Showing papers on "Mobile robot published in 2012"

Kalman Filter for Robot Vision: A Survey

Abstract: Kalman filters have received much attention with the increasing demands for robotic automation. This paper briefly surveys the recent developments for robot vision. Among many factors that affect the performance of a robotic system, Kalman filters have made great contributions to vision perception. Kalman filters solve uncertainties in robot localization, navigation, following, tracking, motion control, estimation and prediction, visual servoing and manipulation, and structure reconstruction from a sequence of images. In the 50th anniversary, we have noticed that more than 20 kinds of Kalman filters have been developed so far. These include extended Kalman filters and unscented Kalman filters. In the last 30 years, about 800 publications have reported the capability of these filters in solving robot vision problems. Such problems encompass a rather wide application area, such as object modeling, robot control, target tracking, surveillance, search, recognition, and assembly, as well as robotic manipulation, localization, mapping, navigation, and exploration. These reports are summarized in this review to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed in an abstract level.

Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments

Abstract: The combination of visual and inertial sensors has proved to be very popular in robot navigation and, in particular, Micro Aerial Vehicle (MAV) navigation due the flexibility in weight, power consumption and low cost it offers. At the same time, coping with the big latency between inertial and visual measurements and processing images in real-time impose great research challenges. Most modern MAV navigation systems avoid to explicitly tackle this by employing a ground station for off-board processing. In this paper, we propose a navigation algorithm for MAVs equipped with a single camera and an Inertial Measurement Unit (IMU) which is able to run onboard and in real-time. The main focus here is on the proposed speed-estimation module which converts the camera into a metric body-speed sensor using IMU data within an EKF framework. We show how this module can be used for full self-calibration of the sensor suite in real-time. The module is then used both during initialization and as a fall-back solution at tracking failures of a keyframe-based VSLAM module. The latter is based on an existing high-performance algorithm, extended such that it achieves scalable 6DoF pose estimation at constant complexity. Fast onboard speed control is ensured by sole reliance on the optical flow of at least two features in two consecutive camera frames and the corresponding IMU readings. Our nonlinear observability analysis and our real experiments demonstrate that this approach can be used to control a MAV in speed, while we also show results of operation at 40Hz on an onboard Atom computer 1.6 GHz.

A depth space approach to human-robot collision avoidance

Abstract: In this paper a real-time collision avoidance approach is presented for safe human-robot coexistence. The main contribution is a fast method to evaluate distances between the robot and possibly moving obstacles (including humans), based on the concept of depth space. The distances are used to generate repulsive vectors that are used to control the robot while executing a generic motion task. The repulsive vectors can also take advantage of an estimation of the obstacle velocity. In order to preserve the execution of a Cartesian task with a redundant manipulator, a simple collision avoidance algorithm has been implemented where different reaction behaviors are set up for the end-effector and for other control points along the robot structure. The complete collision avoidance framework, from perception of the environment to joint-level robot control, is presented for a 7-dof KUKA Light-Weight-Robot IV using the Microsoft Kinect sensor. Experimental results are reported for dynamic environments with obstacles and a human.

Depth camera based indoor mobile robot localization and navigation

Abstract: The sheer volume of data generated by depth cameras provides a challenge to process in real time, in particular when used for indoor mobile robot localization and navigation. We introduce the Fast Sampling Plane Filtering (FSPF) algorithm to reduce the volume of the 3D point cloud by sampling points from the depth image, and classifying local grouped sets of points as belonging to planes in 3D (the “plane filtered” points) or points that do not correspond to planes within a specified error margin (the “outlier” points). We then introduce a localization algorithm based on an observation model that down-projects the plane filtered points on to 2D, and assigns correspondences for each point to lines in the 2D map. The full sampled point cloud (consisting of both plane filtered as well as outlier points) is processed for obstacle avoidance for autonomous navigation. All our algorithms process only the depth information, and do not require additional RGB data. The FSPF, localization and obstacle avoidance algorithms run in real time at full camera frame rates (30Hz) with low CPU requirements (16%). We provide experimental results demonstrating the effectiveness of our approach for indoor mobile robot localization and navigation. We further compare the accuracy and robustness in localization using depth cameras with FSPF vs. alternative approaches that simulate laser rangefinder scans from the 3D data.

