This article considers the pursuit-evasion game of two dynamic systems, which are subject to safety constraints, and in order to additionally guarantee the safety of the system, we propose safety-aware pursuit and escape strategies by combining control barrier function (CBF) and off-policy learning technique. Different from existing pursuit and evader strategies, a safeguarding control law is first designed based on CBF to prioritize the safety of pursuer’s and evader’s trajectories, and then bounded game strategies are proposed by elaborately designing a new cost function. We also provide the sufficient condition for the stability of the closed-loop system with the state denoted by position difference, under which, the pursuer is able to capture the evader. It is worth mentioning that our strategies do not require the knowledge of system dynamics, which are essentially online learning-based ones, featured with the ability of satisfying the safety constraints in the pursuit-evasion game.

In this paper, a non-visual robotic formation control method based on a streaming communication architecture and a leader-follower control model is studied. The proposed stream-based communication architecture is inspired by the flocking behavior of fish. We analogize it into a form resembling an N-ary tree for communication purposes. Communication proceeds to the next layer only when all nodes in the upper layer have completed the follower selection. We also introduce a fault-tolerance mechanism and a termination filtering mechanism to prevent multiple leaders from choosing the same follower, avoiding a scenario where the robots in the last layer enter an endless loop of follower selection. The proposed stream-based communication architecture, built upon serial and parallel tracking, can achieve more complex formations, such as rectangular formations, closely resembling real-world scenarios, significantly enhancing formation efficiency. Simulation experiments on the e-puck platform validate the effectiveness and robustness of this architecture.

Emergency signage systems provide effective route guidance and evacuation information for pedestrians in case of emergencies such as fire, blackout, and explosion. This paper proposes a modified social force (SF) model to investigate crowd evacuation dynamics taking into account the influence of emergency signs. The perceiving probability model is formulated for the quantitative description of the probability that pedestrians can successfully notice the sign and clearly perceive the guidance information. Simulation experiments and controlled experiments are designed to calibrate the parameters of the proposed model. The effectiveness of the modified SF model is preliminarily verified by comparing the simulation results with experimental data and/or empirical results such as fundamental diagrams and self-organization phenomena. A case study of crowd evacuation simulations at a typical Beijing subway station is conducted to evaluate evacuation performance of three signage distribution schemes, i.e., Maximal Covering (MaxCover), Uniform, and Random, which are proposed by the maximal covering location and empirical approaches, as well as contrasted scheme without emergency signs. The effects of the quantity and distribution of emergency signs on crowd evacuation efficiency are studied quantitatively by simulations. The results show that installing emergency signs can improve evacuation efficiency no matter what distribution scheme is adopted. By choosing an appropriate distribution scheme i.e., MaxCover, the evacuation performance can be further improved and the evacuation time can be significantly reduced.

Convolutional neural networks are widely used in artificial intelligence and Internet of Things area. As the scale of convolutional neural network expands, more and more processing units are provided for it. The systems are easy prone to error, and any computing problems in any layer of the network will lead to wrong output results. Traditional multimode redundancy methods make the systems more complex, and increase power consumption. This paper proposes an autonomous error-tolerant architecture for convolutional neural networks. Taking the LeNet-5 as an example, the network layers of CNN are mapped on the AET architecture, an error-tolerant synapse is designed to discover the errors, an active evolution scheme is designed to handle unrecoverable errors and implement network reconfiguration. This design is implemented on FPGA, and the experimental results show that this architecture can realize effective error tolerance for convolutional neural network and has fast error recovery ability under the premise of ensuring the same recognition accuracy.

Microblog opens a prosperous cyberspace, where people on the earth can quickly publish and receive information in a tremendously quick manner. Also different organizations including government create acOn measuring and modeling governmental accounts in sina microblog servicecounts in the microblog service in order to interact with the public society. Sina microblog service, as being the biggest Chinese microblog service player, attracts millions of users and organizations. We collect and analyze data from all verified governmental accounts in the Sina microblog service. Then multiple plots around friend count, follower count, microblog count are illustratively presented. It has been found that many verified governmental accounts have been discarded by their owners. Also, the content productivity is highly correlated with the hierarchical level in the realworld governmental structure, besides other typical exponential characteristics.

