Multiple Pursuers Research Articles

Pursuit Strategy to Capture High-Speed Evaders Using Multiple Pursuers

A novel cooperative pursuit strategy is presented in this paper that contributes to capture a high-speed evader using multiple pursuers. Owing to its superiority in terms of speed, contemporary works have elucidated that a high-speed evader demands multiple pursuers to capture, but struggled to establish a promising strategy. A recent work also challenged the claims of a formation (perfectly encircled formation) that was said to ensure capture. It was shown that there exists an escape strategy for a perfectly encircled formation. In this paper, a cooperative pursuit strategy is presented for multiple pursuers using overlapping Apollonius circles around an evader. An equally competent counterstrategy for an evader is also derived using the geometric properties of Apollonius circles. The strategies are evaluated for both holonomic and nonholonomic systems and the corresponding capturability sets are shown in empirical form. The parameters that have to be considered for the construction of a capturability set are also studied. The results obtained from numerous simulations demonstrate the performance of the cooperative pursuit strategy and the counterstrategy of a high-speed evader.

Cooperative pursuit with Voronoi partitions

This work considers a pursuit–evasion game in which a number of pursuers are attempting to capture a single evader. Cooperation among multiple agents can be difficult to achieve, as it may require the selection of actions in the joint input space of all agents. This work presents a decentralized, real-time algorithm for cooperative pursuit of a single evader by multiple pursuers in bounded, simply-connected planar domains. The algorithm is based on minimizing the area of the generalized Voronoi partition of the evader. The pursuers share state information but compute their inputs independently. No assumptions are made about the evader’s control strategies other than requiring the evader control inputs to conform to a speed limit. Proof of guaranteed capture is shown when the domain is convex and the players’ motion models are kinematic. Simulation results are presented showing the efficiency and effectiveness of this strategy.

Pursuit-Evasion Games of High Speed Evader

In this paper, we address pursuit-evasion games of high speed evader involving multiple pursuers and a single evader with holonomic constraints in an open domain. The existing work on this problem discussed the required formation and capture strategy for a group of pursuers. However, the formulation has mathematical errors and has raised concerns over the validity of the developed capture strategy. This paper uses the idea of Apollonius circle to develop an escape strategy for the high speed evader, resolving the shortfalls in the existing work. The strategy is built on a concept of perfectly encircled formation and the conditions required to construct the same are presented. The escape strategy contains two steps. Firstly, the evader employs a strategy that forces a gap in the formation against all the admissible strategies of a group of pursuers. In the second step, it uses this gap to escape. The strategy considers both direct and indirect gaps in the formations. The indirect gap is encountered when a group of three or four pursuers is employed to capture. The efficacy of the escape strategy is established using simulation results.

Decentralized strategy selection with learning automata for multiple pursuer–evader games

The multiple pursuers and evaders game may be represented as a Markov game. Using this modeling, one may interpret each player as a decentralized unit that has to work independently in order to complete a task. This is a distributed multiagent decision problem and several different possible solutions have already been proposed. However, most solutions require some sort of central coordination. In this paper, we intend to model each player as a learning automaton and let them evolve and adapt in order to solve the difficult problem they have at hand. We are also going to show that, using the proposed learning process, the players’ policies will converge to an equilibrium point. Simulations of such scenarios with multiple pursuers and evaders are presented in order to show the feasibility of the approach.

Autonomous Multi-Target Interception in Dynamic Settings – On-Line Pursuer Task Allocation

Abstract In this paper, we present a generic task-allocation methodology for time-optimal, autonomous on-line interception of multiple dynamic targets by a team of robotic pursuers. The proposed novel methodology is applicable to problems consisting of numerous variations of multiple pursuers and targets. The targets are assumed to be highly maneuverable with a priori unknown, though real-time trackable, motion trajectories. Guidance theory is employed to allow each of the pursuers to navigate autonomously towards its allocated target. Numerous simulations and experiments have verified that the proposed methodology is tangibly efficient in dynamic (one-to-one) re-pairing of pursuers to targets for minimum total overall interception time.

