiple ways, and is at least as efficient as D/sup */. We believe that our results will make D/sup */-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.

Anytime dynamic A*: an anytime, replanning algorithm

Abstract: We present a graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion. Our algorithm tunes the quality of its solution based on available search time, at every step reusing previous search efforts. When updated information regarding the underlying graph is received, the algorithm incrementally repairs its previous solution. The result is an approach that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems. We present theoretical analysis of the algorithm, experimental results on a simulated robot kinematic arm, and two current applications in dynamic path planning for outdoor mobile robots.

Vision-based global localization and mapping for mobile robots

Abstract: We have previously developed a mobile robot system which uses scale-invariant visual landmarks to localize and simultaneously build three-dimensional (3-D) maps of unmodified environments. In this paper, we examine global localization, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive visual landmarks in the current frame to a database map. A Hough transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more efficient for matching specific features, but much worse for matching nonspecific features. Moreover, robust global localization can be achieved by matching a small submap of the local region built from multiple frames. This submap alignment algorithm for global localization can be applied to map building, which can be regarded as alignment of multiple 3-D submaps. A global minimization procedure is carried out using the loop closure constraint to avoid the effects of slippage and drift accumulation. Landmark uncertainty is taken into account in the submap alignment and the global minimization process. Experiments show that global localization can be achieved accurately using the scale-invariant landmarks. Our approach of pairwise submap alignment with backward correction in a consistent manner produces a better global 3-D map.

Off-Road Obstacle Avoidance through End-to-End Learning

Abstract: We describe a vision-based obstacle avoidance system for off-road mobile robots. The system is trained from end to end to map raw input images to steering angles. It is trained in supervised mode to predict the steering angles provided by a human driver during training runs collected in a wide variety of terrains, weather conditions, lighting conditions, and obstacle types. The robot is a 50cm off-road truck, with two forward-pointing wireless color cameras. A remote computer processes the video and controls the robot via radio. The learning system is a large 6-layer convolutional network whose input is a single left/right pair of unprocessed low-resolution images. The robot exhibits an excellent ability to detect obstacles and navigate around them in real time at speeds of 2 m/s.

What is a robot companion - friend, assistant or butler?

Abstract: The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child/animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions.

Autonomous Robots: From Biological Inspiration to Implementation and Control

Abstract: An introduction to the science and practice of autonomous robots that reviews over 300 current systems and examines the underlying technology.

Robomote: enabling mobility in sensor networks

Abstract: Severe energy limitations, and a paucity of computation pose a set of difficult design challenges for sensor networks. Recent progress in two seemingly disparate research areas namely, distributed robotics and low power embedded systems has led to the creation of mobile (or robotic) sensor networks. Autonomous node mobility brings with it its own challenges, but also alleviates some of the traditional problems associated with static sensor networks. We illustrate this by presenting the design of the robomote, a robot platform that functions as a single mobile node in a mobile sensor network. We briefly describe two case studies where the robomote has been used for table top experiments with a mobile sensor network.

Learning Motion Patterns of People for Compliant Robot Motion

Abstract: Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve its behavior. In this paper we propose a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders are clustered using the expectation maximization algorithm. Based on the result of the clustering process, we derive a hidden Markov model that is applied to estimate the current and future positions of persons based on sensory input. We also describe how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot. We present several experiments carried out in different environments with a mobile robot equipped with a laser-range scanner and a camera system. The results demonstrate that our approach can reliably learn motion patterns of persons, can robustly estimate and predict positions of persons, and can be used to improve the navigation behavior of a mobile robot.

Differential-drive in-pipe robot for moving inside urban gas pipelines

Abstract: Pipelines for the urban gas-supply system require a robot possessing outstanding mobility and advanced control algorithms, since they are configured with various pipeline elements, such as straight pipelines, elbows, and branches. We present a comprehensive work for moving inside underground urban gas pipelines with a miniature differential-drive in-pipe robot, called the Multifunctional Robot for IN-pipe inSPECTion (MRINSPECT) IV. MRINSPECT IV has been developed for the inspection of urban gas pipelines with a nominal 4-in inside diameter. The mechanism for steering with differential-drive wheels, arranged three-dimensionally, allows it to easily adapt to most of the existing configurations of pipelines, as well as providing excellent mobility during navigation. After carrying out analysis for fittings in pipelines, mathematical descriptions of their geometries are presented, which make it possible to estimate the movement patterns of the robot while passing through the fittings. Also, we propose a method of controlling the robot by modulating speeds of driving wheels that is applicable without sophisticated sensory information. To confirm the effectiveness of the proposed method, experiments are performed, and supplementary considerations on the design of the in-pipe robot are discussed.

