Sound is one of a human beings most important senses. After vision, it is the sense most used to gather information about the environment. Despite this, comparatively little research has been done into the field of sound recognition. The research that has been done mainly centres around the recognition of speech and music. Our auditory environment is made up of many sounds other than speech and music. This sound information can be taped into for the benefit of specific applications such as security systems. Currently, most researchers are ignoring this sound information. This thesis investigates techniques to recognise environmental non-speech sounds and their direction, with the purpose of using these techniques in an autonomous mobile surveillance robot. It also presents advanced methods to improve the accuracy and efficiency of these techniques. Initially, this report presents an extensive literature survey, looking at the few existing techniques for non-speech environmental sound recognition. This survey also, by necessity, investigates existing techniques used for sound recognition in speech and music. It also examines techniques used for direction detection of sounds. The techniques that have been identified are then comprehensively compared to determine the most appropriate techniques for non-speech sound recognition. A comprehensive comparison is performed using non-speech sounds and several runs are performed to ensure accuracy. These techniques are then ranked based on their effectiveness. The best technique is found to be either Continuous Wavelet Transform feature extraction with Dynamic Time Warping or Mel-Frequency Cepstral Coefficients with Dynamic Time Warping. Both of these techniques achieve a 70% recognition rate. Once the best of the existing classification techniques is identified, the problem of uncountable sounds in the environment can be addressed. Unlike speech recognition, non-speech sound recognition requires recognition from a much wider library of sounds. Due to this near-infinite set of example sounds, the characteristics and complexity of non-speech sound recognition techniques increases. To address this problem, a systematic scheme needs to be developed for non-speech sound classification. Several different approaches are examined. Included is a new design for an environmental sound taxonomy based on an environmental sound alphabet. This taxonomy works over three levels and classifies sounds based on their physical characteristics. Its performance is compared with a technique that generates a structured tree automatically. These structured techniques are compared for different data sets and results are analysed. Comparable results are achieved for these techniques with the same data set as previously used. In addition, the results and greater information from these experiments is used to infer some information about the structure of environmental sounds in general. Finally, conclusions are drawn on both sets of techniques and areas of future research stemming from this thesis are explored.

The interest in autonomous surveillance has increased due to advances in autonomous systems and sensor theory. This thesis is a preliminary study of the cooperation between UGVs and stationary sensors when monitoring a dedicated area. The primary focus is the path planning of a UGV for different initial intrusion alarms. Cell decomposition, i.e., spatial partitioning, of the area of surveillance was utilized, and the objective function is based on the probability of a present intruder in each cell. These probabilities were modeled through two different methods: ExpPlanner, utilizing an exponential decay function. Markov planner, utilizing a Markov chain to propagate the probabilities. The performance of both methods improves when a confident alarm system is utilized. By prioritizing the direction of the planned paths, the performances improved further. The Markov planner outperforms the ExpPlanner in finding a randomly walking intruder. The ExpPlanner is suitable for passive surveillance, and the Markov planner is suitable for ”aggressive target hunting”.