This paper proposes a hybrid control algorithm for mobile manipulation subject to joint-space constraints. More specifically, we consider the problem of making the end-effector of a planar manipulator attached to a nonholonomic mobile platform reach a set-point or equivalently follow a given path in workspace by means of kinematic actuation. A switched control strategy allows the robot to avoid singular joint configurations and execute a pseudo-inverse based feedback using bounded and continuous control signals. The switching scheme is also utilized to maintain feasible joint configurations. Numerical examples provide an intuitive understanding of the algorithm's workings.

The extensive uses of Cloud Computing and Service-Oriented Architecture technologies have effectively prompted enterprises to implement cross-organizational workflow cooperative works. In the service-oriented large-scale distributed computing environment characterized by openness, dynamism and uncertainty, the reliable service computation for cross-organizational workflow applications is facing some new challenges. Based on the theory of enterprise ontology, we propose a reliable service computing platform architecture for cross-organizational workflow applications. In this paper, we discuss the system realization detail. First, we propose a semantic ontology model EOM (Enterprise Ontology Model) considering workflow's QoS and the trust evolvement of cross-organizational workflow services to support the reliable service modeling of cross-organizational business process. We also introduce the flexibility simulation algorithm and pre-treatment scheduling emergency mechanism. After discussing selection of trustworthy cooperative enterprises for cross-organizational workflows, we give a case application on process modeling for cross-organizational e-Learning services. The research is significant for modern enterprise management, cross-organizational workflow services and heterogeneous system integration.

In this paper, we give a distributed solution to the problem of making a team of nonholonomic robots reach consensus about their orientations using monocular cameras. We consider a scheme where the motions of the robots are decided using nearest-neighbor rules. Each robot is equipped with a camera and can only exchange visual information with a subset of the other robots. The main contribution of this paper is a new controller that uses the epipoles that are computed from the images provided by neighboring robots, eventually reaching consensus in their orientations without the necessity of directly observing each other. In addition, the controller only requires a partial knowledge of the calibration of the cameras in order to achieve the desired configuration. We also demonstrate that the controller is robust to changes in the topology of the network and we use this robustness to propose strategies to reduce the computational load of the robots. Finally, we test our controller in simulations using a virtual environment and with real robots moving in indoor and outdoor scenarios.

This paper investigates a distributed tracking problem for multi-agent systems with a varying-velocity leader. The leader modeled by a double integrator can only be perceived by followers located within a sensing distance. The objective is to drive the followers with bounded control law to maintain connectivity, avoid collision and further track the leader, with no need of acceleration measurements. Two cases are considered: the acceleration of the leader is bounded; and the acceleration has a linear form. In the first case, the relative velocities of neighbors are integrated and transmitted as a new variable to account for the uncertain time-varying acceleration. In the second case, two distributed estimators are added for the leader's position and velocity. Simulations are presented to show the effectiveness of the proposed control laws.

This paper proposes a local information based control law in order to solve the planar manipulation problem of rotating a grasped rigid object to a desired orientation using multiple mobile manipulators. We adopt a multi-agent systems theory approach and assume that: (i) the manipulators (agents) are capable of sensing the relative position to their neighbors at discrete time instances, (ii) neighboring agents may exchange information at discrete time instances, and (iii) the communication topology is connected. Control of the manipulators is carried out at a kinematic level in continuous time and utilizes inverse kinematics. The mobile platforms are assigned trajectory tracking tasks that adjust the positions of the manipulator bases in order to avoid singular arm configurations. Our main result concerns the stability of the proposed control law.

