Integrating planning and control for single-bodied wheeled mobile robots

This paper presents an approach to couple path planning and control for mobile robot navigation in a hybrid control framework. We build upon an existing hybrid control approach called sequential composition, in which a set of feedback control policies are prescribed on well-defined domains contained in the robot’s free space. Each control policy drives the robot to a goal set, which lies in the domain of a subsequent policy. Control policies are deployed into the free state space so that when composed among one another, the overall action of the set of control policies drives the robot to perform a task, such as moving from a start to a goal location or patrolling a perimeter. A planner determines the sequence of control policies to be invoked. When control policies defined in this framework respect the low-level dynamics and kinematics of the system, this formal approach guarantees that high-level tasks are either accomplished by a given set of policies, or verifies that the tasks are not achievable with the given policies.     

Fault detection in autonomous robots based on fault injection and learning

In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot’s sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.

Planetary rovers�� wheel�Csoil interaction mechanics: new challenges and applications for wheeled mobile robots

With the increasing challenges facing planetary exploration missions and the resultant increase in the performance requirements for planetary rovers, terramechanics (wheel–soil interaction mechanics) is playing an important role in the development of these rovers. As an extension of the conventional terramechanics theory for terrestrial vehicles, the terramechanics theory for planetary rovers, which is becoming a new research hotspot, is unique and puts forward many new challenging problems. This paper first discusses the significance of the study of wheel–soil interaction mechanics of planetary rovers and summarizes the differences between planetary rovers and terrestrial vehicles and the problems arising thereof. The application of terramechanics to the development of planetary rovers can be divided into two phases (the R&D phase and exploration phase for rovers) corresponding to the high-fidelity and simplified terramechanics models. This paper also describes the current research status by providing an introduction to classical terramechanics and the experimental, theoretical, and numerical researches on terramechanics for planetary rovers. The application status of the terramechanics for planetary rovers is analyzed from the aspects of rover design, performance evaluation, planetary soil parameter identification, dynamics simulation, mobility control, and path planning. Finally, the key issues for future research are discussed. The current planetary rovers are actually advanced wheeled mobile robots (WMRs), developed employing cutting-edge technologies from different fields. The terramechanics for planetary rovers is expected to present new challenges and applications for WMRs, making it possible to develop WMRs using the concepts of mechanics and dynamics.     

Self-assembly strategies in a group of autonomous mobile robots

Robots are said to be capable of self-assembly when they can autonomously form physical connections with each other. By examining different ways in which a system can use self-assembly (i.e., different strategies), we demonstrate and quantify the performance costs and benefits of (i) acting as a physically larger self-assembled entity, (ii) letting the system choose when and if to self-assemble, (iii) coordinating the sensing and actuation of the connected robots so that they respond to the environment as a single collective entity. Our analysis is primarily based on real world experiments in a hill crossing task. The configuration of the hill is not known by the robots in advance—the hill can be present or absent, and can vary in steepness and orientation. In some configurations, the robots can overcome the hill more quickly by navigating individually, while other configurations require the robots to self-assemble to overcome the hill. We demonstrate the applicability of our self-assembly strategies to two other tasks—hole crossing and robot rescue—for which we present further proof-of-concept experiments with real robots.     

Asymmetric trajectory generation and impedance control for running of biped robots

An online asymmetric trajectory generation method for biped robots is proposed to maintain dynamical postural stability and increase energy autonomy, based on the running stability criterion defined in phases. In a support phase, an asymmetric trajectories for the hip and swing leg of the biped robots is obtained from an approximated running model with two springless legs and a spring-loaded inverted pendulum model so that the zero moment point should exist inside the safety boundary of a supporting foot, and the supporting leg should absorb large reaction forces, take off and fly through the air. The biped robot is under-actuated with six degrees of under-actuation during flight. The trajectory generation strategies for the hip and both legs in a flight phase use the approximated running model and non-holonomic constraints based on the linear and angular momenta at the mass center. Next, we present an impedance control with a force modulation strategy to guarantee a stable landing on the ground and simultaneously track the desired trajectories where the desired impedance at the hip link and both legs is specified. A series of computer simulations for two different types of biped robots show that the proposed running trajectory and impedance control method satisfy the two conditions for running stability and make the biped robot more robust to variations in the desired running speed, gait transitions between walking and running, and parametric modeling errors. We have examined the feasibility of this method with running experiments on a 12-DOF biped robot without arms. The biped robot could run successfully with average forward speed of about 0.3359 [m/s]. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.