Footstep Planning for the Honda ASIMO Humanoid

Abstract: Despite the recent achievements in stable dynamic walking for many humanoid robots, relatively little navigation autonomy has been achieved. In particular, the ability to autonomously select foot placement positions to avoid obstacles while walking is an important step towards improved navigation autonomy for humanoids. We present a footstep planner for the Honda ASIMO humanoid robot that plans a sequence of footstep positions to navigate toward a goal location while avoiding obstacles. The possible future foot placement positions are dependent on the current state of the robot. Using a finite set of state-dependent actions, we use an A* search to compute optimal sequences of footstep locations up to a time-limited planning horizon. We present experimental results demonstrating the robot navigating through both static and dynamic known environments that include obstacles moving on predictable trajectories.

A multilevel relaxation algorithm for simultaneous localization and mapping

Abstract: This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.

Designing robots for long-term social interaction

Abstract: Valerie the roboceptionist is the most recent addition to Carnegie Mellon's social robots project. A permanent installation in the entranceway to Newell-Simon hall, the robot combines useful functionality - giving directions, looking up weather forecasts, etc. - with an interesting and compelling character. We are using Valerie to investigate human-robot social interaction, especially long-term human-robot "relationships". Over a nine-month period, we have found that many visitors continue to interact with the robot on a daily basis, but that few of the individual interactions last for more than 30 seconds. Our analysis of the data has indicated several design decisions that should facilitate more natural human-robot interactions.

The vSLAM Algorithm for Robust Localization and Mapping

Abstract: This paper presents the Visual Simultaneous Localization and Mapping (vSLAMTM) algorithm, a novel algorithm for simultaneous localization and mapping (SLAM). The algorithm is vision-and odometry-based, and enables low-cost navigation in cluttered and populated environments. No initial map is required, and it satisfactorily handles dynamic changes in the environment, for example, lighting changes, moving objects and/or people. Typically, vSLAM recovers quickly from dramatic disturbances, such as “kidnapping”.

Revisiting trilateration for robot localization

Abstract: Locating a robot from its distances, or range measurements, to three other known points or stations is a common operation, known as trilateration. This problem has been traditionally solved either by algebraic or numerical methods. An approach that avoids the direct algebrization of the problem is proposed here. Using constructive geometric arguments, a coordinate-free formula containing a small number of Cayley-Menger determinants is derived. This formulation accommodates a more thorough investigation of the effects caused by all possible sources of error, including round-off errors, for the first time in this context. New formulas for the variance and bias of the unknown robot location estimation, due to station location and range measurements errors, are derived and analyzed. They are proved to be more tractable compared with previous ones, because all their terms have geometric meaning, allowing a simple analysis of their asymptotic behavior near singularities.

Detection of multiple source locations using a glowworm metaphor with applications to collective robotics

Abstract: This paper presents a glowworm swarm based algorithm that finds solutions to optimization of multiple optima continuous functions. The algorithm is a variant of a well-known ant-colony optimization (ACO) technique, but with several significant modifications. Similar to how each moving region in the ACO technique is associated with a pheromone value, the agents in our algorithm carry a luminescence quantity along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luminescence and have a circular sensor range. The glowworms depend on a local-decision domain to compute their movements. Simulations demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a multimodal function. The above optimization scenario solves problems where a collection of autonomous robots is used to form a mobile sensor network. In particular, we address the problem of detecting multiple sources of a general nutrient profile that is distributed spatially on a two dimensional workspace using multiple robots.

Multi-hierarchical semantic maps for mobile robotics

Abstract: The success of mobile robots, and particularly of those interfacing with humans in daily environments (e.g., assistant robots), relies on the ability to manipulate information beyond simple spatial relations. We are interested in semantic information, which gives meaning to spatial information like images or geometric maps. We present a multi-hierarchical approach to enable a mobile robot to acquire semantic information from its sensors, and to use it for navigation tasks. In our approach, the link between spatial and semantic information is established via anchoring. We show experiments on a real mobile robot that demonstrate its ability to use and infer new semantic information from its environment, improving its operation.

Dynamic maps for long-term operation of mobile service robots

Abstract: This paper introduces a dynamic map for mobile robots that adapts continuously over time. It resolves the stability plasticity dilemma (the tradeoff between adaptation to new patterns and preservation of old patterns) by representing the environment over multiple time scales simultaneously (five in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the time scale. Robust statistics are used to interpret the samples. It is shown that this approach can track both stationary and non-stationary elements of the environment, covering the full spectrum of variations from moving objects to structural changes. The method was evaluated in a five-week experiment in a real dynamic environment. Experimental results show that the resulting map is stable, improves its quality over time and adapts to changes.

Prioritized motion planning for multiple robots

Abstract: In this paper we address the problem of motion planning for multiple robots. We introduce a prioritized method, based on a powerful method for motion planning in dynamic environments, recently developed by the authors. Our approach is generically applicable: there is no limitation on the number of degrees of freedom of each of the robots, and robots of various types - for instance free-flying robots and articulated robots - can be used simultaneously. Results show that high-quality paths can be produced in less than a second of computation time, even in confined environments involving many robots. We examine three issues in particular in this paper: the assignment of priorities to the robots, the performance of prioritized planning versus coordinated planning, and the influence of the extent by which the robot motions are constrained on the performance of the method. Results are reported in terms of both running time and the quality of the paths produced.

