ViComp: composition of user-generated videos

We propose ViComp, an automatic audio-visual camera selection framework for composing uninterrupted recordings from multiple user-generated videos (UGVs) of the same event. We design an automatic audio-based cut-point selection method to segment the UGV. ViComp combines segments of UGVs using a rank-based camera selection strategy by considering audio-visual quality and camera selection history. We analyze the audio to maintain audio continuity. To filter video segments which contain visual degradations, we perform spatial and spatio-temporal quality assessment. We validate the proposed framework with subjective tests and compare it with state-of-the-art methods.     

Mission Energy Prediction for Unmanned Ground Vehicles Using Real‐time Measurements and Prior Knowledge

A typical unmanned ground vehicle (UGV) mission can be composed of various tasks and several alternative paths. Small UGVs are typically teleoperated and rely on electric rechargeable batteries for their operations. Since each battery has limited energy storage capacity, it is essential to predict the expected mission energy requirement during the mission execution and update this prediction adaptively via real‐time performance measurements (e.g., vehicle power consumption and velocity). We propose and compare two methods in this paper. One is based on recursive least‐squares estimation built upon a UGV longitudinal dynamics model. The other is based on Bayesian estimation when prior knowledge (e.g., road average grade and operator driving style) is available. The proposed Bayesian prediction can effectively combine prior knowledge with real‐time performance measurements for adaptively updating the prediction of the mission energy requirement. Our experimental and simulation studies show that the Bayesian approach can yield more accurate predictions even with moderately imprecise prior knowledge. © 2013 Wiley Periodicals, Inc.     

Multiple UAVs/UGVs heterogeneous coordinated technique based on Receding Horizon Control (RHC) and velocity vector control

Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematical model,the characteristics of heterogeneous flocking is analyzed in detail.Two key issues are considered in multi-UGV subgroups,which are Reynolds Rule and Virtual Leader(VL).Receding Horizon Control(RHC) with Particle Swarm Optimization(PSO) is proposed for multiple UGVs flocking,and velocit...     

Negotiating Uneven Terrain with a Compliant Designed Unmanned Ground Vehicle Equipped with Locomotive Master‐Slave Operation

In recent years, a number operational unmanned ground vehicles (UGVs) have been developed that can negotiate irregular terrain. They have a number of degrees‐of‐freedom (DOF) giving them enhanced mobility, e.g., the ability to climb stairs and over obstacles. However, operating them remotely is complicated because their controllers are similar to conventional control pads or joysticks used in computer games or toys. It is hard for the operator to achieve an intuitive and natural feel, thus mistakes are common. To intuitively control the locomotion of a UGV with many DOFs, a master‐slave operation was implemented. A novel UGV called Kurogane, which consists of a typical crawler combined with a human‐like torso section, was developed. The torso section is controlled via a wearable controller interface. In addition, the UGV is equipped with models of muscle viscoelasticity and stretch reflex, called the involuntary autonomous adaptation system, inspired by the adaptive compliance of animals. The proposed system can autonomously and flexibly react and adapt to irregular terrain in real time. Therefore, the operation of Kurogane is simple and does not require great skill or precision. Experimental results show that it performs well over a fixed step, stairs, and rough outdoor terrain. © 2013 Wiley Periodicals, Inc.     

Unmanned Ground Vehicle Navigation in Coordinate-Free and Localization-Free Wireless Sensor and Actuator Networks

We present a set of algorithms for the navigation of Unmanned Ground Vehicles (UGVs) towards a set of pre-identified target nodes in coordinate-free and localization-free wireless sensor and actuator networks. The UGVs are equipped with a set of wireless listeners that provide sensing information about the potential field generated by the network of actuators. Two main navigation scenarios are considered: single-UGV, single-destination navigation and multi-UGV, multi-destination navigation. For the single-UGV, single-destination case, we present both centralized and distributed navigation algorithms. Both algorithms share a similar two-phase concept. In the first phase, the system assigns level numbers to individual nodes based on their hop distance from the target nodes. In the second phase, the UGV uses the potential field created by the network of actuators to move towards the target nodes, requiring cooperation between triplets of actuator nodes and the UGV. The hop distance to the target nodes is used to control the main moving direction while the potential field, which can be measured by listeners on the UGV, is used to determine the UGV’s movement. For the multi-UGV, multi-destination case, we present a decentralized allocation algorithm such that multiple UGVs avoid converging to the same destination. After each UGV determines its destination, the proposed navigation scheme is applied. The presented algorithms do not attempt to localize UGVs or sensor nodes and are therefore suitable for operating in GPS-free/denied environments. We also present a study of the communication complexity of the algorithms as well as simulation examples that verify the proposed algorithms and compare their performances.     

Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle

We present in this paper a Fuzzy Logic Controller (FLC) combined with a predictive algorithm to track an Unmanned Ground Vehicle (UGV), using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with a down facing camera. The video flow is sent continuously to a ground station to be processed in order to extract the location of the UGV and send the commands back to the UAV to follow autonomously the UGV. To emulate an experienced UAVs pilot, we propose a fuzzy-logic set of rules. Double Exponential Smoothing algorithm is used to filter the measurements and give the predictive value of the errors in the image plan. The FLC inputs are the filtered errors (UGV position) in the image plan and the derivative of its predicted value. The outputs are pitch and roll commands to be sent to the UAV. We show the efficiency of the proposed controller experimentally, and discuss the improvement of the tracking results compared to our previous work.     

Intelligent unmanned ground vehicle navigation via information evaluation

Sensor-centric navigation of unmanned ground vehicles (UGVs) operating in rugged and expansive terrains requires the competency to evaluate the utility of sensor information such that it results in intelligent behavior of the vehicles. Highly imperfect, inconsistent information and incomplete a priori knowledge introduce uncertainty in such unmanned navigation systems. Understanding and quantifying uncertainty yields a measure of useful information that plays a critical role in several robotic navigation tasks such as sensor fusion, mapping, localization, path planning and control. In this article, within a probabilistic framework, the utility of estimation and information-theoretic concepts towards quantifying uncertainty using entropy and mutual information metrics in various contexts of UGV navigation via experimental results is demonstrated.     

Selective Combination of Visual and Thermal Imaging for Resilient Localization in Adverse Conditions: Day and Night,Smoke and Fire

Long‐term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low‐visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual‐SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low‐quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low‐light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera‐based localization resilient to a large range of low‐visibility conditions.     

Decentralized planning and control for UAV–UGV cooperative teams

In this paper we study a symbiotic aerial vehicle-ground vehicle robotic team where unmanned aerial vehicles (UAVs) are used for aerial manipulation tasks, while unmanned ground vehicles (UGVs) aid and assist them. UGV can provide a UAV with a safe landing area and transport it across large distances, while UAV can provide an additional degree of freedom for the UGV, enabling it to negotiate obstacles. We propose an overall system control framework that includes high-accuracy motion planning for each individual robot and ad-hoc decentralized mission planning for complex missions. Experimental results obtained in a mockup arena for parcel transportation scenario show that the system is able to plan and execute missions in various environments and that the obtained plans result in lower energy consumption.     

On the controllability of the parameterized state matrix in “a continuation approach to eigenvalue assignment” by Harris,DeCarlo and Richter

This paper will investigate the controllability properties for systems parameterized as in Harns et al. (1983, Automatica, 19, 551–555). It will be shown that for systems of low dimensions this parameterization must be done carefully to guarantee that the system is controllable over the parameterization. The controllability property is key to the algorithms developed in Lefebvre et al. (1985, Int. J. Control, 41, 1273–1292), Richter and DeCarlo (1984, IEEE Trans. Aut. Control, AC-29), Harris et al. (1983, Automatica, 19, 551–555), Richter et al. (1981, Proc. 1981 Joint Aut. Control Conf.), Harris and DeCarlo (1982, Purdue School of EE, TR-EE 82-11) and Sebok and DeCarlo (1984, Proc. Allerton Conf. Communication, Control and Computing).     

Online turnover‐free control for a mobile agent with a terrain prediction sensor

This paper proposes a turnover‐free control method for a teleoperated mobile agent (or vehicle) moving through uneven terrain. The teleoperated agent is primarily driven by an operator at a remote site and is able to react autonomously when a possible turnover is predicted. In order to predict the turnover, a low‐cost terrain prediction sensor has been developed using a camera vision with a structured laser light. Since it is difficult for an operator to predict the reactive motion of the agent, a force reflection technique with a force feedback joystick is employed to intuitively recognize the inconsistency between the intended motion and the reactive motion of the agent. Finally, to verify the feasibility and effectiveness of the proposed method, experiments with the ROBHAZ‐DT (actual mobile agent) have been carried out. In the experiments, the operator could recognize the reactive motion of the agent for turnover prevention through force reflection while the agent was moving on slopped terrain. © 2006 Wiley Periodicals, Inc.     

