Object reconstruction and inspection tasks play a crucial role in various robotics applications. Identifying paths that reveal the most unknown areas of the object is paramount in this context, as it directly affects reconstruction efficiency. This problem is known as the view path planning problem. Current methods often use sampling-based path planning techniques, evaluating potential views along the path to enhance reconstruction performance. However, these methods are computationally expensive as they require evaluating several candidate views on the path. To this end, we propose a computationally efficient solution that relies on calculating a focus point in the most informative (unknown) region and having the robot maintain this point in the camera field of view along the path. In this way, object reconstruction-related information is incorporated into the whole-body control of a mobile manipulator employing a visibility constraint without the need for an additional path planner. We conducted comprehensive and realistic simulations using a large dataset of 114 diverse objects of varying sizes from 57 categories to compare our method with a sampling-based planning strategy and a strategy that does not employ informative paths using Bayesian data analysis. Furthermore, to demonstrate the applicability and generality of the proposed approach, we conducted real-world experiments with an 8-DoF omnidirectional mobile manipulator and a legged manipulator. Our results suggest that, when compared to a sampling based strategy, there is no statistically significant difference in object reconstruction entropy, and there is a 52.3% probability that they are practically equivalent in terms of coverage. In contrast, our method is 6.2 to 19.36 times faster in terms of computation time and reduces the total time the robot spends between views by 13.76% to 27.9%, depending on the camera field of view and model resolution. When compared with strategies that do not exploit informative paths, our method improves, on average, coverage by 4.9% and entropy by 9.72% at the expense of spending 8.72% more time in the reconstruction process.

Driven by advancements in Industry 4.0, the Internet of Things (IoT), digital twins (DT), and cyber–physical systems (CPS), there is a growing interest in the digitalizing of asset integrity management. CPS, in particular, is a pivotal technology for the development of intelligent and interconnected systems. The design of a scalable, low-latency communication network with efficient data management is crucial for connecting physical and digital twins in heterogeneous robot fleets. This paper introduces a generalised cyber–physical architecture aimed at governing an autonomous multi-robot ecosystem via a scalable communication network. The objective is to ensure accurate and near-real-time perception of the remote environment by digital twins during robot missions. Our approach integrates techniques such as downsampling, compression, and dynamic bandwidth management to facilitate effective communication and cooperative inspection missions. This allow for efficient bi-directional data exchange between digital and physical twins, thereby enhancing the overall performance of the system. This study contributes to the ongoing research on the deployment of cyber–physical systems for heterogeneous multi-robot fleets in remote inspection missions. The feasibility of the approach has been demonstrated through simulations in a representative environment. In these experiments, a fleet of robots is used to map an unknown building and generate a common 3D probabilistic voxel-grid map, while evaluating and managing bandwidth requirements. This study represents a step forward towards the practical implementation of continuous remote inspection with multi-robot systems through cyber–physical infrastructure. It offers potential improvements in scalability, interoperability, and performance for industrial asset monitoring.

Confined and cluttered aquatic environments present a number of significant challenges with respect to inspection by robotic platforms, including localisation and communications. Some of these can be mitigated by using collaborative heterogeneous multi-robot teams. An important element of such a system is collaborative control. This paper addresses this challenge by presenting an Image-Based Visual Servoing (IBVS), leader-follower control system for heterogeneous aquatic robots. Experiments were conducted in an uncluttered pond to demonstrate the capabilities of the system. The results show robots can maintain tracking each other with maximum$x$and$y$displacements of 0.42 m and 0.41 m, the maximum projection distance in the xy-plane of maintaining formation is 0.45 m, showing the stability and feasibility of deploying such system on underwater platforms.

Vision-based perception has become prevalent in robotic applications, especially in those where the control loop relies on visual data, such as visual servoing. For those applications, ensuring that the features or target object remain visible to the camera is critical, necessitating visibility-aware control. In this paper, we propose a method to guarantee the visibility of a dynamic object using a constrained kinematic controller and Vector Field Inequalities (VFIs) to include a linear visibility constraint. Unlike existing methods, we introduce constraints into the kinematic controller to ensure the target's visibility without needing a trajectory optimizer or local planner. Our method maintains the target object in the camera field of view (FoV) by representing the FoV with four infinite planes and maintaining the distance between the target object and each plane higher than a predefined distance. We evaluated the proposed approach using a mobile manipulator in two simulations involving cluttered environments: the first scenario involves a stationary target object, whereas the second scenario presents a more challenging workspace involving a moving target. Our results demonstrate that the proposed approach successfully maintains the target within the FoV while avoiding obstacles in the workspace, showing the potential of our method to improve the safety and reliability of visual-servoing-based robotic systems.

The development of mobile robot platforms for inspection has gained traction in recent years. However, conventional mobile robots are unable to address the challenge of operating in extreme environments where the robot is required to traverse narrow gaps in highly cluttered areas with restricted access, typically through narrow ports. This article presents MIRRAX, a robot designed to meet these challenges by way of its reconfigurable capability. Controllers for the robot are detailed, along with an analysis on the controllability of the robot given the use of mecanum wheels in a variable configuration. Characterization on the robot's performance identified suitable configurations for operating in narrow environments. The experimental validation of the robot's controllability shows good agreement with the theoretical analysis and the capability to address the challenges of accessing entry ports as small as 150-mm diameter, as well as navigating through cluttered environments. This article also presents results from a deployment in a Magnox facility at the Sellafield nuclear site in the U.K.—the first robot to ever do so, for remote inspection and mapping.