Evolving team behaviors with specialization

This article evaluates Collective Neuro-Evolution (CONE), a cooperative co-evolutionary method for solving collective behavior tasks and increasing task performance via facilitating behavioral specialization in agent teams. Specialization is used as a problem solving mechanism, and its emergence is guided and regulated by CONE. CONE is comparatively evaluated with related methods in a simulated evolutionary robotics pursuit-evasion task. This task required multiple pursuer robots to cooperatively capture evader robots. Results indicate that CONE is appropriate for evolving specialized behaviors. The interaction of specialized behaviors produces behavioral heterogeneity in teams and collective prey capture behaviors that yield significantly higher performances compared to related methods.

Modelling and analyses of WSN-based pursuit-evasion strategies for multi-pursuers to multi-evaders

Wireless sensor networks have yielded a lot of new theoretical problems for the localisation, tracking and navigation control of mobile objects within a local area, especially, for investigations of pursuit-evasion games. For the efficiency of capture and evasion of pursuers and evaders in pursuit-evasion games, several novel pursuit-evasion policies are proposed in this study. The vector-ward evasion strategy may bring the evader breaking away from the puzzledom between two fires and reduce fluctuation of evasion routes. The intelligent collaboration pursuit strategy effectively exerts the collaboration efficiency of multiple pursuers and shortens the capture time. The estimated location errors caused by wireless sensor networks were also involved for the pursuers and evaders in the simulation of this study. The results show that every pursuit-evasion strategy is valid and robust. In the simulation of multiple pursuers to capture multiple evaders, the object assignment method and the corresponding pursuit policies are reliable and can capture all evaders in a limited area as quickly as possible.

A cooperative Homicidal Chauffeur game

We address a pursuit-evasion problem involving an unbounded planar environment, a single evader and multiple pursuers moving along curves of bounded curvature. The problem amounts to a multi-agent version of the classic Homicidal Chauffeur problem; we identify parameter ranges in which a single pursuer is not sufficient to guarantee evader capture. We propose a novel multi-phase cooperative strategy in which the pursuers move in specific formations and confine the evader to a bounded region. The proposed strategy is inspired by the hunting and foraging behaviors of various fish species. We characterize the required number of pursuers for which our strategy is guaranteed to lead to confinement.

On Discrete-Time Pursuit-Evasion Games With Sensing Limitations

In this paper, we address discrete-time pursuit-evasion games in the plane where every player has identical sensing and motion ranges restricted to closed disks of given sensing and stepping radii. A single evader is initially located inside a bounded subset of the environment and does not move until detected. We propose a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sweep-pursuit-capture</i> pursuer strategy to capture the evader and apply it to two variants of the game. The first involves a single pursuer and an evader in a bounded convex environment, and the second involves multiple pursuers and an evader in a boundaryless environment. In the first game, we give a sufficient condition on the ratio of sensing to stepping radius of the players that guarantees capture. In the second, we determine the minimum probability of capture, which is a function of a novel pursuer formation and independent of the initial evader location. The sweep and pursuit phases reduce both games to previously studied problems with unlimited range sensing, and capture is achieved using available strategies. We obtain novel upper bounds on the capture time and present simulation studies that address the performance of the strategies under sensing errors, different ratios of sensing to stepping radius, greater evader speed, and a different number of pursuers.

