Task For Mobile Robots Research Articles

Mobile Robot Exploration Based On Three Dimension Cameras Acquisition

The exploration and three dimension map building of mobile robot tasks in an unknown environment are normally do not have any future reference information. The mobile robot has to estimate positions and poses itself. Finding the distinguishable objects in environment are also required in order to create the pose reference points. Therefore, the selected sensors and register mapping algorithm are significant. The sensors which can provide both of depth and image data are still deficient. Recently, the Photonic Mixture Device camera (PMD) is a three dimension sensor which generates real-time high rate volume output of surrounding scenario as well as gray scale data. However, one PMD camera drawback is low resolution output. From this problem, the idea to fusion another high resolution CCD camera to PMD depth data is purposed. In this study hence presents the mobile robot exploration using integration of CCD and PMD camera in order to create the three dimension mapping. The Iterative Closest Point (ICP) algorithm is used for matching clouding points and minimizing the interval pose of the two scans. Eventually, the difference three dimension mapping output between fusion CCD camera and use only pure depth data are presented and evaluated.

Efficient FPGA Realization of CORDIC With Application to Robotic Exploration

We present an area efficient method and field programmable gate array (FPGA) realization for two common operations in robotics, namely, the following: (1) rotating a vector in 2D and (2) aligning a vector in the plane with a specific axis. It is based on a new coordinate rotation digital computer (CORDIC) algorithm that is designed to work with a small set of elementary angles. Unlike conventional CORDIC, the proposed algorithm does not require a ROM and a full-fledged barrel shifter. The proposed CORDIC algorithm is used to design hardware efficient solutions for two mobile robotic tasks in an indoor environment without employing division and floating-point calculations. Experiments with a sole low end FPGA based robot in static as well as dynamic environments validate the power of the approach.

An application of Lyapunov stability analysis to improve the performance of NARMAX models

Previously we presented a novel approach to program a robot controller based on system identification and robot training techniques. The proposed method works in two stages: first, the programmer demonstrates the desired behaviour to the robot by driving it manually in the target environment. During this run, the sensory perception and the desired velocity commands of the robot are logged. Having thus obtained training data we model the relationship between sensory readings and the motor commands of the robot using ARMAX/NARMAX models and system identification techniques. These produce linear or non-linear polynomials which can be formally analysed, as well as used in place of “traditional robot” control code. In this paper we focus our attention on how the mathematical analysis of NARMAX models can be used to understand the robot’s control actions, to formulate hypotheses and to improve the robot’s behaviour. One main objective behind this approach is to avoid trial-and-error refinement of robot code. Instead, we seek to obtain a reliable design process, where program design decisions are based on the mathematical analysis of the model describing how the robot interacts with its environment to achieve the desired behaviour. We demonstrate this procedure through the analysis of a particular task in mobile robotics: door traversal.

Three‐dimensional mapping with time‐of‐flight cameras

AbstractThis article investigates the use of time‐of‐flight (ToF) cameras in mapping tasks for autonomous mobile robots, in particular in simultaneous localization and mapping (SLAM) tasks. Although ToF cameras are in principle an attractive type of sensor for three‐dimensional (3D) mapping owing to their high rate of frames of 3D data, two features make them difficult as mapping sensors, namely, their restricted field of view and influences on the quality of range measurements by high dynamics in object reflectivity; in addition, currently available models suffer from poor data quality in a number of aspects. The paper first summarizes calibration and filtering approaches for improving the accuracy, precision, and robustness of ToF cameras independent of their intended usage. Then, several ego motion estimation approaches are applied or adapted, respectively, in order to provide a performance benchmark for registering ToF camera data. As a part of this, an extension to the iterative closest point algorithm has been developed that increases the robustness under restricted field of view and under larger displacements. Using an indoor environment, the paper provides results from SLAM experiments using these approaches in comparison. It turns out that the application of ToF cameras is feasible to SLAM tasks, although this type of sensor has a complex error characteristic. © 2009 Wiley Periodicals, Inc.

Nonlinear Constraint Network Optimization for Efficient Map Learning

Learning models of the environment is one of the fundamental tasks of mobile robots since maps are needed for a wide range of robotic applications, such as navigation and transportation tasks, service robotic applications, and several others. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we present a highly efficient maximum-likelihood approach that is able to solve 3-D and 2-D problems. Our approach addresses the so-called graph-based formulation of simultaneous localization and mapping (SLAM) and can be seen as an extension of Olson's algorithm toward non-flat environments. It applies a novel parameterization of the nodes of the graph that significantly improves the performance of the algorithm and can cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory. Furthermore, our approach is able to appropriately distribute the roll, pitch, and yaw error over a sequence of poses in 3-D mapping problems. We implemented our technique and compared it with multiple other graph-based SLAM solutions. As we demonstrate in simulated and real-world experiments, our method converges faster than the other approaches and yields accurate maps of the environment.