Uncertainty and imprecision modeling for the mobile robot localization problem

This article deals with uncertainty and imprecision treatment during the mobile robot localization process. The imprecision determination is based on the use of the interval formalism. Indeed, the mobile robot is equipped with an exteroceptive sensor and odometers. The imprecise data given by these two sensors are fused by constraint propagation on intervals. At the end of the algorithm, we get 3D localization subpaving which is supposed to contain the robot’s position in a guaranteed way. Concerning the uncertainty, it is managed through a propagation architecture based on the use of the Transferable Belief Model of Smets. This architecture enables to propagate uncertainty from low level data (sensor data) in order to quantify the global uncertainty of the robot localization estimation.

Design and control of a heavy material handling manipulator for agricultural robots

In this paper, we propose a manipulation system for agricultural robots that handle heavy materials. The structural systems of a mobile platform and a manipulator are selected and designed after proposing new knowledge about agricultural robots. Also, the control systems for these structural systems are designed in the presence of parametric perturbation and uncertainty while avoiding conservative results. The validity of both the structural and control systems is confirmed by conducting watermelon harvesting experiments in an open field. Furthermore, an explicit design procedure is confirmed for both the structural and control systems and three key design tools are clarified.

A comparative study on automatic audio–visual fusion for aggression detection using meta-information

Multimodal fusion is a complex topic. For surveillance applications audio–visual fusion is very promising given the complementary nature of the two streams. However, drawing the correct conclusion from multi-sensor data is not straightforward. In previous work we have analysed a database with audio–visual recordings of unwanted behavior in trains (Lefter et al., 2012) and focused on a limited subset of the recorded data. We have collected multi- and unimodal assessments by humans, who have given aggression scores on a 3 point scale. We showed that there are no trivial fusion algorithms to predict the multimodal labels from the unimodal labels since part of the information is lost when using the unimodal streams. We proposed an intermediate step to discover the structure in the fusion process. This step is based upon meta-features and we find a set of five which have an impact on the fusion process. In this paper we extend the findings in (Lefter et al., 2012) for the general case using the entire database. We prove that the meta-features have a positive effect on the fusion process in terms of labels. We then compare three fusion methods that encapsulate the meta-features. They are based on automatic prediction of the intermediate level variables and multimodal aggression from state of the art low level acoustic, linguistic and visual features. The first fusion method is based on applying multiple classifiers to predict intermediate level features from the low level features, and to predict the multimodal label from the intermediate variables. The other two approaches are based on probabilistic graphical models, one using (Dynamic) Bayesian Networks and the other one using Conditional Random Fields. We learn that each approach has its strengths and weaknesses in predicting specific aggression classes and using the meta-features yields significant improvements in all cases.     

Frequency response method for terrain classification in autonomous ground vehicles

Many autonomous ground vehicle (AGV) missions, such as those related to agricultural applications, search and rescue, or reconnaissance and surveillance, require the vehicle to operate in difficult outdoor terrains such as sand, mud, or snow. To ensure the safety and performance of AGVs on these terrains, a terrain-dependent driving and control system can be implemented. A key first step in implementing this system is autonomous terrain classification. It has recently been shown that the magnitude of the spatial frequency response of the terrain is an effective terrain signature. Furthermore, since the spatial frequency response is mapped by an AGV’s vibration transfer function to the frequency response of the vibration measurements, the magnitude of the latter frequency responses also serve as a terrain signature. Hence, this paper focuses on terrain classification using vibration measurements. Classification is performed using a probabilistic neural network, which can be implemented online at relatively high computational speeds. The algorithm is applied experimentally to both an ATRV-Jr and an eXperimental Unmanned Vehicle (XUV) at multiple speeds. The experimental results show the efficacy of the proposed approach.

A recursive, numerically stable, and efficient simulation algorithm for serial robots with flexible links

A methodology for the formulation of dynamic equations of motion of a serial flexible-link manipulator using the decoupled natural orthogonal complement (DeNOC) matrices, introduced elsewhere for rigid bodies, is presented in this paper. First, the Euler Lagrange (EL) equations of motion of the system are written. Then using the equivalence of EL and Newton–Euler (NE) equations, and the DeNOC matrices associated with the velocity constraints of the connecting bodies, the analytical and recursive expressions for the matrices and vectors appearing in the independent dynamic equations of motion are obtained. The analytical expressions allow one to obtain a recursive forward dynamics algorithm not only for rigid body manipulators, as reported earlier, but also for the flexible body manipulators. The proposed simulation algorithm for the flexible link robots is shown to be computationally more efficient and numerically more stable than other algorithms present in the literature. Simulations, using the proposed algorithm, for a two link arm with each link flexible and a Space Shuttle Remote Manipulator System (SSRMS) are presented. Numerical stability aspects of the algorithms are investigated using various criteria, namely, the zero eigenvalue phenomenon, energy drift method, etc. Numerical example of a SSRMS is taken up to show the efficiency and stability of the proposed algorithm. Physical interpretations of many terms associated with dynamic equations of flexible links, namely, the mass matrix of a composite flexible body, inertia wrench of a flexible link, etc. are also presented. The work has been carried out in the Dept. of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India.     