Coordinated pursuer control using particle filters for autonomous search-and-capture

In a real-world pursuit-evasion (PE) game, the pursuers often have a limited field-of-view of the evaders and thus are required to search for and detect the evaders before capturing them. This paper presents a unified framework and control algorithm using particle filters (PFs) for the coordination of multiple pursuers to search for and capture multiple evaders given the ability of PF to estimate highly non-Gaussian densities prevalent in search problems. The pursuer control problem is formulated as a stochastic control problem where global objectives function of both searching and capturing are common. To take the evaders’ actions into account, an action measure (AM) is defined over the evaders’ PDs is used to represent the probability that the evader may transit each state in the PD. The global objective functions for search and capture are then decomposed into local objective functions for unification through objective priority weights. Coordination between the pursuers takes place through the multi-sensor update where the observation likelihoods of all pursuers are used in the PF update stage. The control actions of each pursuer are then determined individually, based on the updated PDs given the objective weights, action measures as well as evader importance weights in the case of multiple evaders. The proposed algorithm is tested in three scenarios for its effectiveness. In addition, a parametric study on the average capture time against the initial variances of the target state uncertainty is conducted to test for robustness. Results show that the pursuers are able to capture all the evaders in each case with the capture time for the second and last scenario differing by only 2.9% implying firstly that under the proposed algorithm, the capture time is not proportional to the increase in the number of evaders and also suggested robustness and potential scalability of the proposed algorithm.

A reachability-based strategy for the time-optimal control of autonomous pursuers

A control strategy for an autonomous pursuer is proposed in the reachability-based framework by using forward reachable sets (FRSs) to capture an evader vehicle time optimally. The FRS, which is a geometric representation of a vehicle's dynamic capability, allows the pursuers to determine if a single pursuer can capture the evader time optimally as well as to coordinate and maximize the chance of capturing the evader through the FRS coverage of multiple pursuers. The proposed strategy is then tested against the generic point-tracking (PT) algorithm in three different examples: (1) a single pursuer with sufficient manoeuvrability to capture an evader, (2) a single pursuer with significantly lower lateral manoeuvrability than the evader, and (3) multiple pursuers of the same manoeuvrability as in the second example. The numerical results demonstrate the superior performance of the proposed strategy over the PT algorithm when the pursuer has lower lateral manoeuvrability.

On some sufficient conditions for optimality of the pursuit time in the differential game with multiple pursuers

Consideration was given to the problem of optimal pursuit of one object by multiple objects. The player's moves obey the ordinary differential equations. Geometrical constraints are imposed on the player controls. Sufficient conditions were obtained for optimality of the pursuit time, and the optimal player strategies were constructed.

FAST PURSUIT OF MOBILE NODES USING TPR TREES

In this paper, we consider a set of mobile nodes, each of which moves with constant velocity. We present algorithms to determine which of these mobile nodes can be captured the soonest by a set of one or more pursuers. We use a time parameterized R-tree (TPR-tree) to index these moving points. We then develop algorithms that operate on the TPR-tree to answer three query variations: (1) a single pursuer that is faster than all of the mobile nodes, (2) a single pursuer that is slower than some of the mobile nodes and (3) multiple pursuers. Experimental results show that our algorithms are simpler and faster than other approaches to solve the problem.

Application of semantic control to a class of pursuer-evader problems

In this article, we describe our work in developing a comprehensive software system for tactical decision aiding for an evader faced with multiple pursuers. The objective is to provide the evader with defensive maneuver decisions that maximize its chances of survival. We have developed a hierarchical semantic controller consisting of a System Identifier, a Goal Selector and an Adapter. This system is implemented on a 386DX personal computer via object-oriented programming, knowledge-based systems, Analytical Hierarchy Process, optimal control, and differential game methodologies. The viability of our Semantic Control approach to the evasive action selection problem has been shown and its operation has been tested against pursuers which follow either pure pursuit or proportional guidance strategies. The user is included in the decision process via approval of setpoints. Displays of the engagement and effect of coverage by countermeasures provide a visual reinforcement of the recommendation made. Use of semantic controllers as TDA support systems for man-in-the-loop, pursuer-evader problems on a PC with current software and hardware technology is feasible. The ability to deploy the system on a portable PC permits the use of the technology in a wide variety of applications.