The adoption of small-size Unmanned Aerial Vehicles (UAVs) in the commercial and professional sectors is rapidly growing. The miniaturisation of sensors and processors, the advancements in connected edge intelligence and the exponential interest in Artificial Intelligence (AI) are boosting the affirmation of autonomous nano-size drones in the Internet of Things (IoT) ecosystem. However, achieving safe autonomous navigation and high-level tasks like exploration and surveillance with these tiny platforms is extremely challenging due to their limited resources. Lightweight and reliable solutions to this challenge are subject to ongoing research. This work focuses on enabling the autonomous flight of a pocket-size, 30-gram platform called Crazyflie in a partially known environment. We implement a modular pipeline for the safe navigation of the nano drone between waypoints. In particular, we propose an AI-aided, vision-based reactive planning method for obstacle avoidance. We deal with the constraints of the nano drone by splitting the navigation task into two parts: a deep learning-based object detector runs on external hardware while the planning algorithm is executed onboard. For designing the reactive approach, we take inspiration from existing sensorbased navigation solutions and obtain a novel method for obstacle avoidance that does not rely on distance information. In the study, we also analyse the communication aspect and the latencies involved in edge offloading. Moreover, we share insights into the finetuning of an SSD MobileNet V2 object detector on a custom dataset of low-resolution, grayscale images acquired with the drone. The results show the ability to command the drone at ∼ 8 FPS and a model performance reaching a COCO mAP of 60.8. Field experiments demonstrate the feasibility of the solution with the drone flying at a top speed of 1 m/s while steering away from an obstacle placed in an unknown position and reaching the target destination. Additionally, we study the impact of a parameter determining the strength of the avoidance action and its influence on total path length, traversal time and task completion. The outcome demonstrates the compatibility of the communication delay and the model performance with the requirements of the real-time navigation task and a successful obstacle avoidance rate reaching 100% in the best-case scenario. By exploiting the modularity of the proposed working pipeline, future work could target the improvement of the single parts and aim at a fully onboard implementation of the navigation task, pushing the boundaries of autonomous exploration with nano drones. / Användningen av små obemannade flygfarkoster (UAV) inom den kommersiella och professionella sektorn ökar snabbt. Miniatyriseringen av sensorer och processorer, framstegen inom connected edge intelligence och det exponentiella intresset för artificiell intelligens (AI) ökar användningen av autonoma drönare i nanostorlek i ekosystemet för sakernas internet (IoT). Att uppnå säker autonom navigering och uppgifter på hög nivå, som utforskning och övervakning, med dessa små plattformar är dock extremt utmanande på grund av deras begränsade resurser. Lättviktiga och tillförlitliga lösningar på denna utmaning är föremål för pågående forskning. Detta arbete fokuserar på att möjliggöra autonom flygning av en 30-grams plattform i fickformat som kallas Crazyflie i en delvis känd miljö. Vi implementerar en modulär pipeline för säker navigering av nanodrönaren mellan riktpunkter. I synnerhet föreslår vi en AI-assisterad, visionsbaserad reaktiv planeringsmetod för att undvika hinder. Vi hanterar nanodrönarens begränsningar genom att dela upp navigeringsuppgiften i två delar: en djupinlärningsbaserad objektdetektor körs på extern hårdvara medan planeringsalgoritmen exekveras ombord. För att utforma den reaktiva metoden hämtar vi inspiration från befintliga sensorbaserade navigeringslösningar och tar fram en ny metod för hinderundvikande som inte är beroende av avståndsinformation. I studien analyserar vi även kommunikationsaspekten och de svarstider som är involverade i edge offloading. Dessutom delar vi med oss av insikter om finjusteringen av en SSD MobileNet V2-objektdetektor på en skräddarsydd dataset av lågupplösta gråskalebilder som tagits med drönaren. Resultaten visar förmågan att styra drönaren med ∼ 8 FPS och en modellprestanda som når en COCO mAP på 60.8. Fältexperiment visar att lösningen är genomförbar med drönaren som flyger med en topphastighet på 1 m/s samtidigt som den styr bort från ett hinder som placerats i en okänd position och når måldestinationen. Vi studerar även effekten av en parameter som bestämmer styrkan i undvikandeåtgärden och dess påverkan på den totala väglängden, tidsåtgången och slutförandet av uppgiften. Resultatet visar att kommunikationsfördröjningen och modellens prestanda är kompatibla med kraven för realtidsnavigering och ett lyckat undvikande av hinder som i bästa fall uppgår till 100%. Genom att utnyttja modulariteten i den föreslagna arbetspipelinen kan framtida arbete inriktas på förbättring av de enskilda delarna och syfta till en helt inbyggd implementering av navigeringsuppgiften, vilket flyttar gränserna för autonom utforskning med nano-drönare.