Weighted and constrained possibilistic C-means clustering for online fault detection and isolation

In this paper, a new weighted and constrained possibilistic C-means clustering algorithm is proposed for process fault detection and diagnosis (FDI) in offline and online modes for both already known and novel faults. A possibilistic clustering based approach is utilized here to address some of the deficiencies of the fuzzy C-means (FCM) algorithm leading to more consistent results in the context of the FDI tasks by relaxing the probabilistic condition in FCM cost function. The proposed algorithm clusters the historical data set into C different dense regions without having precise knowledge about the number of the faults in the data set. The algorithm incorporates simultaneously possibilistic algorithm and local attribute weighting for time-series segmentation. This allows different weights to be allocated to different features responsible for the distinguished process faults which is an essential characteristic of proper FDI operations. A set of comparative studies have been carried out on the large-scale Tennessee Eastman industrial challenge problem and the DAMADICS actuator benchmark to demonstrate the superiority of the proposed algorithm in process FDI applications with respect to some available alternative approaches.     

Generating gaits for snake robots: annealed chain fitting and keyframe wave extraction

Snake robots have many degrees of freedom, which makes them both extremely versatile and complex to control. They are often controlled with gaits, coordinated cyclic patterns of joint motion. Using gaits simplifies the design of high-level controllers, but shifts the complexity burden to designing the gaits. In this paper, we address the gait design problem by introducing two algorithms: Annealed chain fitting and Keyframe wave extraction. Annealed chain fitting efficiently maps a continuous backbone curve describing the three-dimensional shape of the robot to a set of joint angles for a snake robot. Keyframe wave extraction takes joint angles fit to a sequence of backbone curves and identifies parameterized periodic functions that produce those sequences. Together, they allow a gait designer to conceive a motion in terms three-dimensional shapes and translate them into easily manipulated wave functions, and so unify two previously disparate gait design approaches. We validate the algorithms by using them to produce rolling and sidewinding gaits for crawling and climbing, with results that match previous empirical investigations.     

Local and Nonlocal Discrete Regularization on Weighted Graphs for Image and Mesh Processing

We propose a discrete regularization framework on weighted graphs of arbitrary topology, which unifies local and nonlocal processing of images, meshes, and more generally discrete data. The approach considers the problem as a variational one, which consists in minimizing a weighted sum of two energy terms: a regularization one that uses the discrete p-Dirichlet form, and an approximation one. The proposed model is parametrized by the degree p of regularity, by the graph structure and by the weight function. The minimization solution leads to a family of simple linear and nonlinear processing methods. In particular, this family includes the exact expression or the discrete version of several neighborhood filters, such as the bilateral and the nonlocal means filter. In the context of images, local and nonlocal regularizations, based on the total variation models, are the continuous analog of the proposed model. Indirectly and naturally, it provides a discrete extension of these regularization methods for any discrete data or functions.     

A team of mobile robots and monitoring sensors—from concept to experiment

This paper describes the underlying concepts, architecture and implementation of a robotic system consisting of heterogenous mobile robots and stationary sensors, cooperating in a task of collective perception and world modeling. The navigation capability of a group of robots can be improved by sharing available information about the state of the environment (the environment model) and information about the relative position estimates. The information sharing can be especially beneficial to the robots when there are also some stationary monitoring sensors (e.g. cameras) available in the environment, which can serve as external navigation aids. In the article, information processing performed by individual members of the team—robots and sensors—is analyzed and a unifying multi-agent blackboard architecture is described. For information sharing between robots and monitoring sensors, a framework based on the idea of the Contract Net Protocol is proposed. The communication backbone provides agents with unified communication interfaces. The experimental set-up is described. The results of tests validating the correctness of the design on the tasks of cooperative localization and world-model building are reported. A discussion and comparison to other multi-robot systems closes the article.     

Using the Dempster–Shafer method for the fusion of LIDAR data and multi-spectral images for building detection

A method for the detection of buildings in densely built-up urban areas by the fusion of first and last pulse laser scanner data and multi-spectral images is presented. The method attempts to achieve a classification of land cover into the classes “building”, “tree”, “grassland”, and “bare soil”, the latter three being considered relevant for the subsequent generation of a high-quality digital terrain model (DTM). Building detection is accomplished by first applying a hierarchical rule-based technique for coarse DTM generation based on morphological filtering. After that, data fusion based on the theory of Dempster–Shafer is used at two different stages of the classification process. We describe the algorithms involved, giving examples for a test site in Fairfield (New South Wales).     

