David Hyunchul Shim

Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation

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-mar 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.

A flight control system for aerial robots: algorithms and experiments

This paper presents a hierarchical flight control system for unmanned aerial vehicles. The proposed system executes high-level mission objectives by progressively substantiating them into machine-level commands. The acquired information from various sensors is propagated back to the higher layers for reactive decision making. Each vehicle is connected via standardized wireless communication protocol for scalable multi-agent coordination. The proposed system has been successfully implemented on a number of small helicopters and validated in various applications. Results from waypoint navigation, a probabilistic pursuit-evasion game and vision-based target tracking demonstrate the potential of the proposed approach toward intelligent flying robots.

Decentralized nonlinear model predictive control of multiple flying robots

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.

HIERARCHICAL CONTROL SYSTEM SYNTHESIS FOR ROTORCRAFT-BASED UNMANNED AERIAL VEHICLES

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.

Autonomous Exploration in Unknown Urban Environments for Unmanned Aerial Vehicles

§In this paper, we present an autonomous exploration method for unmanned aerial vehicles in unknown urban environment. We address two major aspects of explorationgathering information about the surroundings and avoiding obstacles in the flight path- by building local obstacle maps and solving for confli ct-free trajectory using model predictive control (MPC) framework. For obstacle sensing, an onboard laser scanner is used to detect nearby objects around the vehicle. An MPC algorithm with a cost function that penalizes the proximity to the nearest obstacle replans the fligh t path in real-time. The adjusted trajectory is sent to the position tracking layer in the UAV a vionics. The proposed approach is implemented on Berkeley rotorcraft UAVs and successfully tested in a series of flights in urban obstacle setup.

Conflict-free navigation in unknown urban environments

This paper presents an autonomous exploration method in an unknown environment that uses model predictive control (MPC)-based obstacle avoidance with local map building by onboard sensing. An onboard laser scanner is used to build an online map of obstacles around the vehicle with outstanding accuracy. This local map is combined with a real-time MPC algorithm that generates a safe vehicle path, using a cost function that penalizes the proximity to the nearest obstacle. The adjusted trajectory is then sent to a position tracking layer in the hierarchical unmanned aerial vehicle (UAV) avionics architecture. In a series of experiments using a Berkeley UAV, the proposed approach successfully guided the vehicle safely through the urban canyon

Autonomous Vision-based Landing and Terrain Mapping Using an MPC-controlled Unmanned Rotorcraft

In this paper, we present a vision-based terrain mapping and analysis system, and a model predictive control (MPC)-based flight control system, for autonomous landing of a helicopter-based unmanned aerial vehicle (UAV) in unknown terrain. The vision system is centered around Geyer et al.s recursive multi-frame planar parallax algorithm (2006), which accurately estimates 3D structure using geo-referenced images from a single camera, as well as a modular and efficient mapping and terrain analysis module. The vision system determines the best trajectory to cover large areas of terrain or to perform closer inspection of potential landing sites, and the flight control system guides the vehicle through the requested flight pattern by tracking the reference trajectory as computed by a real-time MPC-based optimization. This trajectory layer, which uses a constrained system model, provides an abstraction between the vision system and the vehicle. Both vision and flight control results are given from flight tests with an electric UAV.

A hierarchical approach to probabilistic pursuit-evasion games with unmanned ground and aerial vehicles

We consider the problem of having a team of unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV) pursue a team of evaders while concurrently building a map in an unknown environment. We cast this problem in a probabilistic game-theoretic framework and consider two computationally feasible pursuit policies: greedy and global-max. We implement this scenario on a fleet of UGVs and UAVs by using a distributed hierarchical system architecture. Finally, we present both simulation and experimental results that evaluate the pursuit policies relating expected capture times to the speed and intelligence of the evaders and the sensing capabilities of the pursuers.

Flying robots: modeling, control and decision making

This paper presents a flight management system (FMS) implemented as on-board intelligence for rotorcraft-based unmanned aerial vehicles (RUAVs), in order to gradually refine given abstract mission commands into real-time control signals for each vehicle. A strategy planner uses the probabilistic decision making algorithms to determine suboptimal action at each time step. A graphical interface on ground station enables human intervention. We derive nonlinear dynamics model upon which we design a tracking control layer using nonlinear model predictive control and integrate with a trajectory generator for logistical action planning. The proposed structure has been implemented on Berkeley RUAVs and validated in probabilistic pursuit-evasion games to show the possibility of intelligent flying robots.

A FLIGHT CONTROL SYSTEM FOR AERIAL ROBOTS: ALGORITHMS AND EXPERIMENTS

This paper presents a hierarchical flight control system for unmanned aerial vehicles. The proposed system executes high-level mission objectives by progressively substantiating them into machine-level commands. The acquired information from various sensors is propagated back to the higher layers for reactive decision making. Each vehicle is connected via standardized wireless communication protocol for scalable multi-agent coordination. The proposed system has been successfully implemented on a number of small helicopters and validated in various applications. Results from waypoint navigation, a probabilistic pursuit-evasion game and vision-based target tracking demonstrate the potential of the proposed approach toward intelligent flying robots.