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Both aerial robotic platforms and additive manufacturing (AM) have become more affordable to consumers. Indoor aerial robotic platforms are typically small and lightweight, while AM is renowned for creating small, high-strength prototypes and components. This paper discusses the material selection and structural optimisation of a 3D-printed indoor aerial robotic platform. Three commonly used AM materials were compared using finite element analysis (FEA): acrylonitrile butadiene styrene (ABS), polyethylene terephthalate glycol (PETG), and Nylon. It was found that Nylon offered the best performance in terms of the strength-to-weight ratio. The aerial robotic frame was optimised using an iterative design approach and previous knowledge with regards to the breaks observed during flight crashes. A dynamic FEA was performed to simulate a drop test from a height of one meter to compare the optimised design with the previous frame design. It was found that the improvements in the redesign have led to a 13.67 % decrease in weight and a 11.78 % decrease stress of the aerial robotic frame. This not only demonstrates the effectiveness of design optimisation, but also highlights the commitment to producing more efficient, reliable and sustainable designs.

Place classification is crucial for a robot's ability to make high- level decisions. When a robot can identify its operating environment, it can provide more appropriate services. This capability is similar to how humans use their understanding of their surroundings to make informed decisions about appropriate actions. Depth data offers valuable spatial information that can enhance place classification on a robot. However, it is more common for mobile robot applications to rely on RGB data rather than RGB-D data for classifying indoor places. This study demonstrates that incorporating depth information improves the classification of indoor places using a mobile robot. Data were collected from a mobile robot, and indoor scenes were classified based on RGB and RGB-D inputs. A comparison was made between the performance of VGG16, Inception v3, and ResNet50 architectures using RGB data alone. Subsequently, depth information was fused with these RGB models. Experiments showed that classification accuracy improved when tested on the mobile robot by including depth data. In the experiment, the robot created a map of the indoor environment and identified four different rooms on the map using the trained models. This demonstrates the enhanced classification capabilities achieved by incorporating depth information.

During the development of an aerial robotic platform, it is necessary to characterise the flight controller to ensure flight stability. Even though this can be done through open-source software like Betaflight, flight testing can result in potential crashes. To prevent this, this paper aims to design and manufacture a test rig to test and characterise the flight controller reliably. A 4-degree-of-freedom (DOF) test rig was developed to allow for vertical motion and characterise the yaw, pitch, and roll angles. It was also entirely constructed utilising additive manufacturing (AM). An iterative design approach was considered to improve the design after practical testing with a micro aerial robotic platform. This approach significantly enhanced the design of the ball joint and linear shaft to assist in better performance of the micro aerial robotic platform when using the test rig.

Inventory management in warehouses is a crucial task in the logistics industry. Manual stocktaking in larger-scale warehouses can be time-consuming and labour-intensive. To automate this process, unmanned aerial vehicles (UAVs) have gained popularity due to their potential to offer a safer, timeous, and more efficient solution. However, deploying drone systems can face challenges and therefore requires planning tasks such as path planning. This study investigates two commonly used UAV flight paths to identify the optimal path within a warehouse: zigzag and up-down flight paths. A Gazebo simulation was considered with a six-rotor UAV model to analyse the different flight paths. The accuracy of both path types is measured for comparison, and flight times were considered as a means for optimisation. The results indicated that the zigzag flight path is the most optimal with the shortest flight time. The study found that the zigzag path resulted in a 27.25% shorter estimated flight time compared to the up-down path.

Spatial awareness is an important competence for a mobile robotic system. A robot needs to localise and perform context interpretation to provide any meaningful service. With the deep learning tools and readily available sensors, visual place recognition is a first step towards identifying the environment to bring a robot closer to spatial awareness. In this paper, we implement place recognition on a mobile robot considering a deep learning approach. For simple place classification, where the task involves classifying images into a limited number of categories, all three architectures; VGG16, Inception-v3 and ResNet50, perform well. However, considering the pros and cons, the choice may depend on available computational resources and deployment constraints.

There is a need to ensure that an Unmanned Aerial Vehicle (UAV) can fly safely in indoor environments. In this paper, the visual-inertial odometry (VIO) implemented in a commercial UAV is tested to determine its reliability to keep its position in confined indoor spaces. The experimental results indicated that the UAV was able to track the subject from an average distance of 3915.23 mm in the y-direction, with RMSEx and RMSEy of 452.47 mm and 332.66 mm, respectively. This shows that the UAV can track objects in confined environments, such as the aisles of an indoor warehouse.

