Euan William McGookin

The exploration of Subsurface Access Points (SAPs), such as lava tubes on the Moon and Mars, has gained significant interest due to their potential as stable environments shielded from surface radiation and temperature extremes. These sites are considered high‐value targets for detecting water and signs of ancient life, and assessing their suitability as habitats for human missions. However, SAP exploration presents significant challenges, including navigating unknown and hazardous terrains, operating in low‐light conditions, and managing limited communication capabilities. Recent advances in high‐resolution imaging, Synthetic Aperture Radar, and other sensing technologies have enabled better identification and characterization of SAPs, providing critical data for potential exploration missions. This review presents a structured critical analysis of the challenges in planetary cave exploration and evaluates the state‐of‐the‐art robotic platforms which offer a cost‐effective and safe alternative to human exploration in hazardous environments, in addition to sensor technologies that aid the understanding of SAPs, such as seismic studies, geological characterization, and biosignature detection. This article emphasizes the advantages of multirobot teams in generating comprehensive data sets and improving mission resilience. By combining the unique capabilities of heterogeneous robotic systems, these teams represent a crucial step toward enabling the exploration of SAPs and advancing our understanding of planetary subsurface environments.

Autonomous planetary rovers require robust path planning over rough 3D terrains, where traditional metrics such as path length, number of nodes, and planning time do not adequately capture path quality. Rapidly Exploring Random Trees (RRT) and its asymptotically optimal variant, RRT*, are widely used sampling-based algorithms for non-holonomic mobile robots but are limited when traversing uneven 3D terrain. This study proposes 3D-RRT*, a simplified, terrain-aware extension of Traversability-Based RRT*, designed to maintain high path quality while reducing planning time. The performance of 3D-RRT* is evaluated using metrics that are both practical and meaningful in the context of planetary rover path planning: path smoothness, path flatness, path length, and planning time. Exploration of a simulated Martian surface demonstrates that 3D-RRT* significantly improves path quality compared to standard RRT and RRT*, achieving smoother, safer, and more efficient routes for planetary rover missions.

One of the fundamental limiting factors in planetary exploration is the autonomous capabilities of planetary exploration rovers. This study proposes a novel methodology for trustworthy autonomous multi-robot teams which incorporates data from multiple sources (HiRISE orbiter imaging, probability distribution maps, and on-board rover sensors) to find efficient exploration routes in Jezero crater. A map is generated, consisting of a 3D terrain model, traversability analysis, and probability distribution map of points of scientific interest. A three-stage mission planner generates an efficient route, which maximises the accumulated probability of identifying points of interest. A 4D RRT* algorithm is used to determine smooth, flat paths, and prioritised planning is used to coordinate a safe set of paths. The above methodology is shown to coordinate safe and efficient rover paths, which ensure the rovers remain within their nominal pitch and roll limits throughout operation.

RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully.

An ultrasonic auger was designed, manufactured, and tested in glass microspheres to investigate force and torque reducing effects upon application of high-powered ultrasonic vibration. It was found that whilst vibration had no effect on overhead force during rotary augering, the torque was reduced by up to 30%, depending on amplitude of vibration.

This work presents the methods used and initial findings of the control of the model for an autonomous trenchless drilling device, with bioinspired worm-like locomotion. The model is validated using Inverse Simulation. The initial control is detailed with data from the simulation and experimental device.

Failure Detection, Isolation and Recovery (FDIR) of autonomous systems working in hazardous conditions is essential. Methods of detection and recovery without intervention are required. This work describes the failure modes currently identified with an autonomous biologically inspired trenchless drilling robotic system. Inverse Simulation is used for detecting failures and is demonstrated on a simulation model of the robotic system. Results from the experiments, show that Inverse Simulation can be used to detect and identify system failures.

A novel approach to quantify the uncertainty associated with any aircraft trajectory prediction based on the application of the Polynomial Chaos (PC) theory is presented. The proposed method relies on univariate polynomial descriptions of the uncertainty sources affecting the trajectory prediction process. Those descriptions are used to build the multivariate polynomial expansions that represent the variability of the aircraft state variables along the predicted trajectory. A case study compares the results obtained by a classical Monte Carlo approach with those generated by applying the so-called arbitrary Polynomial Chaos Expansions (aPCE). The results provided herein lead to conclude that this new methodology can be used to accurately quantify trajectory prediction uncertainty with a very low computational effort, enabling the capability of computing the uncertainty of the individual trajectories of a traffic sample of thousands flights within very short time intervals.