Haibo Deng

Tracking ground targets using an autonomous helicopter with a vision system

An autonomous helicopter with a vision system onboard to track ground targets can accomplish very important tasks like inspection and surveillance. The autonomous helicopter target tracking system is a interdisciplinary complex system. A typical autonomous helicopter target tracking system using vision sensors usually includes five major component: a navigation system, a flight controller, a active vision system, a target state estimator and a helicopter guidance law. In this paper, we focus on target state estimator and helicopter guidance law. It is assumed that the target moves on a flat ground, so the relative position between the target and the helicopter can be measured by a monocular vision system. Kalman filter is used to estimate the state of the target based on the vision measurements. A new guidance law inspired by velocity pursuit guidance and car-following control is proposed to guide the helicopter to follow the moving target. The tracking system is tested in a virtual reality environment and the results shows that the performance of the system is satisfied.

Design of an H ∞ loop shaping velocity controller for a small unmanned helicopter

In this paper, a velocity controller based on H∞ loop shaping for an unmanned helicopter is presented. H∞ loop shaping controller guaranteed the stability margin of the closed-loop system above desired level. The performance of the controller is improved using gain-scheduling of key parameters. The simulation results show that the controller is robust and has good performance.

A motion controller for a pan-tilt camera on an autonomous helicopter

In this paper, a motion controller for a pan-tilt camera mounted on an autonomous helicopter is presented. The motion planner is designed according to the kinematics of the pan-tilt camera. However, the solution of the inverse kinematics of the pan-tilt camera is not unique. To deal with this situation, a decision maker is designed. The decision maker makes use of a cost function to decide which solution should be chose. And the error signal is defined as the difference between pan-tilt joint angles and the desired joint angles. The dynamics of the error system is derived, and proportional controllers are designed according to the error dynamics to control the pan and tilt joints respectively. This system is validated in a virtual reality environment, and the simulation results show that this system can track the target successfully.