Optimal path planning of mobile robots: A review

Abstract: Mobile robots are increasingly used in automated industrial environments. There are also other applications like planet exploration, surveillance, landmine detection, etc. In all these applications, in order that the mobile robots perform their tasks, collision-free path planning is a prerequisite. This article provides an overview of the research progress in path planning of a mobile robot for off-line as well as on-line environments. Commonly used classic and evolutionary approaches of path planning of mobile robots have been addressed. Review shows that evolutionary optimization algorithms are computationally efficient and hence are increasingly being used in tandem with classic approaches while handling Non-deterministic Polynomial time hard (NP-hard) problems. Also, challenges involved in developing a computationally efficient path planning algorithm are addressed. Key words: Path planning, mobile robot, off-line environment, on-line environment, classic, evolutionary algorithms.

Real-time plane segmentation using RGB-D cameras

Abstract: Real-time 3D perception of the surrounding environment is a crucial precondition for the reliable and safe application of mobile service robots in domestic environments Using a RGB-D camera, we present a system for acquiring and processing 3D (semantic) information at frame rates of up to 30Hz that allows a mobile robot to reliably detect obstacles and segment graspable objects and supporting surfaces as well as the overall scene geometry Using integral images, we compute local surface normals The points are then clustered, segmented, and classified in both normal space and spherical coordinates The system is tested in different setups in a real household environment The results show that the system is capable of reliably detecting obstacles at high frame rates, even in case of obstacles that move fast or do not considerably stick out of the ground The segmentation of all planes in the 3D data even allows for correcting characteristic measurement errors and for reconstructing the original scene geometry in far ranges

Distributed Computing by Oblivious Mobile Robots

Abstract: The study of what can be computed by a team of autonomous mobile robots, originally started in robotics and AI, has become increasingly popular in theoretical computer science (especially in distributed computing), where it is now an integral part of the investigations on computability by mobile entities. The robots are identical computational entities located and able to move in a spatial universe; they operate without explicit communication and are usually unable to remember the past; they are extremely simple, with limited resources, and individually quite weak. However, collectively the robots are capable of performing complex tasks, and form a system with desirable fault-tolerant and self-stabilizing properties. The research has been concerned with the computational aspects of such systems. In particular, the focus has been on the minimal capabilities that the robots should have in order to solve a problem. This book focuses on the recent algorithmic results in the field of distributed computing by oblivious mobile robots (unable to remember the past). After introducing the computational model with its nuances, we focus on basic coordination problems: pattern formation, gathering, scattering, leader election, as well as on dynamic tasks such as flocking. For each of these problems, we provide a snapshot of the state of the art, reviewing the existing algorithmic results. In doing so, we outline solution techniques, and we analyze the impact of the different assumptions on the robots' computability power. Table of Contents: Introduction / Computational Models / Gathering and Convergence / Pattern Formation / Scatterings and Coverings / Flocking / Other Directions

