Learn More
In this paper we develop a probabilistic framework for pursuit-evasion games. We propose a “greedy” policy to control a swarm of autonomous agents in the pursuit of one or several evaders. At each instant of time this policy directs the pursuers to the locations that maximize the probability of finding an evader at that particular time instant. It is shown(More)
Autonomous helicopter flight represents a challenging control problem, with complex, noisy, dynamics. In this paper, we describe a successful application of reinforcement learning to autonomous helicopter flight. We first fit a stochastic, nonlinear model of the helicopter dynamics. We then use the model to learn to hover in place, and to fly a number of(More)
We consider the problem of having a team of Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV) pursue a second team of evaders while concurrently building a map in an unknown environment. We cast the problem in a probabilistic game theoretic framework and consider two computationally feasible greedy pursuit policies: local-max and global-max.(More)
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(More)
Recently, laparoscopic colorectal surgery using a single incision usually made at the umbilical area has emerged as a tool to minimize the numbers of scars and provide better cosmetic results. But experience in laparoscopic skills is needed to maintain the oncologic principles of colorectal cancer surgery with the restricted operating field during the(More)
This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input(More)
Learning algorithms have enjoyed numerous successes in robotic control tasks. In problems with time-varying dynamics, online learning methods have also proved to be a powerful tool for automatically tracking and/or adapting to the changing circumstances. However, for safety-critical applications such as airplane flight, the adoption of these algorithms has(More)
This article presents a survey on publicly available open-source projects (OSPs) on quadrotor unmanned aerial vehicles (UAVs). Recently, there has been increasing interest in quadrotor UAVs. Exciting videos have been published on the Internet by many research groups and have attracted much attention from the public [1][7]. Relatively simple structures of(More)
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(More)
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.(More)