Autonomous exploration in unknown environments relies on onboard state estimation for localisation and mapping. Existing exploration methods primarily maximise coverage efficiency, but often overlook that visual-inertial odometry (VIO) performance strongly depends on the availability of robust visual features. Consequently, exploration policies can drive a robot into feature-sparse regions where tracking degrades, leading to odometry drift, corrupted maps, and mission failure. We propose a hierarchical perception-aware exploration framework for a stereo-equipped uncrewed aerial vehicle (UAV) that explicitly incorporates perceptual quality into frontier selection and yaw planning during exploration. Our approach (i) associates each candidate frontier with an expected feature quality using a global feature map, prioritising visually informative subgoals, and (ii) optimises a continuous yaw trajectory along the planned path to maintain stable feature tracks. We evaluate our method in simulation across environments with varying texture levels and in real-world indoor experiments with largely textureless walls. Compared to baselines that ignore feature quality and/or omit continuous yaw optimisation, our method achieves more reliable feature tracking, reduced odometry drift, and up to 48% higher coverage before odometry error exceeds specified thresholds. ... Read more

Designing limbed robots is a complex, multidisciplinary task that typically requires substantial effort from experienced engineers. In this paper, we present a novel automatic robot design framework based on Decomposition-Optimization-Assembling (DOA) to address this challenge. Our framework enables non-experts to create personalized limbed robot designs from media inputs, such as text and images, within minutes to a few hours. Our system leverages recent advances in generative AI and 3D printing to produce designs that match the descriptions provided in the input media. The output consists of selected motors and 3D-printable mechanical components that can be assembled into a limbed robot. To handle the large design space and intricate details in fabrication and assembly, we formulate and solve a series of optimization problems involving actuators, geometry, and structural density. We validate the proposed system by designing and fabricating a Centaur robot based on an image input. Furthermore, we demonstrate the system’s versatility and effectiveness through the generation of a wide variety of limbed robot designs. ... Read more

Battery-powered multirotor unmanned aerial vehicles (UAVs) can rapidly map unknown environments, but mission performance is often limited by energy rather than geometry alone. Standard exploration policies that optimise for coverage or time can therefore waste energy through manoeuvre-heavy trajectories. In this paper, we address energy-aware autonomous 3D exploration for multirotor UAVs in initially unknown environments. We propose Energy-Aware Autonomous Exploration (EAAE), a modular frontier-based framework that makes energy an explicit decision variable during frontier selection. EAAE clusters frontiers into view-consistent regions, plans dynamically feasible candidate trajectories to the most informative clusters, and predicts their execution energy using an offline power estimation loop. The next target is then selected by minimising predicted trajectory energy while preserving exploration progress through a dual-layer planning architecture for safe execution. We evaluate EAAE in a full exploration pipeline with a rotor-speed-based power model across simulated 3D environments of increasing complexity. Compared to representative distance-based and information gain-based frontier baselines, EAAE consistently reduces total energy consumption while maintaining competitive exploration time and comparable map quality, providing a practical drop-in energy-aware layer for frontier exploration. ... Read more