Abstract-We consider the problem of having a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) pursue a second team of evaders while concurrently building a map in an unknown environment. We cast the problem in a probabilistic game theoretical framework, and consider two computationally feasible greedy pursuit policies: local-max and global-max. To implement this scenario on real UAVs and UGVs, we propose a distributed hierarchical hybrid system architecture which emphasizes the autonomy of each agent, yet allows for coordinated team efforts. We describe the implementation of the architecture on a fleet of UAVs and UGVs, detailing components such as high-level pursuit policy computation, map building and interagent communication, and low-level navigation, sensing, and control. We present both simulation and experimental results of real pursuit-evasion games involving our fleet of UAVs and UGVs, and evaluate the pursuit policies relating expected capture times to the speed and intelligence of the evaders and the sensing capabilities of the pursuers.

Abstract-In this paper, we present a nonlinear model predictive control (NMPC) for multiple autonomous helicopters in a complex.environment. The NMPC provides a framework to solve optimal discrete control problems for a nonlinear system under state constraints and input saturation. Our approach combines stabilization of vehicle dynamics and decentralized trajectory generation, by including a potential function that reflects the state information of possibly moving obstacles or other vehicles to the cost function. We present various realistic scenarios which show that the integrated approach outperforms a hierarchical structure composed of a separate controller and a path planner based on the potential function method. The proposed approach is combined with an efficient numerical algorithm, which enables the real-time nonlinear model predictive control of multiple autonomous helicopters.

In this paper, we investigate the feasibility of a nonlinear model predictive tracking control (NMPTC) for autonomous helicopters. We formulate a NMPTC algorithm for planning paths under input and state constraints and tracking the generated position and heading trajectories, and implement an on-line optimization controller using gradient-descent method. The proposed NMPTC algorithm demonstrates superior tracking performance over conventional multi-loop proportional-derivative (MLPD) controllers especially when nonlinearity and coupling dominate the vehicle dynamics. Furthermore, NMPTC shows outstanding robustness to parameter uncertainty, and input saturation and state constraints are easily incorporated. When the cost includes an potential function with a possibly moving obstacle or other agents' state information, the NMPTC can solve the trajectory planning and control problem in a single step. This constitutes a promising one-step solution for trajectory generation and regulation for RUAVs, which operate under various uncertainties and constraints arising from the vehicle dynamics and environmental contingencies. The computation load of this approach is significantly less than many existing model predictive algorithms, thus enabling real-time applications for autonomous helicopters.

This paper introduces the development of multiple number of Unmanned Arial Vehicle (UAV) system as a part of BErkeley AeRobot (BEAR) project, highlighting the recent achievements in the design and implementation of rotorcraft-based UAV (RUAV) control system. Based on the experimental flight data, linear system model valid near hover condition is found by applying time-domain numerical methods to experimental flight data. The acquired linear model is used to design feedback controller consisting of inner-loop attitude feedback control, mid-loop velocity feedback control and the outer-loop position control. The proposed vehiclelevel controller is implemented and tested in Berkeley UAV, Ursa Magna 2, and shows superior hovering performance. The vehicle level controller is integrated with higher-level control using a script language framework to command UAV.

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