In this paper, the multi pursuer version of the pursuit evasion problem in polygonal environments is addressed. This problem is NP-hard, and therefore we seek good enough, but not optimal solutions. By modeling the problem as a Boolean Control Network, we can efficiently keep track of which regions are cleared, and which are not, while the input nodes of the network are used to represent the motion of the pursuers. The environment is partitioned into a set of convex regions, where each region correspond to a set of nodes in the network. The method is implemented in ANSI C, and efficiently solves complex environments containing multiple loops and requiring so-called recontamination. The provided examples demonstrate the effectiveness of the method in terms of computational time.

A novel human-computer interaction (HCI) system based on real-time image processing is proposed in this paper. With the help of infrared tracking technology, the proposed system achieves real-time processing and stable operation on an ADSP-BF533 hardware platform. Compared with the conventional remote control methods, the proposed system enables a user to control the cursor on the screen of a TV by targeting it, and provides users with new experiences of remote control. To realize such a new remote control system, cross-ratio invariant, which is an important characteristic of the projective transformation, is also studied. Experimental results show the potential of the proposed system in TV remote control.

In this paper, leader-following formation control for mobile multiagent systems with limited sensor information is studied. The control algorithms developed require information available from onboard sensors only, and in particular, the measurement of the leader (neighbor) speed is not needed. Instead, an observer is designed for the estimation of this speed. With the proposed control algorithms as building blocks, many complex formations can be obtained.

In this paper, a recursive smoothing spline approach for contour reconstruction is studied and evaluated. Periodic smoothing splines are used by a robot to approximate the contour of encountered obstacles in the environment. The splines are generated through minimizing a cost function subject to constraints imposed by a linear control system and accuracy is improved iteratively using a recursive spline algorithm. The filtering effect of the smoothing splines allows for usage of noisy sensor data and the method is robust to odometry drift. Experimental evaluation is performed for contour reconstruction of three objects using a SICK laser scanner mounted on a PowerBot from ActivMedia Robotics.

In this paper, formation adaptation and stability for mobile multi-agent systems is studied. The objective is to suggest a set of robust control functions that can be combined to build complex formations of mobile robots. A properly designed formation should be able to follow a single leader in a clustered environment and to, if necessary, adapt to the surroundings by changing its shape. The two control algorithms proposed here are adapted for systems with limited communication capacity and low performance sensors. The algorithms only require information that can be directly achieved from on-board sensors and, in particular, they only need very coarse estimation of the velocity of the neighbors in the formation. An arbitrary change of the shape of a formation may require switching between control algorithms. In the paper, it is shown that switching between the two proposed control algorithms is stable under some reasonable assumptions. The results are verified by simulations, which also show that switching can be performed safely even with a high noise level and no prior filtering of sensor input

In this paper stability and formation adaptation of mobile multi-agent systems with limited sensor information is studied. A globally stable control for line formations with varying separations and bearing angles between the agents is evaluated in simulations and experiments with Khepera robots. The control algorithm only requires information available from on-board sensors, although stability is improved if communication and sharing of information between the agents is possible. In addition, the control only needs a coarse estimation of the actual target speed

In this paper mobile multi-agent systems with limited sensor information are studied. Some control algorithms are proposed that do not require global information, and are easy to implement. First two basic controls for a single mobile agent tracking another moving object are derived. Then it is shown how these basic controls can be combined in order to achieve more complex formations.

The problem of coordinating a team of robots with heterogeneous sensing capabilities is studied in this paper. A control structure is proposed that combines terrain servoing for the leading robot with on-line formation planning and path following for the followers, while enabling obstacle avoidance for all the robots.

Electronic compass is often used to provide the absolute heading reference for tracking the user's head and hands in virtual reality (VR) and augmented reality (AR), especially for outdoor AR applications. However, compass is vulnerable to environment magnetism disturbance. Existing compass calibration methods require complex steps and true heading reference which is often impossible to be obtained in outdoor AR applications, and is useful only when compass is in horizontal plane. An autocalibration method without the need of heading reference and redundant sensors is proposed in This work. First the compass error model based on physical principle is presented, then the algorithm to calculate the compensation coefficients with a set of sample measurements of the sensors in the compass is described. Because the influence of the environmental disturbance has been effectively compensated, the calibrated compass can provided accurate heading even when it is under large tilt attitude.

