Pursuit-evasion Game for Nonholonomic Mobile Robots With Obstacle Avoidance using NMPC

Sani, Mukhtar; Robu, Bogdan; Hably, Ahmad

doi:10.1109/med48518.2020.9182862

Cited by 17 publications

(22 citation statements)

References 22 publications

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“…Model Predictive Control is an online optimization technique which allow controller to compute new control variables at each decision instant. The main motivation for employing MPC technique was its ability to handle Nonlinear dynamics, Multi-Inputs Multi-outputs (MIMO) systems, states and inputs constraints as well as incorporating obstacles avoidance [11], [13]. In MPC, a controller is obtained by minimizing a cost function subject to constraints which comprises the dynamic model of the system.…”

Section: A Model Predictive Controlmentioning

confidence: 99%

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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)

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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.

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Section: A Model Predictive Controlmentioning

confidence: 99%

“…However, states constraints cannot be imposed and obstacles avoidance cannot be incorporated. In our earlier paper [11], we used NMPC based game-theoretic algorithms to solve PEG in the presence of obstacles. Game-theoretic algorithms were developed for both players, thus knowledge of full information is assumed.…”

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)

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“…In addition to its intricate computation, especially for nonholonomic systems, these methods could not incorporate obstacles avoidance. Model predictive Control approaches are used to handle pursuit-evasion games in [8], [9] and [10] due to their capacity of handling constraints. In [8], Non-linear Model Predictive Tracking Control (NMPTC) was employed to deal with symmetric pursuit-evasion games for unmanned aerial vehicles.…”

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)

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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.

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“…Several works involving discrete NMPC for controlling nonholonomic robots utilized the Euler method (also called Euler forward method [22], [36]. The resulting discrete model is given in (4).…”

Section: B Runge-kutta Discretization Methodsmentioning

confidence: 99%

“…Active collision avoidance system has become a research hot-spot in the field of automotive due to its ability to effectively improve traffic safety [20]. Static obstacles avoidance have been dealt with in [1], [22] for tracking problems, in [36] for pursuit-evasion games, in [14] for point-stabilization and in [35], [40] for path following problems. Dynamic collision avoidance among multiple mobile robots has been considered in [29].…”

Section: Introductionmentioning

confidence: 99%