Performance evaluation of pure-motion tasks for mobile robots with respect to world models

The evaluation of the performance of robot motion methods and systems is still an open challenge, although substantial progress has been made in the field over the years. On the one hand, these techniques cannot be evaluated off-line, on the other hand, they are deeply influenced by the task, the environment and the specific representation chosen for it. In this paper we concentrate on "pure-motion tasks": tasks that require to move the robot from one configuration to another, either being an independent sub-task of a more complex plan or representing a goal by itself. After characterizing the goals and the tasks, we describe the commonly-used problem decomposition and different kinds of modeling that can be used, from accurate metric maps to minimalistic representations. The contribution of this paper is an evaluation framework that we adopt in a set of experiments showing how the performance of the motion system can be affected by the use of different kinds of environment representations.

Constrained Motion Model of Mobile Robots and Its Applications

Target detecting and dynamic coverage are fundamental tasks in mobile robotics and represent two important features of mobile robots: mobility and perceptivity. This paper establishes the constrained motion model and sensor model of a mobile robot to represent these two features and defines the k -step reachable region to describe the states that the robot may reach. We show that the calculation of the k-step reachable region can be reduced from that of 2(k) reachable regions with the fixed motion styles to k + 1 such regions and provide an algorithm for its calculation. Based on the constrained motion model and the k -step reachable region, the problems associated with target detecting and dynamic coverage are formulated and solved. For target detecting, the k-step detectable region is used to describe the area that the robot may detect, and an algorithm for detecting a target and planning the optimal path is proposed. For dynamic coverage, the k-step detected region is used to represent the area that the robot has detected during its motion, and the dynamic-coverage strategy and algorithm are proposed. Simulation results demonstrate the efficiency of the coverage algorithm in both convex and concave environments.

2P2-C13 全方位画像列を用いた3次元環境モデル生成(移動ロボットの自己位置推定と地図構築)

A 3D environment model is important for tasks of autonomous mobile robots. In this paper, we propose a method for 3D environment modeling based on structure from motion using an omni-directional camera installed on a mobile robot. The method extracts and tracks feature points in an omni-directional image sequence to get corresponding points in image pairs taken during the movement of a robot. The relative relations of camera positions and orientations are estimated by the positions of the corresponding points. With these relations and image coordinates of the feature points, 3D coordinates of these points are calculated by triangulation. The individual measurements are integrated by scale matching. By using 3D Delaunay triangulation, triangular meshes is constructed. The method makes the triangular meshes compatible with the physical shape from texture information of images. By texture-mapping, a 3D environment model is generated. Experimental results showed the effectiveness of the proposed method.

Translating Structured English to Robot Controllers

Recently, Linear Temporal Logic (LTL) has been successfully applied to high-level task and motion planning problems for mobile robots. One of the main attributes of LTL is its close relationship with fragments of natural language. In this paper, we take the first steps toward building a natural language interface for LTL planning methods with mobile robots as the application domain. For this purpose, we built a structured English language which maps directly to a fragment of LTL.

Model-based control architecture for attentive robots in rescue scenarios

In this paper we present a control architecture for an autonomous rescue robot specialized in victim finding in an unknown and unstructured environment. The reference domain for rescue robots is the rescue-world arenas purposefully arranged for the Robocup competitions. The main task of a rescue mobile robot is to explore the environment and report to the rescue-operators the map of visited areas annotated with its finding. In this context all the attentional activities play a major role in decision processes: salient elements in the environment yield utilities and objectives. A model-based executive controller is proposed to coordinate, integrate, and monitor the distributed decisions and initiatives emerging from the modules involved in the control loop. We show how this architecture integrates the reactive model-based control of a rescue mission, with an attentive perceptual activity processing the sensor and visual stimuli. The architecture has been implemented and tested in real-world experiments by comparing the performances of metric exploration and attentive exploration. The results obtained demonstrate that the attentive behavior significantly focus the exploration time in salient areas enhancing the overall victim finding effectiveness.