The problem of estimating and tracking the motion of a moving target by a team of mobile robots is studied in this paper. Each robot is assumed to have a directional sensor with limited range, thus more than one robot (sensor) is needed for solving the problem. A sensor fusion scheme based on inter-robot communication is proposed in order to obtain accurate real-time information of the target's position and motion. Accordingly a hierarchical control scheme is applied, in which a consecutive set of desired formations is planned through a discrete model and low-level continuous-time controls are executed to track the resulting references. The algorithm is illustrated through simulations and on an experimental platform.

In this paper, the multiagent coordination problem is studied. This problem is addressed for a class of robots for which control Lyapunov functions can be found. The main result is a suite of theorems about formation maintenance, task completion time, and formation velocity. It is also shown how to moderate the requirement that, for each individual robot, there exists a control Lyapunov function. An example is provided that illustrates the soundness of the method.

We propose a model independent coordination strategy for multi-agent formation control. The main theorem states that under a bounded tracking error assumption our method stabilizes the formation error. We illustrate the usefulness of the method by applying it to rigid body constrained motions, as well as to mobile manipulation.

We study the attitude estimation problem for an accelerated rigid body using gyros and accelerometers. The application in mind is that of a walking robot and particular attention is paid to the large and abrupt changes in accelerations that can be expected in such an environment. We propose a state estimation algorithm that fuses data from rate gyros and accelerometers to give long-term drift free attitude estimates. The algorithm does not use any local parameterization of the rigid body kinematics and can thus be used for a rigid body performing any kind of rotations. The algorithm is a combination of two non-standard, but in a sense linear, Kalman filters between which a trigger based switching takes place. The kinematics representation used makes it possible to construct a linear algorithm that can be shown to give convergent estimates for this nonlinear problem. The state estimator is evaluated in simulations demonstrating how the estimates are long term stable even in the presence of gyrodrift.

In this paper state observers for control systems with nonlinear outputs are studied. For such systems, the observability does not only depend on the initial conditions, but also on the exciting control used. In this paper some sufficient conditions are given for the convergence of an observer. It is also discussed, via a vision example, how to actively excite the system in order to improve the observability.

For a robot trying to reach places in at least partially unknown environments there is often a need to replan paths online based on information extracted from the surroundings. In the paper is is assumed that the sensing range of the robot is short compared to the length of the paths it plans and the environment is modeled as a graph consisting of nodes and arcs. The replanning problem is solved using the network simplex method. The applicability of the planner is demonstrated by integrating it with a navigation control strategy. Simulation results show that the approach works well.

We propose a model independent coordination strategy for multi-agent formation control. The main theorem states that under a bounded tracking error assumption, our method stabilizes the formation error. We illustrate the usefulness of the method by applying it to rigid body constrained motions.

A platform independent control approach for mobile manipulation and coordinated trajectory following is proposed and analyzed. Given a path for the gripper to follow, another path is planned for the base in such a way that it is feasible with respect to manipulability. The base and the end-effector then follow their respective reference trajectories according to proven stable, error-feedback control algorithms, while the base is placed in such a way that the end-effector trajectory always is within reach for the manipulator.

A solution to the trajectory tracking problem for mobile manipulators is proposed, that allows for the base to be influenced by a reactive, obstacle avoidance behavior. Given a trajectory for the gripper to follow, a tracking algorithm for the manipulator is designed, and at the same time the base motions are generated in such a way that the base is coordinated with the gripper. Furthermore, it is shown that the method allows arbitrary upper and lower bounds on the gripper-base distance to be set and this can be achieved without introducing deadlocks into the system. The solution also ensures that the control effort, spent on slow base motions, is kept small.

A solution to the problem of controlling a car-like nonholonomic robot is proposed using a "virtual" vehicle approach, which is shown to be robust with respect to errors and disturbances. The proposed algorithms are model independent, and the stability analysis is done using a dynamical model in which, for instance, the side slip angles are taken into account.