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In this study, three general formulations are presented for trajectory optimisation in patrolling problems. In the traditional patrolling problem, some basic assumptions are made (often implicitly). For example, it is known how many robots and how many starting depots exist. Furthermore, the starting depots are assumed to be pre‐specified. Each of the three formulations provided here relaxes some (or all) of these assumptions, hence generalising the patrolling problem. A group of robots are supposed to travel through a number of nodes (viewpoints) in such an order so that the total travel distance is minimised. This problem is, in fact, a variant of the Travelling Salesman Problem and is called Multidepot multiple Travelling Salesman Problem. The effectiveness of the approach is demonstrated by comparing the results with those in the literature. [ABSTRACT FROM AUTHOR]

This paper investigates the expected rate of convergence to consensus in an asymmetric network represented by a weighted directed graph. The initial state of the network is represented by a random vector and the expectation is taken with respect to the random initial condition of the network. The proposed convergence rate is described in terms of the eigenvalues of the Laplacian matrix of the network graph. The generalized power iteration algorithm is then introduced based on the Krylov subspace method to compute the proposed expected convergence rate in a centralized fashion. To this end, the Laplacian matrix of the network is transformed to a new matrix such that existing techniques can be used to find the eigenvalue representing the expected convergence rate of the network. The convergence analysis of the centralized algorithm is performed with a prescribed upper bound on the approximation error of the algorithm. A distributed version of the centralized algorithm is then developed using the notion of consensus observer. The efficiency of the algorithms is subsequently demonstrated by simulations. [ABSTRACT FROM AUTHOR]

The consensus and containment problems in a multi-agent system consisting of single integrators with angular field of view (FOV) constraints in their sensing capabilities are investigated in this paper. First, it is assumed that all FOVs are half-planes and an impulsive switching strategy is developed such that the underlying sensing graph of the network remains uniformly quasi-strongly connected (UQSC) throughout the system evolution. The control schemes are designed in the framework of switched interconnected systems in such a way that the objectives of consensus and containment are achieved over the entire network. Then, the problem is extended to address a network of single-integrator agents with limited heterogeneous angular FOVs. The FOV of all sensing devices are assumed to rotate with sufficiently large angular velocities, which are controlled independently along with the translational motion of all agents. The velocity vector and the lower bound on the magnitude of the angular velocity of the FOVs are designed such that the agents converge to an arbitrarily small ball, and reach consensus. The convergence of the moving followers to the convex hull of static leaders is addressed for the containment problem as well. Simulation results verify the effectiveness of the proposed control strategies. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of minimum-energy cooperative transmission in wireless networks under the assumption of limited channel state information at the transmitters. It is assumed that only the average statistics of the fading channels are known to the transmitters. The objective here is to jointly optimize the set of relays and the transmission powers for both the broadcasting and cooperative transmission phases while satisfying probabilistic signal-to-noise ratio (SNR) constraints at the relays and at the destination node. Increasing the broadcasting power expands the set of potential relays and decreases the required power for cooperative transmission. Hence, there is a compromise in the selection of the power values, which is addressed in this work using a chance-constrained optimization framework. A closed-form approximate solution is also presented, which provides a low-complexity transmission scheme for energy-harvesting wireless networks. Simulations are presented to demonstrate the efficacy of the proposed approach and the approximate solution. [ABSTRACT FROM AUTHOR]

In this paper, a distributed consensus control strategy is presented for a team of unicycle agents subject to external disturbances. Bounded disturbances with unknown dynamics on both translational and angular velocities are applied to the system. The key idea is to design the control inputs of each agent in such a way that, after a finite time, agents move with an acute angle with respect to a reference vector typically used for the consensus control of disturbance-free single-integrator agents. Convergence to consensus is then proved using Lyapunov theory. Simulation results confirm the efficacy of the proposed controller. [ABSTRACT FROM AUTHOR]

Abstract: A time-optimal reconfiguration strategy for formation flying of autonomous acceleration-controlled agents is presented. In the proposed strategy, the agents are moved to a special designated formation in the time interval between the completion of the mission in the current formation and the issuance of the next reconfiguration command. It is shown that the problem of finding the special designated formation which minimizes the expected value of the reconfiguration time is nonconvex. This optimization problem is treated for two cases of constrained acceleration, and constrained acceleration and velocity. It is shown that in both cases, the search space for finding the special designated formation can be reduced to a convex compact set. An alternative search algorithm is presented for the second case, which consists of searching a vicinity of possible formations, and solving a convex nondifferentiable optimization problem. This search algorithm is typically much faster than the one concerning the acceleration constraint only. The effectiveness of the proposed strategy is illustrated by simulation. [Copyright &y& Elsevier]