An information theoretic approach to camera control for crowded scenes

Navigation and monitoring of large and crowded virtual environments is a challenging task and requires intuitive camera control techniques to assist users. In this paper, we present a novel automatic camera control technique providing a scene analysis framework based on information theory. The developed framework contains a probabilistic model of the scene to build entropy and expectancy maps. These maps are utilized to find interest points which represent either characteristic behaviors of the crowd or novel events occurring in the scene. After an interest point is chosen, the camera is updated accordingly to display this point. We tested our model in a crowd simulation environment and it performed successfully. Our method can be integrated into existent camera control modules in computer games, crowd simulations and movie pre-visualization applications.     

Synthesis of discrete H ∞-controllers with given stability margin radius and settling time

For linear multivariable systems, we construct discrete output controllers that guarantee a given stability margin radius on the input or output of a control plant. Besides, given control time is also taken into account. We show that solving such problems reduces to a certain specially constructed standard H _∞-optimization problem. A numerical solution has been implemented in MATLAB with the Robust Control Toolbox suite based on the method of linear matrix inequalities (LMI).     

A DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs

A dynamic data driven adaptive multi-scale simulation (DDDAMS) based planning and control framework is proposed for effective and efficient surveillance and crowd control via UAVs and UGVs. The framework is mainly composed of integrated planner, integrated controller, and decision module for DDDAMS. The integrated planner, which is designed in an agent-based simulation (ABS) environment, devises best control strategies for each function of (1) crowd detection (vision algorithm), (2) crowd tracking (filtering), and (3) UAV/UGV motion planning (graph search algorithm). The integrated controller then controls real UAVs/UGVs for surveillance tasks via (1) sensory data collection and processing, (2) control command generation based on strategies provided by the decision planner for crowd detection, tracking, and motion planning, and (3) control command transmission via radio to the real system. The decision module for DDDAMS enhances computational efficiency of the proposed framework via dynamic switching of fidelity of simulation and information gathering based on the proposed fidelity selection and assignment algorithms. In the experiment, the proposed framework (involving fast-running simulation as well as real-time simulation) is illustrated and demonstrated for a real system represented by hardware-in-the-loop (HIL) real-time simulation integrating real UAVs, simulated UGVs and crowd, and simulated environment (e.g. terrain). Finally, the preliminary results successfully demonstrate the benefit of the proposed dynamic fidelity switching concerning the crowd coverage percentage and computational resource usage (i.e. CPU usage) under cases with two different simulation fidelities.     

Multivariable self-organizing fuzzy logic control using dynamic performance index and linguistic compensators

As far as fuzzy logic based multivariable control systems are concerned, it is not always an easy task to express control strategies in the form of related multi-situations to multi-actions control rules. Decoupled control is one possible and attractive strategy to simplify this problem. However, the control performance of the decoupled controller relies greatly on ‘a prior’ knowledge of the system dynamics to build suitable compensators. This paper aims at introducing a new model-independent decoupled control architecture with the ability of on-line learning, which ensures a fast tracking performance. In this architecture, the dominating controller is developed using a new model-free Self-Organizing Fuzzy Logic Control (SOFLC) architecture whereby the Performance Index table is ‘dynamic’, of a free structure, and starting from no knowledge. Furthermore, a switching mode scheme, with a compensating action triggered by the interaction between the channels, is proposed to improve the tracking performance of the closed-loop system. A series of simulations are carried out on a two-input and two-output biomedical process, with the conclusion that the proposed control mechanism has the ability to deal with varying system dynamics and noise and is tolerant to the choice of the compensator gains effectively.     

