1,721,106 research outputs found
Set membership localization of mobile robots via angle measurements
No full textThis paper addresses the localization problem for a mobile robot navigating in an unstructured outdoor environment. A new technique is introduced, for computing an estimate of the position of the robot and the related uncertainty region, in the presence of visual angle measurements affected by bounded errors. The proposed set membership estimation procedure exploits the structure of the static set estimator, to solve recursively the dynamic localization problem
A Family of Switching Pursuit Strategies for a Multi-Pursuer Single-Evader Game
No full textA new family of pursuit strategies is introduced for a multi-pursuer single-evader game. By exploiting the optimal solution of the game involving two pursuers, conditions are derived under which the multi-pursuer game becomes equivalent to the two-pursuer one. This opens the possibility of designing a number of pursuit strategies in which the pursuers first try to enforce the satisfaction of the aforementioned condition and then switch to a two-pursuer game as soon as it is verified. The contribution is useful in two ways. First, new winning pursuit strategies can be devised starting from simple plans, such as pure pursuit. Moreover, the performance of existing pursuit strategies, like those based on Voronoi partitions, can be significantly improved by resorting to the corresponding switching version
A Two-Pursuer One-Evader Game with Equal Speed and Finite Capture Radius
No full textIn this paper, a two-pursuer one-evader game in the plane is considered. All the agents have simple motion and the same speed. As opposed to the game with superior pursuers, capture can occur in finite time only by defining a nonzero capture radius and for a subset of initial game states. Such a set is characterized and the full game solution is provided. In particular, the value function of the game and explicit expressions of the closed-loop optimal strategies of all the agents are derived. The results are validated via numerical simulations, comparing the optimal control actions with alternative strategies for both the evader and the pursuers
On the advantage of centralized strategies in the three-pursuer single-evader game
No full textCooperation in multi-pursuer games is known to be useful. However, it is not easy to quantify how much it is convenient for the pursuers to play according to a centralized strategy with respect to a decentralized one. This paper provides an answer to this question, for the problem of three pursuers chasing a single evader in a planar environment. It is shown that centralized pursuit algorithms can halve the time required to capture the evader, with respect to decentralized pursuit strategies. Moreover, this limit is proven to be tight. Numerical computations of lower bounds to the ratio between the capture times of centralized and decentralized strategies, show that for several game initial conditions the benefit of playing in a centralized way may be significantly less than halving the game duration
Cooperative versus non-cooperative strategies in three-pursuer single-evader games
No full textThe value of cooperation in pursuit-evasion games is investigated. The considered setting is that of three pursuers chasing one evader in a planar environment. The optimal evader trajectory for a well-known non-cooperative pursuer strategy is characterized. This result is instrumental to derive upper and lower bounds to the game length, in the case in which the pursuers cooperate in the chasing strategy. It is shown that the cooperation cannot reduce the capture time by more than one half with respect to the non-cooperative case, and that such bound is tight
On worst-case approximation of feasible system sets via orthonormal basis functions
No full textThis note deals with the approximation of sets of linear time-invariant systems via orthonormal basis functions. This problem is relevant to conditional set membership identification, where a set of feasible systems is available from observed data, and a reduced-complexity model must be estimated. The basis of the model class is made of impulse responses of linear filters. The objective of the note is to select the basis function poles according to a worst-case optimality criterion. Suboptimal conditional identification algorithms are introduced and tight bounds are provided on the associated identification errors
An efficient algorithm for the construction of l1 uncertainty model sets
No full textRobust control techniques require the construction of uncertainty model sets. When dealing with unstructured norm-bounded uncertainties, it is important that the size of the uncertainty set is minimized, so that robust performances can be enhanced. This paper addresses the problem of constructing the minimum l1 uncertainty model set containing a finite set of assigned models. The problem is formulated as a conditional Chebyshev center problem and an efficient algorithm for its solution is proposed. The algorithm converges in a finite number of steps and is able to deal with large size problems in reasonable time
A Set Theoretic Approach for Time to Contact Estimation in Dynamic Vision
No full textThis paper deals with the estimation of the time to contact in dynamic vision problems. A novel approach based on set membership estimation theory is proposed. It allows for the computation of bounds on the time to contact estimates in finite time
An iterative optimization-based approach to piecewise affine system identification
No full textIn this paper, we propose an iterative approach to PWA system identification. At each iteration, a single optimization problem is solved, performing simultaneously the estimation of the partition of the regressor domain, the assignment of data points to submodels, and the estimation of the submodel parameters. A nice feature of the proposed approach is that at each iteration it provides a classification of the data points that is linearly separable by construction, while guaranteeing that the value of the prediction error criterion is non-increasing along the iterations. The optimization problem solved at each iteration is a mixed integer program, where the classification involves only a fixed number of data points close to the boundaries of the partition estimated at the previous iteration. This number can be tuned to control the computational burden of the mixed integer program to be solved. The proposed technique can be applied to tackle an identification problem from scratch, or to refine the solution provided by other suboptimal techniques. This is shown through an application to the pick-and-place machine data set
An interpolatory algorithm for distributed set membership estimation in asynchronous networks
No full textThis paper addresses distributed estimation problems over asynchronous networks in a set membership framework. The agents in the network asynchronously collect and process measurements, communicate over a possibly time-varying and unbalanced directed graph and may have non negligible computation times. Measurements are affected by bounded errors, so that they define feasible sets containing the unknown parameters to be estimated. The proposed algorithm requires each agent to compute a weighted average of its estimate and those of its neighbors and to project it onto a local feasible set. By assuming convexity of the measurement sets, the local estimates are shown to converge to a common point belonging to the global feasible set
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