Abstract: In this paper, the concept of two-stage energy-optimal reconfiguration strategy for formation flying of autonomous agents is presented. It is desired to move the agents to a special designated formation in the idle time (the time between the completion of the current formation mission and the issuance of the next reconfiguration command), in order to minimize the expected value of the reconfiguration energy consumption. The position of each agent in the special designated formation is obtained as a function of the agent’s current position and the weighted center of gravity of the set of possible positions for that agent. It is shown that this strategy can reduce energy consumption considerably compared to conventional strategies. In the case of constrained acceleration, it is shown that the results remain the same as the unconstrained case if the reconfiguration time is sufficiently longer than its minimum possible value. The effectiveness of the proposed strategy is illustrated by simulation. [Copyright &y& Elsevier]

Abstract: In this paper, we develop stability and control design framework for time-varying and time-invariant sets of nonlinear dynamical systems using vector Lyapunov functions. Several Lyapunov functions arise naturally in multi-agent systems, where each agent can be associated with a generalized energy function which further becomes a component of a vector Lyapunov function. We apply the developed control framework to the problem of multi-vehicle coordinated motion to design distributed controllers for individual vehicles moving in a specified formation. The main idea of our approach is that a moving formation of vehicles can be characterized by a time-varying set in the state space, and hence, the problem of distributed control design for multi-vehicle coordinated motion is equivalent to the design of stabilizing controllers for time-varying sets of nonlinear dynamical systems. The control framework is shown to ensure global exponential stabilization of multi-vehicle formations. Finally, we implement the feedback stabilizing controllers for time-invariant sets to achieve global exponential stabilization of static formations of multiple vehicles. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the problem of designing a switching policy for an adaptive switching control system is formulated as a problem of supervisory control of a discrete-event system (DES). Two important problems in switching control are then addressed using the DES formulation and the theory of supervisory control under partial observation. First, it is verified whether for a given set of controllers, a switching policy satisfying a given set of constraints on the transitions among controllers exists. If so, then a minimally restrictive switching policy is designed. Next, an iterative algorithm is introduced for finding a minimal set of controllers for which a switching policy satisfying the switching constraints exists. It is shown that in the supervisory control problem considered in this paper, limitations on event observation are the factors that essentially restrict supervisory control. In other words, once observation limitations are respected, limitations on control will be automatically satisfied. This result is used to simplify the proposed iterative algorithm for finding minimal controller sets. [Copyright &y& Elsevier]

In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.

Mobile sensor networking technology has attracted considerable attention in various research communities in recent years due to their widespread applications in civilian and military environments. One objective when using mobile sensors is to obtain maximum field coverage by properly deploying sensor nodes. In many real-world applications a priori knowledge about the best deployment position for the sensors is not available. However, the motion capability of the sensors could allow each node to adjust its position (i.e. relocate) so that a better (and ultimately maximal) coverage is achieved. In this paper, a novel autonomous joint sensing range and relocation control algorithm is presented that achieves improved coverage and network lifetime at the same time. In the proposed algorithm, the sensing range of each sensor is adjusted iteratively based on its residual energy. At the same time, the sensor is directed to move within its corresponding multiplicatively weighted Voronoi (MW-Voronoi) region to ultimately increase sensing coverage in the field. Simulation results demonstrate the efficacy of the technique.

Ultrasound elastography involves measuring the mechanical properties of tissue, and has many applications in diagnostics and intervention. A common step in different elastography methods is imaging the tissue while it undergoes deformation and estimating the displacement field from the images. A popular next step is to estimate tissue strain, which gives clues into the underlying tissue elasticity modulus. To estimate the strain, one should compute the gradient of the displacement image, which amplifies the noise. The noise is commonly minimized by least square estimation of the gradient from multiple displacement measurements, which reduces the noise by sacrificing image resolution. In this work, we adaptively adjust the level and orientation of the smoothing using two different mechanisms. First, the precision of the displacement field decreases significantly in the regions with high signal decorrelation, which requires increasing the smoothness. Second, smoothing the strain field at the boundaries between different tissue types blurs the edges, which can render small targets invisible. To minimize blurring and noise, we perform anisotropic smoothing parallel to the direction of edges. The first mechanism ensures that textures/variations in the strain image reflect underlying tissue properties and are not caused by errors in the displacement estimation. The second mechanism keeps the edges between different tissue structures sharp while minimizing the noise. We validate the proposed method using phantom and in-vivo clinical data.