Design and Rolling Locomotion of a Magnetically Actuated Soft Capsule Endoscope

Abstract: This paper proposes a magnetically actuated soft capsule endoscope (MASCE) as a tetherless miniature mobile robot platform for diagnostic and therapeutic medical applications inside the stomach. Two embedded internal permanent magnets and a large external magnet are used to actuate the robot remotely. The proposed MASCE has three novel features. First, its outside body is made of soft elastomer-based compliant structures. Such compliant structures can deform passively during the robot-tissue contact interactions, which makes the device safer and less invasive. Next, it can be actively deformed in the axial direction by using external magnetic actuation, which provides an extra degree of freedom that enables various advanced functions such as axial position control, drug releasing, drug injection, or biopsy. Finally, it navigates in three dimensions by rolling on the stomach surface as a new surface locomotion method inside the stomach. Here, the external attractive magnetic force is used to anchor the robot on a desired location, and the external magnetic torque is used to roll it to another location, which provides a stable, continuous, and controllable motion. The paper presents design and fabrication methods for the compliant structures of the robot with its axial deformation and position control capability. Rolling-based surface locomotion of the robot using external magnetic torques is modeled, and its feasibility is tested and verified on a synthetic stomach surface by using a magnetically actuated capsule endoscope prototype.

Apparatus and method for controlling force to be used for motion of surgical robot

Abstract: An apparatus and method for controlling a robot may scale a motion of a surgical robot based on a type of object gripped by the surgical robot. In the robot controlling method, by scaling the motion of the surgical robot based on the type of object gripped by the surgical robot, the surgical robot may automatically perform the motion on objects using an optimized force although a user does not control a force minutely based on the type of object gripped by the surgical robot.

Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments

Abstract: In this paper, we present controllers that enable mobile robots to persistently monitor or sweep a changing environment. The environment is modeled as a field that is defined over a finite set of locations. The field grows linearly at locations that are not within the range of a robot and decreases linearly at locations that are within range of a robot. We assume that the robots travel on given closed paths. The speed of each robot along its path is controlled to prevent the field from growing unbounded at any location. We consider the space of speed controllers that are parametrized by a finite set of basis functions. For a single robot, we develop a linear program that computes a speed controller in this space to keep the field bounded, if such a controller exists. Another linear program is derived to compute the speed controller that minimizes the maximum field value over the environment. We extend our linear program formulation to develop a multirobot controller that keeps the field bounded. We characterize, both theoretically and in simulation, the robustness of the controllers to modeling errors and to stochasticity in the environment.

Distributed Computing by Mobile Robots: Gathering

Abstract: Consider a set of $n>2$ identical mobile computational entities in the plane, called robots, operating in Look-Compute-Move cycles, without any means of direct communication. The Gathering Problem is the primitive task of all entities gathering in finite time at a point not fixed in advance, without any external control. The problem has been extensively studied in the literature under a variety of strong assumptions (e.g., synchronicity of the cycles, instantaneous movements, complete memory of the past, common coordinate system, etc.). In this paper we consider the setting without those assumptions, that is, when the entities are oblivious (i.e., they do not remember results and observations from previous cycles), disoriented (i.e., have no common coordinate system), and fully asynchronous (i.e., no assumptions exist on timing of cycles and activities within a cycle). The existing algorithmic contributions for such robots are limited to solutions for $n \leq 4$ or for restricted sets of initial configura...

Design and Locomotion Control of a Biomimetic Underwater Vehicle With Fin Propulsion

Abstract: As a novel biologically inspired underwater vehicle, a robotic manta ray (RoMan-II) has been developed for potential marine applications. Manta ray can perform diversified locomotion patterns in water by manipulating two wide tins. These motion patterns have been implemented on the developed fish robot, including swimming by flapping fins, turning by modulating phase relations of fins, and online transition of different motion patterns. The movements are achieved by using a model of artificial central pattern generators (CPGs) constructed with coupled nonlinear oscillators. This paper focuses on the analytical formulation of coupling terms in the CPG model and the implementation issues of the CPG-based control on the fish robot. The control method demonstrated on the manta ray robot is expected to be a frame- work that can tackle locomotion control problems in other types of multifin-actuated fish robots or more general robots with rhythmic movement patterns.

