Project Leader Dr J.Ford, School of Electrical Engineering and Computer Science, Queensland University of Technology, j2.ford@qut.edu.au Research Team Assoc Prof D.Campbell, Dr L.Mejias, Dr F.Gonzalez, Dr T.Bruggemann, Dr Y.Liu, Mr J.Kok (CRCSI Scholarship Holder) – Queensland University of

Technology; Mr M.Colman – Ergon Energy

Automated Flight Planning and Control for Precision Aerial Survey with the Enhanced Flight Assist System (eFAS)

Project Participants Universities – Queensland University of Technology through the Australian Research Centre for Aerospace Automation Industry– Ergon Energy, Qld

Cooperative Research Centre for Spatial Information www.crcsi.com.au

Motivation Routine aerial inspection of large powerline networks at low altitude with fixed-wing aircraft is a potentially hazardous task. This motivates improving the safety and efficiency of powerline inspection by automating the planning and control of the aircraft. Since 2009, the Australian Research Centre for Aerospace Automation (ARCAA) has researched and developed flight test proven automated aerial powerline inspection technologies, in CRCSI Project 6.07 [Spatial Information Business Improvement Applications at Ergon Energy], and Project 9.07 [Flight Assist System (FAS) Demonstrator Project]. The CRCSI 4.3.1 enhanced Flight Assist System (eFAS) project extends the 2D aircraft flight path planning, data capture and flight assist concepts developed under CRCSI Project 6.07 and Project 9.07 to an active 3D capability. This enables automated and optimized flight management and planning for large scale infrastructure aerial surveys. This work also makes a step towards fully autonomous aerial survey with UAVs where a complete 3D planning and automated flight capability is required. To illustrate, Fig. 1 below shows inspection flight path lines in 3D space above a digital terrain model, which the survey aircraft might need to fly to capture the desired ground features with an onboard camera/LiDAR. The problem is how to automatically route plan and fly each flight line in a cost-efficient way, whilst meeting the data capture requirements. Determination of the optimal 3D path requires consideration of the aircraft’s 3D dynamics and the additional constraints due to a downward pointing camera/LiDAR. Such additional constraints typically include minimizing platform attitude motion and keeping the aircraft within certain speed and altitude operating ranges.

Fig 1. 3D flight lines (black lines) above terrain for powerline inspection.

Optimized 3D Flight Plans Given a spatial dataset for the required flight lines, the optimal path to fly from one inspection line to the next can be determined by knowledge of the aircraft dynamic and kinematic constraints. Following this, a route optimization process creates optimized flight paths for more efficient aerial survey, also accounting for the kinematic and dynamic constraints of the aircraft with the 3D inspection flight lines. Due to the difficulty of the problem, a globally optimal solution cannot be guaranteed, however the solution quality is guaranteed to improve with computation time (see Fig. 2 ). For this task, biologically inspired metaheuristic algorithms have been adapted for 3D inspection route planning of powerlines and Fig. 3 shows the outcome of a route planning process for a smaller representative set of flight lines.

Fig 2. Route planning improves with computation time, where it is guaranteed to approach, but not necessarily find, a globally optimal solution. In this case, after 2.5 minutes computation time (MATLAB on PC), a 3D flight length of 118 km was found.

Fig 5. Automatic controlled flight path (red line) over 3D planned flight path (black line). Tested in full aerodynamic simulation environment and in experimental flight test with Cessna 172 aircraft.

Automated 3D Flight After the creation of optimized 3D flight plans, these can be flown under manual (human pilot), semi-autonomous (2D autopilot and human pilot) or fully autonomous (full 3D autopilot) control with manned aircraft or UAVs. Fig 4. shows a prototype display for 3D control of a Cessna 172 aircraft. The planned 3D flight paths with altitude, speed, pitch and heading commands are displayed in real-time to the pilot. For semi-autonomous control the pilot can control vertical dynamics using the display whilst the autopilot controls horizontal dynamics automatically. Alternatively, speed, pitch, heading and altitude commands may be transmitted in real-time to a 3D autopilot for completely autonomous (hands free) control to fly the planned 3D paths.

Fig 3. 118 km long planned 3D path (red line) over smaller representative set of inspection flight lines (black lines). This is a result of an optimized route planning process.

The optimised 3D flight plans could be used in pre-flight operational planning, or onboard manned or unmanned aircraft for manual, semi-autonomous or fully autonomous aerial inspection flight.

Fig 4. Pilot display prototype for 3D flight of planned 3D paths, showing planned flight path, current position, current and required speed, altitude, pitch, and heading indications.

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