Reconfigurable mobile robots are well suited for inspection tasks in legacy nuclear facilities where access is restricted and the environment is often cluttered. A reconfig-urable snake robot, MIRRAX, has previously been developed to investigate such facilities. The joints used for the robot's reconfiguration introduce additional constraints on the robot's control, such as balance, on top of the existing actuator and collision constraints. This paper presents a set-point controller for MIRRAX using vector-field inequalities to enforce hard constraints on the robot's balance, actuator limits, and collision avoidance in a single quadratic programming formulation. The controller has been evaluated in simulation and early experiments in some scenarios. The results show that the controller generates feasible control inputs that enable the robot to retain its balance while moving with less oscillation and operating within the actuation and collision constraints.

Digital twins tend to be on the way of becoming the future of robots, artificial intelligence, and IoT devices, especially in industrial applications. Creating a digital twin solution will help to solve challenges faced in managing the tasks in their operating environment since it enables an integrated solution for the framework that seamlessly connects to their physical counterparts with the latest internet technologies. In this study, we aim to develop a synchronous, situational-aware, bi-directional/multi-directional digital twin platform that allows us to perceive real-time/simultaneous flow of sensory environmental data and remotely operate robots through digital twins for continuous inspection. To achieve this aim, we will investigate the interoperability issues in robot teleoperation with 3D mapping of a remote unknown environment case scenario.

In recent years, the inspection of extreme environments using mobile robots has gained traction, as robots are able to mitigate the risk placed on humans and at times achieve what humans are unable to. In some scenarios, the robot is required to operate in cluttered environments with highly restricted access through 150 mm diameter ports. The MIRRAX robot has been designed to meet these challenges with the capability of reconfiguring itself to both access environments and navigate through tightly spaced obstacles. The joints used for reconfiguration of the robot introduce additional challenges for path planning due to the significant changes that can occur between adjacent poses. This paper presents a global path planner for MIRRAX. A Voronoi diagram is first used to generate a sparse graph to represent the topology of the environment, which allows for fast, coarse path planning. The coarse path is then refined via a heuristic pose fitting routine to ensure that the path is both collision-free and reduce unnecessary joint angle changes. The planner has been evaluated in simulation, demonstrating the feasibility of generating collision-free paths through narrow pathways for a reconfigurable robot.

Explainability is an important factor to drive user trust in the use of neural networks for tasks with material impact. However, most of the work done in this area focuses on image analysis and does not take into account 3D data. We extend the saliency methods that have been shown to work on image data to deal with 3D data. We analyse the features in point clouds and voxel spaces and show that edges and corners in 3D data are deemed as important features while planar surfaces are deemed less important. The approach is model-agnostic and can provide useful information about learnt features. Driven by the insight that 3D data is inherently sparse, we visualise the features learnt by a voxel-based classification network and show that these features are also sparse and can be pruned relatively easily, leading to more efficient neural networks. Our results show that the Voxception-ResNet model can be pruned down to 5% of its parameters with negligible loss in accuracy.

LiDAR provides highly accurate 3D point clouds. However, data needs to be manually labelled in order to provide subsequent useful information. Manual annotation of such data is time consuming, tedious and error prone, and hence in this paper we present three automatic methods for annotating trees in LiDAR data. The first method requires high density point clouds and uses certain LiDAR data attributes for the purpose of tree identification, achieving almost 90% accuracy. The second method uses a voxel-based 3D Convolutional Neural Network on low density LiDAR datasets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelisation process. The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an F_score of 82.1% on the ISPRS benchmark dataset, comparable to the state-of-the-art methods but with increased efficiency.

In this paper, we proposed a pheromone-based aggregation method based on the state-of-the-art BEECLUST algorithm. We investigated the impact of pheromone-based communication on the efficiency of robotic swarms to locate and aggregate at areas with a given cue. In particular, we evaluated the impact of the pheromone evaporation and diffusion on the time required for the swarm to aggregate. In a series of simulated and real-world evaluation trials, we demonstrated that augmenting the BEECLUST method with artificial pheromone resulted in faster aggregation times.

This paper presents the motion planning framework for a hexapod, based on advanced motions, for accessing challenging spaces, namely narrow pathways and large holes, both of which are surrounded by walls. The advanced motions, wall and chimney walking, utilise environment surfaces that are perpendicular to the ground plane to support the robot motion. Such techniques have not yet been studied in the literature. The hierarchical planning framework proposed here is an extension to existing approaches which have only considered ground walking where foothold contacts are confined to the ground plane. During the pre-processing phase of the 2.5D grid map, the motion primitives employed are assessed for each cell and stacked to the graph if valid. The A* algorithm is then used to find a path to the goal position. Following that, the path is post-processed to smoothen the motions and generate a continuous path. Footholds are then selected along the path. The framework has been evaluated in simulation on the custom-designed Corin hexapod. The resulting path enables access to areas that are previously thought to be inaccessible and reduces the travelling distance compared to previous studies.

Characterisation of nuclear environments is critical for long term operation and decommissioning. Laser Induced Breakdown Spectroscopy (LIBS) is an example of a scientific instrument that could be deployed to aid in characterisation of unknown environments. LIBS consists of a high intensity pulsed laser being focussed down onto a target to create a plasma, and optical emission from the plasma is then used to determine elemental composition of unknown materials. For robots deployed with these instruments in extreme environments, mission time can be limited by hazards present such as radiation. Once deployed a robot must be able to collect the best data possible whilst maximising operational runtime. We present a data quality based probabilistic approach to robot pose estimation to maximise data quality, by considering optimum sensor placement whilst avoiding harmful environmental features such as radiation for a fixed-focus laser diagnostic such as LIBS. This approach is able to determine optimum robot poses for arbitrary targets in 3D for arbitrary diagnostic mounting with respect to the robot. The approach is able to avoid obstacles and avoid occlusion of the target by said obstacles. This can be used as part of autonomous investigation and characterisation performed by mobile robots in hazardous environments.