Observer design for nonlinear discrete‐time systems: Immersion and dynamic observer error linearization techniques

This paper focuses on the observer design for nonlinear discrete‐time systems by means of nonlinear observer canonical form. At first, sufficient and necessary conditions are obtained for a class of autonomous nonlinear discrete‐time systems to be immersible into higher dimensional observer canonical form. Then a method called dynamic observer error linearization is developed. By introducing a dynamic auxiliary system, the augmented system is shown to be locally equivalent to the generalized observer form, whose nonlinear terms contain auxiliary states and output of the system. A constructive algorithm is also provided to obtain the state coordinate transformation. These results are an extension of their counterparts of nonlinear continuous‐time systems to nonlinear discrete‐time systems (Syst. Control Lett. 1986; 7 :133–142; SIAM. J. Control Optim. 2003; 41 :1756–1778; Int. J. Control 2004; 77 :723–734; Automatica 2006; 42 :321–328; IEEE Trans. Automat. Control 2007; 52 :83–88; IEEE Trans. Automat. Control 2004; 49 :1746–1750; Automatica 2006; 42 :2195–2200; IEEE Trans. Automat. Control 1996; 41 :598–603; Syst. Control Lett. 1997; 31 :115–128). Copyright © 2009 John Wiley & Sons, Ltd.     

Supervisory telerobotics testbed for unstructured environments

This article describes a telerobotics testbed for performing tasks such as assembly and repair of spacecraft in unstructured environments. This fully operational multiarm system can operate in teleoperated or supervisory control modes, as well as mixed shared-control modes, thus enabling operations in totally to partially unstructured environments. Various sources of uncertainty are identified and approaches to minimize their effects are presented. In the teleoperated mode, the system uses two force-reflecting hand controllers to operate two manipulator arms. A third arm is utilized to position four cameras to view the scene. In the supervisory mode, the system can be operated from three different levels: task, process, and servo levels, providing different levels of autonomy and performance. Various tools are provided so that an operator can perform tasks even when objects are partially occluded or their positions are not known a priori. Facilities are available for building models of previously unknown objects on-line, as well as generating collision-free motions. Architecturally, the system is designed so that an operator and much of the associated computing environment can be located remotely relative to the robots. As such, ground-controlled remote manipulation is possible.     

A note on static output‐feedback for affine nonlinear systems

In this paper we present sufficient conditions for the solvability of the static‐output feedback stabilization problem for affine nonlinear systems using a factorization approach. We extend the results of (IEEE Trans. Autom. Control, Vol. 47, No. 12, pp. 2038–2041 (2002)) to include disturbance‐attenuation and singular control. The sufficient conditions given are also less stringent than the ones given in (IEEE Trans. Autom. Control Vol. 47, No. 12, pp. 2038–2041 (2002)). The usefulness of the results are also illustrated with some examples. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptive backstepping sliding mode control for feedforward uncertain systems

Output tracking backstepping sliding mode control for feedforward uncertain systems is considered in this article. Feedforward systems are not usually transformable to the parametric semi-strict feedback form, and they may include unmatched uncertainties consisting of disturbances and unmodelled dynamics terms. The backstepping method presented in this article, even without uncertainties differs from that of Ríos-Bolívar and Zinober [Ríos-Bolívar, M. and Zinober, A.S.I. (1999), ‘Dynamical Adaptive Sliding Mode Control of Observable Minimum Phase Uncertain Nonlinear Systems’, in Variable Structure Systems: Variable Structure Systems, Sliding Mode and Nonlinear Control, eds., K.D. Young and Ü. Özgüner. Ozguner, London, Springer-Verlag, pp. 211–236; Ríos-Bolívar, M., and Zinober, A.S.I. (1997a), ‘Dynamical Adaptive Backstepping Control Design via Symbolic Computation’, in Proceedings of the 3rd European Control Conference, Brussels]. In this article, the backstepping is not a dynamical method as in Ríos-Bolívar and Zinober (1997a Ríos-Bolívar, M and Zinober, ASI. 1997a. Dynamical Adaptive Sliding Mode Output Tracking Control of a Class of Nonlinear Systems. International Journal of Robust and Nonlinear Control, 7: 387–405.  [Google Scholar], 1999), since at each step, the control and map input remain intact, and the differentiations of the control are not used. Therefore, the method can be introduced as static backstepping. Two different controllers are designed based upon the backstepping approach with and without sliding mode. The dynamic and static backstepping methods are applied to a gravity-flow/pipeline system to compare two methods.