Reducing a class of polygonal path tracking to straight line tracking via nonlinear strip-wise affine transformation

In this paper a piecewise linear homeomorphism is presented that maps a strictly monotone polygonal chain to a straight line. This mapping enables one to reduce the path tracking task for mobile robots to straight line tracking. Due to the simplicity of the transformation, closed form solutions for the direct and inverse mapping are presented. Furthermore, the transformation also defines a feedback equivalence relation between the original and the transformed system equations of the mobile robot. It is shown that the form of the system equations is preserved and that the transformation essentially maps a car-like robot in the original domain, to a car-like robot in the transformed domain. This enables one to use straight line trackers developed solely for this system, for the tracking of arbitrary strictly monotone polygonal curves. Finally, it is shown that the use of this mapping can also simplify the application of existing path tracking controllers since they only need to track straight line paths. In general, one can eliminate from the existing path controllers all parameters that are needed for non-straight paths, thus obtaining respective simplified controllers. For example, it is shown that a fuzzy path controller with 135 rules can be reduced to an equivalent fuzzy straight line tracking controller with 45 rules.

Foraging Task of Multiple Mobile Robots in a Dynamic Environment Using Adaptive Behavior in Crickets

In this paper, we propose an algorithm for multiple mobile robots performing a foraging task. In the proposed algorithm, robots select behaviors based on their activities, which were adjusted by interaction with other robots and foods. The proposed algorithm was inspired by the mechanism governing the fighting behavior in male crickets. Simulation results showed that the algorithm is efficient in a dynamic environment.

A Potential Field Model Using Generalized Sigmoid Functions

The lack of a potential field model capable of providing accurate representations of objects of arbitrary shapes is considered one major limitation in applying the artificial potential field method in many practical applications. In this correspondence, we propose a potential function based on generalized sigmoid functions. The generalized sigmoid model can be constructed from combinations of implicit primitives or from sampled surface data. The constructed potential field model can achieve an accurate analytic description of objects in two or three dimensions and requires very modest computation at run time. In this correspondence, applications of the generalized sigmoid model in path-planning tasks for mobile robots and in haptic feedback tasks are presented. The validation results in this correspondence show that the model can effectively allow the user or mobile robot to avoid penetrations of obstacles while successfully accomplishing the task.

Resource management for real-time tasks in mobile robotics

Coordinated behavior of mobile robots is an important emerging application area. Different coordinated behaviors can be achieved by assigning sets of control tasks, or strategies, to robots in a team. These control tasks must be scheduled either locally on the robot or distributed across the team. An application may have many control strategies to dynamically choose from, although some may not be feasible, given limited resource and time availability. Thus, dynamic feasibility checking becomes important as the coordination between robots and the tasks that need to be performed evolves with time. This paper presents an on-line algorithm for finding a feasible strategy given a functionally equivalent set of strategies for achieving an application’s goals. We present two algorithms for feasibility improvement. Both consider communication cost and utilization bound to make resource allocation and scheduling decisions. Extensive experimental results show the effectiveness of the approaches, especially in resource-tight environments. We also demonstrate the application of our approach to real world scenarios involving teams of robots and show how feasibility analysis also allows the prediction of the scalability of the solution to large robot teams.

초음파의 반사 신호를 이용한 실내환경의 재질 인식

Material information for environment may be useful to accomplish mobile robot localization. A procedure to classify a set of indoor materials (glass, steel, wood, aluminum and concrete) with the reflected signal of ultrasonic sensor is proposed in this paper. The main idea is to use material-specific reflection characteristics for the recognition of material type. To achieve the classification task, we modeled reflected signal as a maximum amplitude with respect to distance. In this way, we can generate echo signal models for the given materials and these models are used to compare with the current sensor reading. The experimental results show that the proposed method may give material information during map building task of mobile robot.

An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks

Self-organizing feature maps such as extended Kohonen maps (EKMs) have been very successful at learning sensorimotor control for mobile robot tasks. This letter presents a new ensemble approach, cooperative EKMs with indirect mapping, to achieve complex robot motion. An indirect-mapping EKM self-organizes to map from the sensory input space to the motor control space indirectly via a control parameter space. Quantitative evaluation reveals that indirect mapping can provide finer, smoother, and more efficient motion control than does direct mapping by operating in a continuous, rather than discrete, motor control space. It is also shown to outperform basis function neural networks. Furthermore, training its control parameters with recursive least squares enables faster convergence and better performance compared to gradient descent. The cooperation and competition of multiple self-organized EKMs allow a nonholonomic mobile robot to negotiate unforeseen, concave, closely spaced, and dynamic obstacles. Qualitative and quantitative comparisons with neural network ensembles employing weighted sum reveal that our method can achieve more sophisticated motion tasks even though the weighted-sum ensemble approach also operates in continuous motor control space.