Human-robot teaming for search and rescue

Abstract: This work establishes an architecture for Urban Search and Rescue and a methodology for mixing real-world and simulation-based testing. A sensor suite and sensor fusion algorithm for robust victim detection permits aggregation of sensor readings from various sensors on multiple robots. We have embarked on a research program focusing on the enabling technologies of effective USAR robotic rescue devices. The program is also researching system-level design, evaluation, and refinement of USAR rescue architectures that include teams of sensor-laden robots and human rescuers. In this paper, we present highlights from our research, which include our multiagent system (MAS) infrastructure, our simulation environment, and our approach to sensor fusion and interface design for effective robotic control.

Node localization using mobile robots in delay-tolerant sensor networks

Abstract: We present a novel scheme for node localization in a delay-tolerant sensor network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a robust extended Kalman filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1 m in a large indoor setting.

A comparison of line extraction algorithms using 2D laser rangefinder for indoor mobile robotics

Abstract: This paper presents an experimental evaluation of different line extraction algorithms on 2D laser scans for indoor environment. Six popular algorithms in mobile robotics and computer vision are selected and tested. Experiments are performed on 100 real data scans collected in an office environment with a map size of 80m /spl times/ 50m. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with the ground truth using standard statistical methods.

Validating human-robot interaction schemes in multitasking environments

Abstract: The ability of robots to autonomously perform tasks is increasing. More autonomy in robots means that the human managing the robot may have available free time. It is desirable to use this free time productively, and a current trend is to use this available free time to manage multiple robots. We present the notion of neglect tolerance as a means for determining how robot autonomy and interface design determine how free time can be used to support multitasking, in general, and multirobot teams, in particular. We use neglect tolerance to 1) identify the maximum number of robots that can be managed; 2) identify feasible configurations of multirobot teams; and 3) predict performance of multirobot teams under certain independence assumptions. We present a measurement methodology, based on a secondary task paradigm, for obtaining neglect tolerance values that allow a human to balance workload with robot performance.

Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

Abstract: We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two grid maps provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and real robots experiments show the efficiency of our approach and also show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.

Temporal Logic Motion Planning for Mobile Robots

Abstract: In this paper, we consider the problem of robot motion planning in order to satisfy formulas expressible in temporal logics. Temporal logics naturally express traditional robot specifications such as reaching a goal or avoiding an obstacle, but also more sophisticated specifications such as sequencing, coverage, or temporal ordering of different tasks. In order to provide computational solutions to this problem, we first construct discrete abstractions of robot motion based on some environmental decomposition. We then generate discrete plans satisfying the temporal logic formula using powerful model checking tools, and finally translate the discrete plans to continuous trajectories using hybrid control. Critical to our approach is providing formal guarantees ensuring that if the discrete plan satisfies the temporal logic formula, then the continuous motion also satisfies the exact same formula.

Supervised Learning of Places from Range Data using AdaBoost

Abstract: This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of place improves the capabilities of a mobile robot in various domains including localization, path-planning, or human-robot interaction. Our approach uses AdaBoost, a supervised learning algorithm, to train a set of classifiers for place recognition based on laser range data. In this paper we describe how this approach can be applied to distinguish between rooms, corridors, doorways, and hallways. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various environments.

Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization

Abstract: In this paper, we present a vision-based approach to mobile robot localization that integrates an image-retrieval system with Monte Carlo localization. The image-retrieval process is based on features that are invariant with respect to image translations and limited scale. Since it furthermore uses local features, the system is robust against distortion and occlusions, which is especially important in populated environments. To integrate this approach with the sample-based Monte Carlo localization technique, we extract for each image in the database a set of possible viewpoints using a two-dimensional map of the environment. Our technique has been implemented and tested extensively. We present practical experiments illustrating that our approach is able to globally localize a mobile robot, to reliably keep track of the robot's position, and to recover from localization failures. We furthermore present experiments designed to analyze the reliability and robustness of our approach with respect to larger errors in the odometry.

Behavior-Grounded Representation of Tool Affordances

Abstract: This paper introduces a novel approach to representing and learning tool affordances by a robot. The tool representation described here uses a behavior-based approach to ground the tool affordances in the behavioral repertoire of the robot. The representation is learned during a behavioral babbling stage in which the robot randomly chooses different exploratory behaviors, applies them to the tool, and observes their effects on environmental objects. The paper shows how the autonomously learned affordance representation can be used to solve tool-using tasks by dynamically sequencing the exploratory behaviors based on their expected outcomes. The quality of the learned representation was tested on extension-of-reach tool-using tasks.