8 accepted papers
Robotic soil manipulation is essential for automated farming, particularly in excavation and levelling tasks. However, the nonlinear dynamics of granular materials challenge traditional control methods, limiting stability and efficiency. We propose Celebi, a causality-enhanced optimisation method th
Rolling contact kinematics plays a vital role in dexterous manipulation and rolling-based locomotion. Yet, in practical applications, the environments and objects involved are often captured as discrete point clouds, creating substantial difficulties for traditional motion control and planning frame
The curve skeleton is known to geometric modelling and computer graphics communities as one of the shape descriptors which intuitively indicates the topological properties of the objects. In recent years, studies have also suggested the potential of applying curve skeletons to assist robotic reasoni
Long-horizon robotic manipulation tasks typically involve a series of interrelated sub-tasks spanning multiple execution stages. Skill chaining offers a feasible solution for these tasks by pre-training the skills for each sub-task and linking them sequentially. However, imperfections in skill learn…
This work is focused on reinforcement learning (RL)-based navigation for drones, whose localisation is based on visual odometry (VO). Such drones should avoid flying into areas with poor visual features, as this can lead to deteriorated localization or complete loss of tracking. To achieve this, we…
This paper introduces a novel deep learning approach to semantic segmentation of the shoreline environments with a high frames-per-second (fps) performance, making the approach readily applicable to autonomous navigation for Unmanned Surface Vehicles (USV). The proposed ShorelineNet is an efficient…
This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative MultiRobot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex vari
Special high-end sensors with expensive hardware are usually needed to measure shock signals with high accuracy. In this paper, we show that cheap low-end sensors calibrated by deep neural networks are also capable to measure high-g shocks accurately. Firstly we perform drop shock tests to collect a…