Autonomous search for a diffusive source in an unknown environment

The paper presents an approach to olfactory search for a diffusive emitting source of tracer (e.g. aerosol, gas) in an environment with unknown map of randomly placed and shaped obstacles. The measurements of tracer concentration are sporadic, noisy and without directional information. The search domain is discretised and modelled by a finite two-dimensional lattice. The links is the lattice represent the traversable paths for emitted particles and for the searcher. A missing link in the lattice indicates a blocked paths, due to the walls or obstacles. The searcher must simultaneously estimate the source parameters, the map of the search domain and its own location within the map. The solution is formulated in the sequential Bayesian framework and implemented as a Rao-Blackwellised particle filter with information-driven motion control. The numerical results demonstrate the concept and its performance.Comment: 11 pages, 7 figure

Autonomous Search and Rescue Helicopter System Design

The objective of this project is to design and implement an autonomous search-and-rescue drone system capable of operating on targets positioned within a 30 ft. radius. Targets are located through heat signature, after which the drone performs a controlled approach to retrieve the rescuee and return to the launch point. The Pixhawk 4 autopilot is utilized to run autonomous missions, an IR camera to identify the target through OpenCV, and a winch system to pull the victim to safety. The flight director is designed in MATLAB/Simulink to help guide the drone to the precise location of the target. Simulations of the drone are run using jMAVSim to test software-in-the-loop implementations. At the project’s conclusion, several requirements have been fulfilled, while others remain incomplete due to unforeseen obstacles. Although the drone hardware has been assembled and image detection and UAV communication capabilities on the offboard computer are fully functional, the Simulink flight director component of the rescue loop was not completed and test flights of the drone were unsuccessful. The team faced some challenges, such as global chip shortage, limited budget, time constraints, and weight capacity

Deep Reinforcement Learning for Autonomous Search and Rescue

Unmanned Aerial Vehicles (UAVs) are becoming more prevalent every day. In addition, advances in battery life and electronic sensors have enabled the development of diverse UAV applications outside their original military domain. For example, Search and Rescue (SAR) operations can benefit greatly from modern UAVs since even the simplest commercial models are equipped with high-resolution cameras and the ability to stream video to a computer or portable device. As a result, autonomous unmanned systems (ground, aquatic, and aerial) have recently been employed for such typical SAR tasks as terrain mapping, task observation, and early supply delivery. However, these systems were developed before advances such as Google Deepmind’s breakthrough with the Deep Q-Network (DQN) technology. Therefore, most of them rely heavily on greedy or potential-based heuristics, without the ability to learn. In this research, we present two possible approximations (Partially Observable Markov Decision Processes) for enhancing the performance of autonomous UAVs in SAR by incorporating newly-developed Reinforcement Learning methods. The project utilizes open-source tools such as Microsoft’s state-of-the-art UAV simulator AirSim, and Keras, a machine learning framework that can make use of Google’s popular tensor library called TensorFlow. The main approach investigated in this research is the Deep Q-Network

Autonomous Search and Rescue with Modeling and Simulation and Metrics

Unmanned Aerial Vehicles (UAVs) provide rapid exploration capabilities in search and rescue missions while accepting more risks than human operations. One limitation in that current UAVs are heavily manpower intensive and such manpower demands limit abilities to expand UAV use. In operation, manpower demands in UAVs range from determining tasks, selecting waypoints, manually controlling platforms and sensors, and tasks in between. Often, even a high level of autonomy is possible with human generated objectives and then autonomous resource allocation, routing, and planning. However, manually generating tasks and scenarios is still manpower intensive. To reduce manpower demands and move towards more autonomous operations, the authors develop an adaptive planning system that takes high level goals from a human operator and translates them into situationally relevant tasking. For expository simulation, the authors further describe constructing a scenario around the 2018 Hawaii Puna lava natural disaster

Dual Control for Exploitation and Exploration (DCEE) in Autonomous Search

This paper proposes an optimal autonomous search framework, namely Dual Control for Exploration and Exploitation (DCEE), for a target at unknown location in an unknown environment. Source localisation is to find sources of atmospheric hazardous material release in a partially unknown environment. This paper proposes a control theoretic approach to this autonomous search problem. To cope with an unknown target location, at each step, the target location is estimated by Bayesian inference. Then a control action is taken to minimise the error between future robot position and the hypothesised future estimation of the target location. The latter is generated by hypothesised measurements at the corresponding future robot positions (due to the control action) with the current estimation of the target location as a prior. It shows that this approach can take into account both the error between the next robot position and the estimate of the target location, and the uncertainty of the estimate. This approach is further extended to the case with not only an unknown source location, but also an unknown local environment (e.g. wind speed and direction). Different from current information theoretic approaches, this new control theoretic approach achieves the optimal trade-off between exploitation and exploration in a unknown environment with an unknown target by driving the robot moving towards estimated target location while reducing its estimation uncertainty. This scheme is implemented using particle filtering on a mobile robot. Simulation and experimental studies demonstrate promising performance of the proposed approach. The relationships between the proposed approach, informative path planning, dual control, and classic model predictive control are discussed and compared

A Fully Autonomous Search and Rescue System Using Quadrotor UAV

In order to deal with critical missions a growing interest has been shown to the UAVs design. Flying robots are now used fire protection, surveillance and search & rescue (SAR) operations. In this paper, a fully autonomous system for SAR operations using quadrotor UAV is designed. In order to scan the damaged area, speeds up the searching process and detect any possible survivals a new search strategy that combines the standard search strategies with the probability of detection is developed. Furthermore the autopilot is designed using an optimal backstepping controller and this enables the tracking of the reference path with high accuracy and maximizes the flying time. Finally a comparison between the applied strategies is made using a study case of survivals search operation. The obtained results confirmed the efficiency of the designed system