Pervaho: A specialized middleware for mobile context-aware applications

The concept of context-awareness offers a great potential for the future of mobile applications. In order to be developed in an optimal way, mobile context-aware applications need appropriate middleware services. This paper introduces Pervaho, an integrated middleware aimed specifically at supporting the development and testing of mobile context-aware applications. To illustrate the use of Pervaho, we walk through the development of a concrete mobile application and show how it can be built on top of Pervaho’s location-based publish/subscribe service. We also illustrate how a specialized mobility testing tool significantly simplifies the process of testing proximity-based semantics. We then present the implementation of Pervaho, which is based on a set of communication protocols geared at mesh networks. Finally, we provide a performance analysis of our implementation.     

A new model for the integrated vehicle-crew-rostering problem and a computational study on rosters

Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicle-scheduling, crew-scheduling and rostering problems. In this paper, a new integer mathematical formulation to describe the integrated vehicle-crew-rostering problem is presented. The method proposed to obtain feasible solutions for this binary non-linear multi-objective optimization problem is a sequential algorithm considered within a preemptive goal programming framework that gives a higher priority to the integrated vehicle-crew-scheduling goal and a lower priority to the driver rostering goals. A heuristic approach is developed where the decision maker can choose from different vehicle-crew schedules and rosters, while respecting as much as possible management’s interests and drivers’ preferences. An application to real data of a Portuguese bus company shows the influence of vehicle-crew-scheduling optimization on rostering solutions.     

Structure-based color learning on a mobile robot under changing illumination

A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. To operate in the real world, autonomous robots rely on sensory information. Despite the potential richness of visual information from on-board cameras, many mobile robots continue to rely on non-visual sensors such as tactile sensors, sonar, and laser. This preference for relatively low-fidelity sensors can be attributed to, among other things, the characteristic requirement of real-time operation under limited computational resources. Illumination changes pose another big challenge. For true extended autonomy, an agent must be able to recognize for itself when to abandon its current model in favor of learning a new one; and how to learn in its current situation. We describe a self-contained vision system that works on-board a vision-based autonomous robot under varying illumination conditions. First, we present a baseline system capable of color segmentation and object recognition within the computational and memory constraints of the robot. This relies on manually labeled data and operates under constant and reasonably uniform illumination conditions. We then relax these limitations by introducing algorithms for (i) Autonomous planned color learning, where the robot uses the knowledge of its environment (position, size and shape of objects) to automatically generate a suitable motion sequence and learn the desired colors, and (ii) Illumination change detection and adaptation, where the robot recognizes for itself when the illumination conditions have changed sufficiently to warrant revising its knowledge of colors. Our algorithms are fully implemented and tested on the Sony ERS-7 Aibo robots.

Tradeoffs between energy consumption and QoS in mobile grid

Mobile grid, which combines grid and mobile computing, supports mobile users and resources in a seamless and transparent way. However, mobility, QoS support, energy management, and service provisioning pose challenges to mobile grid. The paper presents a tradeoff policy between energy consumption and QoS in the mobile grid environment. Utility function is used to specify each QoS dimension; we formulate the problem of energy and QoS tradeoff by utility optimization. The work is different from the classical energy aware scheduling, which usually takes the consumed energy as the constraints; our utility model regards consumed energy as one of the components of measure of the utility values, which indicates the tradeoff of application satisfaction and consumed energy. It is a more accurate utility model for abstracting the energy characteristics and QoS requirement for mobile users and resources in mobile grid. The paper also proposes a distributed energy–QoS tradeoff algorithm. The performance evaluation of our energy–QoS tradeoff algorithm is evaluated and compared with other energy and deadline constrained scheduling algorithm.     

Lattice Boltzmann Model Based on the Rebuilding-Divergency Method for the Laplace Equation and the Poisson Equation

In this paper, a new lattice Boltzmann model based on the rebuilding-divergency method for the Poisson equation is proposed. In order to translate the Poisson equation into a conservation law equation, the source term and diffusion term are changed into divergence forms. By using the Chapman-Enskog expansion and the multi-scale time expansion, a series of partial differential equations in different time scales and several higher-order moments of equilibrium distribution functions are obtained. Thus, by rebuilding the divergence of the source and diffusion terms, the Laplace equation and the Poisson equation with the second accuracy of the truncation errors are recovered. In the numerical examples, we compare the numerical results of this scheme with those obtained by other classical method for the Green-Taylor vortex flow, numerical results agree well with the classical ones.