Using Socially Assistive Human–Robot Interaction to Motivate Physical Exercise for Older Adults

Abstract: In this paper, we present the design, implementation, and user study evaluation of a socially assistive robot (SAR) system designed to engage elderly users in physical exercise aimed at achieving health benefits and improving quality of life. We discuss our design methodology, which incorporates insights from psychology research in the area of intrinsic motivation, and focuses on maintaining engagement through personalized social interaction. We describe two user studies conducted to test the motivation theory in practice with our system. The first study investigated the role of praise and relational discourse in the exercise system by comparing a relational robot coach to a nonrelational robot coach. The second study evaluated participant preferences regarding user choice in the task scenario. Both studies served to evaluate the feasibility and overall effectiveness of the robot exercise system. The results of both studies are presented; they show a strong user preference for the relational over the nonrelational robot in terms of enjoyableness, companionship, and as an exercise coach, varying user preferences regarding choice, and high user ratings of the system across multiple metrics. The outcomes of the presented user studies, brought together, support the motivational capabilities of the robot, and demonstrate the viability and usefulness of the system in motivating exercise in elderly users.

Robot Motion Planning in Dynamic, Uncertain Environments

Abstract: This paper presents a strategy for planning robot motions in dynamic, uncertain environments (DUEs). Successful and efficient robot operation in such environments requires reasoning about the future evolution and uncertainties of the states of the moving agents and obstacles. A novel procedure to account for future information gathering (and the quality of that information) in the planning process is presented. To approximately solve the stochastic dynamic programming problem that is associated with DUE planning, we present a partially closed-loop receding horizon control algorithm whose solution integrates prediction, estimation, and planning while also accounting for chance constraints that arise from the uncertain locations of the robot and obstacles. Simulation results in simple static and dynamic scenarios illustrate the benefit of the algorithm over classical approaches. The approach is also applied to more complicated scenarios, including agents with complex, multimodal behaviors, basic robot-agent interaction, and agent information gathering.

Design and architecture of the unified modular snake robot

Abstract: The design of a hyper-redundant serial-linkage snake robot is the focus of this paper. The snake, which consists of many fully enclosed actuators, incorporates a modular architecture. In our design, which we call the Unified Snake, we consider size, weight, power, and speed tradeoffs. Each module includes a motor and gear train, an SMA wire actuated bistable brake, custom electronics featuring several different sensors, and a custom intermodule connector. In addition to describing the Unified Snake modules, we also discuss the specialized head and tail modules on the robot and the software that coordinates the motion.

A robot path planning framework that learns from experience

Abstract: We propose a framework, called Lightning, for planning paths in high-dimensional spaces that is able to learn from experience, with the aim of reducing computation time. This framework is intended for manipulation tasks that arise in applications ranging from domestic assistance to robot-assisted surgery. Our framework consists of two main modules, which run in parallel: a planning-from-scratch module, and a module that retrieves and repairs paths stored in a path library. After a path is generated for a new query, a library manager decides whether to store the path based on computation time and the generated path's similarity to the retrieved path. To retrieve an appropriate path from the library we use two heuristics that exploit two key aspects of the problem: (i) A correlation between the amount a path violates constraints and the amount of time needed to repair that path, and (ii) the implicit division of constraints into those that vary across environments in which the robot operates and those that do not. We evaluated an implementation of the framework on several tasks for the PR2 mobile manipulator and a minimally-invasive surgery robot in simulation. We found that the retrieve-and-repair module produced paths faster than planning-from-scratch in over 90% of test cases for the PR2 and in 58% of test cases for the minimally-invasive surgery robot.

Development of a Hand-Assist Robot With Multi-Degrees-of-Freedom for Rehabilitation Therapy

Abstract: This paper presents a virtual reality (VR)-enhanced new hand rehabilitation support system that enables patients to exercise alone. This system features a multi-degrees-of-freedom (DOF) motion assistance robot, a VR interface for patients, and a symmetrical master-slave motion assistance training strategy called "self-motion control," in which the stroke patient's healthy hand on the master side creates the assistance motion for the impaired hand on the slave side. To assist in performing the fine exercise motions needed for functional recovery of the impaired hand, the robot was constructed in an exoskeleton with 18 DOFs, to assist finger and thumb independent motions such as flexion/extension and abduction/adduction, thumb opposability, and hand-wrist co- ordinated motions. To enhance the effectiveness of the exercises, audio-visual instructions of each training motion using VR technology were designed with the input of clinician researchers. Experimental results from healthy subjects and patients show sufficient performance in the range of motion of the robot as well as sufficient assistance forces.

