Torsten Merz

Multi-Objective Four-Dimensional Vehicle Motion Planning in Large Dynamic Environments

This paper presents Multi-Step A* (MSA*), a search algorithm based on A* for multi-objective 4-D vehicle motion planning (three spatial and one time dimensions). The research is principally motivated by the need for offline and online motion planning for autonomous unmanned aerial vehicles (UAVs). For UAVs operating in large dynamic uncertain 4-D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and a grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles, and the rules of the air. It is shown that MSA* finds a cost optimal solution using variable length, angle, and velocity trajectory segments. These segments are approximated with a grid-based cell sequence that provides an inherent tolerance to uncertainty. The computational efficiency is achieved by using variable successor operators to create a multiresolution memory-efficient lattice sampling structure. The simulation studies on the UAV flight planning problem show that MSA* meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of a vector neighborhood-based A*.

Beyond visual range obstacle avoidance and infrastructure inspection by an autonomous helicopter

This paper demonstrates the feasibility of accomplishing real-world inspection tasks beyond visual range with an autonomous helicopter using simple but effective methods. We propose a LIDAR-based perception and guidance system that enables a helicopter to perform obstacle detection and avoidance, terrain following, and close-range inspection. The system has been implemented on board the CSIRO unmanned helicopter and flight tested in a number of different mission scenarios in unknown environments. We have successfully completed 37 missions and recorded more than 14 hours of autonomous flight time. Missions beyond visual range were executed without a backup pilot. The system has achieved a high success rate and has proven to be dependable.

On-board multi-objective mission planning for Unmanned Aerial Vehicles

A system for automated mission planning is presented with a view to operate Unmanned Aerial Vehicles (UAVs) in the National Airspace System (NAS). This paper describes methods for modelling decision variables, for enroute flight planning under Visual Flight Rules (VFR). For demonstration purposes, the task of delivering a medical package to a remote location was chosen. Decision variables include fuel consumption, flight time, wind and weather conditions, terrain elevation, airspace classification and the flight trajectories of other aircraft. The decision variables are transformed, using a Multi-Criteria Decision Making (MCDM) cost function, into a single cost value for a grid-based search algorithm (e.g. A*). It is shown that the proposed system provides a means for fast, autonomous generation of near-optimal flight plans, which in turn are a key enabler in the operation of UAVs in the NAS.

Dependable Low-altitude Obstacle Avoidance for Robotic Helicopters Operating in Rural Areas

This paper presents a system enabling robotic helicopters to fly safely without user interaction at low altitude over unknown terrain with static obstacles. The system includes a novel reactive behavior-based method that guides rotorcraft reliably to specified locations in sparsely occupied environments. System dependability is, among other things, achieved by utilizing proven system components in a component-based design and incorporating safety margins and safety modes. Obstacle and terrain detection is based on a vertically mounted off-the-shelf two-dimensional LIDAR system. We introduce two flight modes, pirouette descent and waggle cruise, which extend the field of view of the sensor by yawing the aircraft. The two flight modes ensure that all obstacles above a minimum size are detected in the direction of travel. The proposed system is designed for robotic helicopters with velocity and yaw control inputs and a navigation system that provides position, velocity, and attitude information. It is cost effective and can be easily implemented on a variety of helicopters of different sizes. We provide sufficient detail to facilitate the implementation on single-rotor helicopters with a rotor diameter of approximately 1.8 m. The system was extensively flight-tested in different real-world scenarios in Queensland, Australia. The tests included flights beyond visual range without a backup pilot. Experimental results show that it is feasible to perform dependable autonomous flight using simple but effective methods.

A platform for the direct hardware evolution of quadcopter controllers

We describe an experimental platform that uses an evolutionary algorithm to automatically tune the gains of a cascaded PID quadcopter controller. All parameters are tuned simultaneously, few platform assumptions are necessary, and no modeling is required. The platform is able to run back-to-back experiments for over 24 hours without human intervention. In a sample experiment, we apply the system to solve a hovering task - the behaviors generated by an initially-random population of gain vectors are evaluated and gradually improved, with the attainment of high fitness hover controllers reported within 12 hours.