An Introduction to Pursuit-evasion Differential Games

Weintraub, Isaac E.; Pachter, Meir; García, Eloy

doi:10.23919/acc45564.2020.9147205

Cited by 119 publications

(48 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A solution based on variational techniques was then proposed as a solution of differential games without treating any specific example. Solutions of pursuit-evasion games with more than one player in a team have been reviewed in [3]. In [4] and [5] safe reachable area minimization was presented to capture a single evader using a group of pursuers.…”

Section: Introductionmentioning

confidence: 99%

Pursuit-evasion Games Based on Game-theoretic and Model Predictive Control Algorithms

Sani

Robu

Hably

2021

2021 International Conference on Control, Automation and Diagnosis (ICCAD)

View full text Add to dashboard Cite

Non-cooperative game problems such as pursuitevasion require a solution approach that takes into consideration the strategy of the opponents. To predict the strategy of an opponent in a game, its full information is required and more computation time would be spent. However, this requirement of the opponent's full information is not realistic. Also, the computation time required by the game-theoretic algorithm (GTA) could make the controller unimplementable for some systems. Conversely, Model Predictive Control (MPC) could be use to solve the same problem using only the states information on the opponent by solving minimization or maximization cost functions. In this paper, we compared the GTA and MPC algorithm using two autonomous nonholonomic ground robots. Several simulations were conducted in the absence and presence of obstacles, using different initial conditions. The results obtained showed that the MPC algorithm can achieve similar performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Pursuit-evasion Games Based on Game-theoretic and Model Predictive Control Algorithms

Sani

Robu

Hably

2021

2021 International Conference on Control, Automation and Diagnosis (ICCAD)

View full text Add to dashboard Cite

show abstract

“…A classification-based solution depending on the number of players, i.e. one-pursuer-one-evader, N-pursuers-one-evader, one-pursuer-M-evader, and N-pursuers-M-evaders were summarised in [6]. Paper [7] deals with the cooperative two-pursuer one-evader differential game using a geometric approach.…”

Section: Introductionmentioning

confidence: 99%

Limited Information Model Predictive Control for Pursuit-evasion Games

Sani

Robu

Hably

2021

2021 60th IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

This paper explores the use of model predictive control (MPC) in dealing with the pursuit-evasion game (PEG) problem where players have incomplete information on their opponents. This is different from most cases in the literature where each player knows all the information (states information and dynamics) on the opponent. The burden caused by such demand for the opponent's full information induces the need for more sensors during physical implementation as well as high computation time. However, we found that only the current positions, i.e. x−y coordinate of the opponent, are indispensable. Thus, knowing the orientation and the dynamics of the opponent are insignificant to the performance of the game. We propose a new method to exploit a two-player PEG in the presence and absence of obstacles, where each player can only rely on the current position information of its opponent. Several simulation results show that the PEG problem can be handled and obstacles can be avoided using the proposed control protocol. We also show that our approach is robust to measurement noise and can perform better, in terms of the computation than the approach with full information.

show abstract

“…In [26], the authors study the possibility of role switch as well as the cooperation between the target and defender. The recent tutorial article [27] provides a survey of PE and TAD differential games. In all the above TAD game related works, all the players are assumed to have unlimited observations without visibility constraints.…”

Section: B An Overview Of Related Literaturementioning

confidence: 99%

Dynamic network analysis of a target defense differential game with limited observations

Singh,

Reddy

2021

Preprint

View full text Add to dashboard Cite

In this paper, we study a Target-Attacker-Defender (TAD) differential game involving one attacker and one target, both with unlimited visibility range, and multiple defenders with limited visibility capabilities. We assume that the target and attacker are unaware of defenders' visibility constraints which results in a dynamic game with asymmetric information. We seek to obtain the strategies that are likely to be used by the players. The visibility constraints of the defenders induce a visibility network which encapsulates the visibility information during the evolution of the game. Based on this observation, we introduce network adapted feedback or implementable strategies for the defenders. We construct a class of parametric performance indices for which the defenders' strategies along with standard feedback Nash strategies of the attacker and target provides a network adapted feedback Nash equilibrium. We introduce a consistency criterion for selecting a subset (or refinement) of network adapted feedback Nash strategies, and provide an optimization based approach for computing them. Finally, we illustrate our results with numerical experiments.

show abstract

An Introduction to Pursuit-evasion Differential Games

Cited by 119 publications

References 90 publications

Pursuit-evasion Games Based on Game-theoretic and Model Predictive Control Algorithms

Pursuit-evasion Games Based on Game-theoretic and Model Predictive Control Algorithms

Limited Information Model Predictive Control for Pursuit-evasion Games

Dynamic network analysis of a target defense differential game with limited observations

Contact Info

Product

Resources

About