Bilateral Teleoperation of Groups of Mobile Robots With Time-Varying Topology

Abstract: In this paper, a novel decentralized control strategy for bilaterally teleoperating heterogeneous groups of mobile robots from different domains (aerial, ground, marine, and underwater) is proposed. By using a decentralized control architecture, the group of robots, which is treated as the slave side, is made able to navigate in a cluttered environment while avoiding obstacles, interrobot collisions, and following the human motion commands. Simultaneously, the human operator acting on the master side is provided with a suitable force feedback informative of the group response and of the interaction with the surrounding environment. Using passivity-based techniques, we allow the behavior of the group to be as flexible as possible with arbitrary split and join events (e.g., due to interrobot visibility/packet losses or specific task requirements) while guaranteeing the stability of the system. We provide a rigorous analysis of the system stability and steady-state characteristics and validate performance through human/hardware-in-the-loop simulations by considering a heterogeneous fleet of unmanned aerial vehicles (UAVs) and unmanned ground vehicles as a case study. Finally, we also provide an experimental validation with four quadrotor UAVs.

Chemical Sensing in Robotic Applications: A Review

Abstract: Robots are generally equipped with at least several different modalities of sensors. Vision and range sensors are the most popular, especially in mobile robots. On the other hand, olfaction (or chemical sensing in general) had long been ignored in the robotics community because of the technical difficulties involved in realizing artificial olfaction on robotic platforms. Over the past two decades, however, various attempts are made to use chemical sensors in robotic applications. With the help of chemical sensors, mobile robots can follow chemical trails laid on the ground, track chemical plumes to find their sources, and build distribution maps of chemical substances. This paper is intended to present a brief history and the current trends of the research in this emerging field.

Review article: locomotion systems for ground mobile robots in unstructured environments

Abstract: . The world market of mobile robotics is expected to increase substantially in the next 20 yr, surpassing the market of industrial robotics in terms of units and sales. Important fields of application are homeland security, surveillance, demining, reconnaissance in dangerous situations, and agriculture. The design of the locomotion systems of mobile robots for unstructured environments is generally complex, particularly when they are required to move on uneven or soft terrains, or to climb obstacles. This paper sets out to analyse the state-of-the-art of locomotion mechanisms for ground mobile robots, focussing on solutions for unstructured environments, in order to help designers to select the optimal solution for specific operating requirements. The three main categories of locomotion systems (wheeled – W, tracked – T and legged – L) and the four hybrid categories that can be derived by combining these main locomotion systems are discussed with reference to maximum speed, obstacle-crossing capability, step/stair climbing capability, slope climbing capability, walking capability on soft terrains, walking capability on uneven terrains, energy efficiency, mechanical complexity, control complexity and technology readiness. The current and future trends of mobile robotics are also outlined.

Bayesian optimisation for Intelligent Environmental Monitoring

Abstract: Environmental Monitoring (EM) is typically performed using sensor networks that collect measurements in predefined static locations. The possibility of having one or more autonomous robots to perform this task increases versatility and reduces the number of necessary sensor nodes to cover the same area. However, several problems arise when making use of autonomous moving robots for EM. The main challenges are how to build an accurate spatial-temporal model while choosing locations for measuring the phenomenon. This paper addresses the problem by using Bayesian Optimisation for choosing sensing locations, and presents a new utility function that takes into account the distance travelled by a moving robot. The proposed methodology is tested in simulation and in a real environment. Compared to existing strategies, our approach exhibits slightly better accuracy in terms of RMSE error and considerably reduces the total distance travelled by the robot.

