Abstract
This paper presents an unmanned aircraft system (UAS) that uses a probabilistic model for autonomous front-on environmental sensing or photography of a target. The system is based on low-cost and readily-available sensor systems in dynamic environments and with the general intent of improving the capabilities of dynamic waypoint-based navigation systems for a low-cost UAS. The behavioural dynamics of target movement for the design of a Kalman filter and Markov model-based prediction algorithm are included. Geometrical concepts and the Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of a target, thus delivering a new waypoint for autonomous navigation. The results of the application to aerial filming with low-cost UAS are presented, achieving the desired goal of maintained front-on perspective without significant constraint to the route or pace of target movement.
Highlights
Dynamic waypoint navigation capabilities of unmanned aircraft systems (UAS) are considered an important component of UAS use in environmental sensing [1]
Autonomous collision avoidance of either ground-based obstacles or other airspace users is a dynamic navigation technology that has been extensively researched throughout the past decade across a broad range of applications [1,3,4,5]
Autonomous following has seen significant research and development, including the application of a broad range of methodologies, models and analysis techniques to a broad range of target types, behaviours and environments. This class of problem has commercial utility in the area of sports coverage and film production [10], which was evidenced by the use of remotely-piloted aircraft systems (RPAS) for sports coverage at the
Summary
Dynamic waypoint navigation capabilities of unmanned aircraft systems (UAS) are considered an important component of UAS use in environmental sensing [1] This is especially true in operating environments where the obstacles or objects of interest exhibit non-deterministic dynamics [2]. Autonomous following has seen significant research and development, including the application of a broad range of methodologies, models and analysis techniques to a broad range of target types, behaviours and environments This class of problem has commercial utility in the area of sports coverage and film production [10], which was evidenced by the use of remotely-piloted aircraft systems (RPAS) for sports coverage at the. The system is adaptable to function on any set of two-dimensional positional data
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