5 papers
Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning
Jan-Hendrik Ewers, David Anderson, Douglas Thomson
Wilderness search and rescue operations are often carried out over vast landscapes. The search efforts, however, must be undertaken in minimum time to maximize the chance of surviv…
Predictive Probability Density Mapping for Search and Rescue Using An Agent-Based Approach with Sparse Data
Jan-Hendrik Ewers, David Anderson, Douglas Thomson
Predicting the location where a lost person could be found is crucial for search and rescue operations with limited resources. To improve the precision and efficiency of these pred…
Deep Reinforcement Learning for Time-Critical Wilderness Search And Rescue Using Drones
Jan-Hendrik Ewers, David Anderson, Douglas Thomson
Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible solution, but optimizing their se…
A Novel Methodology for Autonomous Planetary Exploration Using Multi-Robot Teams
Sarah Swinton, Jan-Hendrik Ewers, Euan McGookin +2
One of the fundamental limiting factors in planetary exploration is the autonomous capabilities of planetary exploration rovers. This study proposes a novel methodology for trustwo…
Enhancing Reinforcement Learning in Sensor Fusion: A Comparative Analysis of Cubature and Sampling-based Integration Methods for Rover Search Planning
Jan-Hendrik Ewers, Sarah Swinton, David Anderson +2
This study investigates the computational speed and accuracy of two numerical integration methods, cubature and sampling-based, for integrating an integrand over a 2D polygon. Usin…