Spatial models for context-aware indoor navigation systems: A survey

Abstract: This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, consid- ering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environ- ments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation pur- poses, and by taking into account the context dimensions.

Estimation of Battery State of Charge With $H_{\infty}$ Observer: Applied to a Robot for Inspecting Power Transmission Lines

Abstract: Battery state-of-charge (SOC) estimation is essential for a mobile robot, such as inspection of power transmission lines. It is often estimated using a Kalman filter (KF) under the assumption that the statistical properties of the system and measurement errors are known. Otherwise, the SOC estimation error may be large or even divergent. In this paper, without the requirement of the known statistical properties, a SOC estimation method is proposed using an H∞ observer, which can still guarantee the SOC estimation accuracy in the worst statistical error case. Under the conditions of different currents and temperatures, the effectiveness of the proposed method is verified in the laboratory and field environments. With the comparison of the proposed method and the KF-based one, the experimental results show that the proposed method can still provide accurate SOC estimation when there exist inexact or unknown statistical properties of the errors. The proposed method has been applied successfully to the robot for inspecting the running 500-kV extra high voltage power transmission lines.

Tracking people within groups with RGB-D data

Abstract: This paper proposes a very fast and robust multi-people tracking algorithm suitable for mobile platforms equipped with a RGB-D sensor. Our approach features a novel depth-based sub-clustering method explicitly designed for detecting people within groups or near the background and a three-term joint likelihood for limiting drifts and ID switches. Moreover, an online learned appearance classifier is proposed, that robustly specializes on a track while using the other detections as negative examples. Tests have been performed with data acquired from a mobile robot in indoor environments and on a publicly available dataset acquired with three RGB-D sensors and results have been evaluated with the CLEAR MOT metrics. Our method reaches near state of the art performance and very high frame rates in our distributed ROS-based CPU implementation.

Socially-aware robot navigation: A learning approach

Abstract: The ability to act in a socially-aware way is a key skill for robots that share a space with humans. In this paper we address the problem of socially-aware navigation among people that meets objective criteria such as travel time or path length as well as subjective criteria such as social comfort. Opposed to model-based approaches typically taken in related work, we pose the problem as an unsupervised learning problem. We learn a set of dynamic motion prototypes from observations of relative motion behavior of humans found in publicly available surveillance data sets. The learned motion prototypes are then used to compute dynamic cost maps for path planning using an any-angle A* algorithm. In the evaluation we demonstrate that the learned behaviors are better in reproducing human relative motion in both criteria than a Proxemics-based baseline method.

Decentralized formation control with variable shapes for aerial robots

Abstract: We address formation control for a team of quadrotors in which the robots follow a specified group trajectory while safely changing the shape of the formation according to specifications. The formation is prescribed by shape vectors which dictate the relative separations and bearings between the robots, while the group trajectory is specified as the desired trajectory of a leader or a virtual robot in the group. Each robot plans its trajectory independently based on its local information of neighboring robots which includes both the neighbor's planned trajectory and an estimate of its state. We show that the decentralized trajectory planners (a) result in consensus on the planned trajectory for predefined shapes and (b) achieve safe reconfiguration when changing shapes.

Temporal Logic Motion Planning and Control With Probabilistic Satisfaction Guarantees

Abstract: We describe a computational framework for automatic deployment of a robot with sensor and actuator noise from a temporal logic specification over a set of properties that are satisfied by the regions of a partitioned environment. We model the motion of the robot in the environment as a Markov decision process (MDP) and translate the motion specification to a formula of probabilistic computation tree logic (PCTL). As a result, the robot control problem is mapped to that of generating an MDP control policy from a PCTL formula. We present algorithms for the synthesis of such policies for different classes of PCTL formulas. We illustrate our method with simulation and experimental results.