Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

Decomposition-based mission planning for fixed-wing UAVs surveying in wind

This paper presents a new method for planning fixed-wing aerial survey paths that ensures efficient image coverage of a large complex agricultural field in the presence of wind. By decomposing any complex polygonal field into multiple convex polygons, the traditional back-and-forth boustrophedon paths can be used to ensure coverage of these decomposed regions. To decompose a complex field in an efficient and fast manner, a top-down recursive greedy approach is used to traverse the search space in order to minimise flight time of the survey. This optimisation can be computed fast enough for use in the field. As wind can severely affect flight time, it is included in the flight time calculation in a systematic way using a verified cost function that offer greatly reduced survey times in wind. Other improved cost functions have been developed to take into account real world problems, e.g. No Fly Zones, in addition to flight time. A number of real surveys are performed in order to show the flight time in wind model is accurate, to make further comparisons to previous techniques and to show that the proposed method works in real-world conditions providing total image coverage. A number of missions are generated and flown for real complex agricultural fields. In addition to this, the wind field around a survey area is measured from a multi-rotor carrying an ultrasonic wind speed sensor. This shows that the assumption of steady uniform wind holds true for the small areas and time scales of a Unmanned Aerial Vehicle (UAV) aerial survey.</div

Biologically Inspired Intelligence with Applications on Robot Navigation

Biologically inspired intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society and this trend will continue with biologically inspired neural network techniques. In this chapter, multiple robots cooperate to achieve a common coverage goal efficiently, which can improve the work capacity, share the coverage tasks, and reduce the completion time by a biologically inspired intelligence technique, is addressed. In many real-world applications, the coverage task has to be completed without any prior knowledge of the environment. In this chapter, a neural dynamics approach is proposed for complete area coverage by multiple robots. A bio-inspired neural network is designed to model the dynamic environment and to guide a team of robots for the coverage task. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each mobile robot treats the other robots as moving obstacles. Each robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot position. The proposed model algorithm is computationally simple. The feasibility is validated by four simulation studies

Optimal polygon decomposition for UAV survey coverage path planning in wind

In this paper, a new method for planning coverage paths for fixed-wing Unmanned Aerial Vehicle (UAV) aerial surveys is proposed. Instead of the more generic coverage path planning techniques presented in previous literature, this method specifically concentrates on decreasing flight time of fixed-wing aircraft surveys. This is achieved threefold: by the addition of wind to the survey flight time model, accounting for the fact fixed-wing aircraft are not constrained to flight within the polygon of the region of interest, and an intelligent method for decomposing the region into convex polygons conducive to quick flight times. It is shown that wind can make a huge difference to survey time, and that flying perpendicular can confer a flight time advantage. Small UAVs, which have very slow airspeeds, can very easily be flying in wind, which is 50% of their airspeed. This is why the technique is shown to be so effective, due to the fact that ignoring wind for small, slow, fixed-wing aircraft is a considerable oversight. Comparing this method to previous techniques using a Monte Carlo simulation on randomised polygons shows a significant reduction in flight time