Fixed-priority scheduling of variable rate tasks for an autonomous mobile robot

We present an autonomous, mobile, robotics application that requires dynamic adjustments of task execution rates to meet the demands of an unpredictable environment. The Robotic Safety Marker (RSM) system consists of one lead robot, the foreman, and a group of guided robots, called robotic safety markers (a.k.a., barrels). The application requires adjusting task periods on the foreman to achieve desired performance metrics with respect to the speed at which a system activity is completed, the accuracy of RSM placement, or the number of RSMs controlled by the foreman. A static priority scheduling solution is proposed that takes into consideration the strict deadline requirements of some of the tasks and their dynamic periods. Finally, a schedulability analysis is developed that can be executed online to accommodate the dynamic performance requirements and to distinguish between safe operating points and potentially unsafe operating points.

On actively closing loops in grid-based FastSLAM

Acquiring models of the environment belongs to the fundamental tasks of mobile robots. In the past, several researchers have focused on the problem of simultaneous localization and mapping (SLAM). Classical SLAM approaches are passive in the sense that they only process the perceived sensor data and do not influence the motion of the mobile robot. In this paper, we present a novel integrated approach that combines autonomous exploration with simultaneous localization and mapping. Our method uses a grid-based version of the FastSLAM algorithm and considers at each point in time actions to actively close loops during exploration. By re-entering already visited areas, the robot reduces its localization error and in this way learns more accurate maps. Experimental results presented in this paper illustrate the advantage of our method over previous approaches that lack the ability to actively close loops.

Stability analysis and motion planning strategies with fuzzy coordinator and learning for mobile base robotic part macro/micro-assembly tasks

This paper addresses problems of a mobile base robot’s part assembly. This process can be broken down into two phases. First, a macro-assembly, bringing a part to an assembly hole or a receptacle (target) for a purpose of a part mating. For the macro-manipulation task, a stability analysis of a mobile base robot subject to disturbances, such as an external impact force and torque as well as a tipping movement, is discussed. The mobile robotic system is stabilized by balancing the system moment through a fuzzy coordinator. Simulations are performed by applying external forces and torque to the system and adding disturbances to the mobile base’s tipping movement. Second, a micro-assembly, mating a part with a target. For the micro-manipulation task, two learning methodologies are presented. First, a learning strategy to minimize the entropy, uncertainty, and eliminate unneeded events in the plan related to avoiding jamming is described. An entropy function, which is a useful measure of the variability and the information in terms of uncertainty, is introduced to measure its overall performance of a task execution related to the part mating. Next, a fuzzy stochastic learning method, based on the probability of a fuzzy set and a modified distance metric, to update the probability of a plan composed of fuzzy events used for the part mating task is introduced. The degree of uncertainty associated with the fuzzy event of plan is used as an optimality criterion, or cost function, e.g. minimum Hamming distance, for a specific task execution. The above techniques are applicable to a wide range of mobile robotic tasks including pick and place operations, maneuvering around workspace, manufacturing, part mating, or complex assembly tasks.

Improving Efficiency in Mobile Robot Task Planning Through World Abstraction

Task planning in mobile robotics should be performed efficiently, due to real-time requirements of robot-environment interaction. Its computational efficiency depends both on the number of operators (actions the robot can perform without planning) and the size of the world states (descriptions of the world before and after the application of operators). Thus, in real robotic applications, where both components can be large, planning may turn inefficient, and even unsolvable. In the artificial intelligence (AI) literature on planning, little attention has been put into efficient management of large-scale world descriptions. In real large-scale situations, conventional AI planners (in spite of the most modern improvements) may consume intractable amounts of storage and computing time, due to the huge amount of information. This paper proposes a new approach to task planning called "hierarchical task planning through world abstraction" that, by hierarchically arranging the world representation, becomes a good complement of Stanford Research Institute Problem Solver-style planners, significantly improving their computational efficiency. Broadly speaking, our approach works by first solving the task-planning problem in a highly abstracted model of the environment of the robot, and then refines the solution under more detailed models, where irrelevant world elements can be ignored, due to the results previously obtained at abstracted levels. Among the different implementations that can be made with our general strategy, we describe two that use a graph-based hierarchical world representation named the "annotated and hierarchical" graph. We show experiments, as well as results of a mobile robot operating in a large-scale environment, that demonstrate an important improvement in the efficiency of our algorithms with respect to conventional (both hierarchical and nonhierarchical) planning and their nice integration with existing planners.