This paper proposes a UAV platform that autonomously detects, hunts, and takes down other small UAVs in GPS-denied environments. The platform detects, tracks, and follows another drone within its sensor range using a pre-trained machine learning model. We collect and generate a 58,647-image dataset and use it to train a Tiny YOLO detection algorithm. This algorithm combined with a simple visual-servoing approach was validated on a physical platform. Our platform was able to successfully track and follow a target drone at an estimated speed of 1.5 m/s. Performance was limited by the detection algorithm’s 77% accuracy in cluttered environments and the frame rate of eight frames per second along with the field of view of the camera.

In this work we aim to mimic the human ability to acquire the intuition to estimate the performance of a design from visual inspection and experience alone. We study the ability of convolutional neural networks to predict static and dynamic properties of cantilever beams directly from their raw cross-section images. Using pixels as the only input, the resulting models learn to predict beam properties such as volume maximum deflection and eigenfrequencies with 4.54% and 1.43% mean average percentage error, respectively, compared with the finite-element analysis (FEA) approach. Training these models does not require prior knowledge of theory or relevant geometric properties, but rather relies solely on simulated or empirical data, thereby making predictions based on ‘experience’ as opposed to theoretical knowledge. Since this approach is over 1000 times faster than FEA, it can be adopted to create surrogate models that could speed up the preliminary optimization studies where numerous consecutive evaluations of similar geometries are required. We suggest that this modelling approach would aid in addressing challenging optimization problems involving complex structures and physical phenomena for which theoretical models are unavailable.

Self-modeling refers to an agent's ability to learn a predictive model of its own behavior. A continuously adapted self-model can serve as an internal simulator, enabling the agent to plan and assess various potential behaviors internally, reducing the need for expensive physical experimentation. Self-models are especially important in legged locomotion, where manual modeling is difficult, reinforcement learning is slow, and physical experimentation is risky. Here, we propose a Quasi-static Self-Modeling framework that focuses on learning a predictive model only of high-level quasi-static dynamics, rather than a continuous model. Experimental results on a 12-degree-of-freedom-legged robot demonstrate improvements over model-free and traditional model-based continuous approaches. Using 80 diverse robot morphologies, we confirm a correlation of R2=0.94 between the improvements rendered by our method and the DoF of the robot, suggesting that as future robots increase in complexity, this approach will become more valuable.