Collaborative robots are adopted in the drive towards Industry 4.0 to automate manufacturing, while retaining a human workforce. This area of research is known as human-robot collaboration (HRC) and focusses on understanding the interactions between the robot and a human. During HRC the robot is often programmed to perform a predefined task, however when working in a dynamic and unstructured environment this is not achievable. To this end, machine learning is commonly employed to train the collaborative robot to autonomously execute a collaborative task. Most of the current research is concerned with HRC, however, when considering the smart factory of the future investigating an autonomous collaborative task between two robots is pertinent. In this paper deep reinforcement learning (DRL) is considered to teach two collaborative robots to handover an object in a simulated environment. The simulation environment was developed using Pybullet and OpenAI gym. Three DRL algorithms and three different reward functions were investigated. The results clearly indicated that PPO is the best performing DRL algorithm as it provided the highest reward output, which is indicative that the robots were learning how to perform the task, even though they were not successful. A discrete reward function with reward shaping, to incentivise the cobot to perform the desired actions and incremental goals (picking up the object, lifting the object and transferring the object), provided the overall best performance.

There is an increased interest in using aerial robotic platforms for indoor applications in industrial and manufacturing environments. One such example is stocktaking in warehouses, where the use of other mobile robotic systems is not efficient as they aren’t able to reach higher shelves or operate at the same speed as an aerial robotic platform. In this paper we discuss a prototype design for an aerial robotic platform that will be able to operate in GPS-denied areas, such as indoor warehouses. Suitable choices for the hardware and avionics are proposed based on the system and user requirements. The final design weighs 194 g, costs R 7 354.3, and has an estimated flight time of 5.5 min which is within the system requirements.

After years of drought, the rainy season is always welcomed. Unfortunately, this can also herald widespread flooding which can result in loss of livelihood, property, and human life. In this study a Bayesian network is used to develop a flood prediction model for a Tshwane catchment area prone to flash floods. This causal model was considered due to a shortage of flood data. The developed Bayesian network was evaluated by environmental domain experts and implemented in Python through pyAgrum. Three what-if scenarios are used to verify the model and estimation of probabilities which were based on expert knowledge. The model was then used to predict a low and high rainfall scenario. It was able to predict no flooding events for a low rainfall scenario, and flooding events, especially around the rivers, for a high rainfall scenario. The model therefore behaves as expected.

Moments of inertia define the amount of torque needed to rotate an object at a defined angular acceleration around a given axis. With the limited information available for multi-rotor unmanned aerial vehicles (UAVs), this paper sets out to experimentally determine the moments of inertia of a hexarotor UAV with a mounted payload. The moments of inertia are calculated in three rotational axes using pendulum and torsion tests. The pendulum test is performed to verify the test setup by experimentally determining a parameter which is already known: gravity. In pitch and yaw orientations gravity could be experimentally calculated to within 1.33% and 1.22% respectively. Unfortunately, this was not the case in the roll orientation, indicating that the test setup in that orientation needs to be adjusted. The torsion test is then performed to determine the period which is used to calculate the moments of inertia. Ultimately, these calculated moments of inertia will be used in simulation from which a flight controller can be designed.

Statistical design of experiments (DoE) aims to develop a near efficient design while minimising the number of experiments required. This is an optimal approach especially when there is a need to investigate multiple variables. DoE is a powerful methodology for a wide range of applications, from the efficient design of manufacturing processes to the accurate evaluation of global optima in numerical studies. The contribution of this paper is to provide a general introduction to statistical design of experiments for a non-expert audience, with the aim of broadening exposure in the applied mechanics community. We focus on response surface methodology (RSM) designs — Taguchi Design, Central Composite Design, Box-Behnken Design and D-optimal Design. These different RSM designs are compared in the context of a case study from the field of polymer composites. The results demonstrate that an exact D-optimal design is generally considered to be a good design when compared to the global D-optimal design. That is, it requires fewer experiments while retaining acceptable efficiency measures for all three response surface models considered. This paper illustrates the benefits of DoE, demonstrates the importance of evaluating different designs, and provides an approach to choose the design best suited for the problem of interest.