Ensuring the safety and efficiency of Autonomous Vehicles (AVs) necessitates highly accurate perception, especially for lane detection and lane-change manoeuvres. Among object detection frameworks, “You Only Look Once” (YOLO) algorithms have emerged as prominent contenders due to their rapid inference and commendable accuracy. However, the broad spectrum of YOLO variants and their applications in complex, real-world environments remain insufficiently mapped, necessitating a more integrative and critical perspective than what is typically offered by surveys. This comprehensive review synthesizes theoretical foundations, architectural innovations, and empirical evaluations of YOLO-based algorithms in AV-related tasks. It not only highlights key findings—such as the notable gains in real-time detection and adaptability to a range of driving conditions—but also explicitly identifies persistent gaps and limitations. These include difficulties in detecting subtle or degraded lane markings, handling unpredictable environmental factors like adverse weather and varied lighting, mitigating adversarial perturbations, and scaling effectively across diverse datasets and geographic regions. By critically examining these vulnerabilities, we illuminate the opportunities for refining YOLO's training paradigms, optimizing model architectures, incorporating sensor fusion, and fostering universally applicable datasets. The implications of addressing these gaps extend beyond mere technical refinements. Proactively tackling YOLO's current challenges can expedite the realization of safer, more robust, and globally adaptable AV navigation systems. In doing so, this review provides clear, actionable insights for researchers, engineers, and policymakers, guiding them toward strategic innovations that will strengthen AV perception and contribute to more reliable, future-ready transportation solutions.

Within the Multi-Platform Inspection, Maintenance and Repair in Extreme Environments (MIMRee) project, a lightweight and multifunctional robotic repair arm is created for wind turbine blades. The design features a toolbox at the base of the arm housing multiple end-effector tools and an autonomous end-effector tool-changer. The arm communicates commands and data via internet with a bespoke user interface enabling human-in-the-loop operation and overriding of autonomous repair actions. This paper outlines our approach in design, development, testing and control of the robotic repair system. The functionalities of the arm include cleaning, sanding, and filler material deposition and forming, each using a bespoke end-effector tool closely replicating the relevant manual repair process. The experimental results confirm the effectiveness of our approach indicating a maximum end-effector position error of 3 mm, a maximum tool switching time of 8 seconds, and a maximum arm’s weight of 1.8 kg. This presents around 84% weight reduction compared with existing technologies used for the same purpose. Our standalone design enables modular integration into a wide range of mobile platform types used in industrial operations.

This paper proposes a novel adaptive anchoring module that can be integrated into robots to enhance their mobility and manipulation abilities. The module can deploy a suitable mode of attachment, via spines or vacuum suction, to different contact surfaces in response to the textural properties of the surfaces. In order to make a decision on the suitable mode of attachment, an original dataset of 100 images of outdoor and indoor surfaces was enhanced using a WGAN-GP to generate an additional 200 synthetic images. The enhanced dataset was then used to train a visual surface examination model using Faster RCNN. The addition of synthetic images increased the mean average precision of the Faster R-CNN model from 81.6% to 93.9%. We have also conducted a series of load tests to characterize the module’s strength of attachments. The results of the experiments indicate that the anchoring module can withstand an applied detachment force of around 22N and 20N when attached using spines and vacuum suction on the ideal surfaces, respectively.

This paper proposes a bioinspired adaptive anchoring module that can be integrated into robots to enhance their mobility and manipulation abilities. The design of the module is inspired by the structure of the mouth in Chilean lamprey (Mordacia lapicida) where a combination of suction and several arrays of teeth with different sizes around the mouth opening is used for catching preys and anchoring onto them. The module can deploy a suitable mode of attachment, via teeth or vacuum suction, to different contact surfaces in response to the textural properties of those surfaces. In order to make a decision on the suitable mode of attachment, an original dataset of 500 images of outdoor and indoor surfaces was used to train a visual surface examination model using YOLOv3; a virtually real-time object detection algorithm. The mean average precision of the trained model was calculated to be 91%. We have conducted a series of pull-out tests to characterize the module's strength of attachments. The results of the experiments indicate that the anchoring module can withstand an applied detachment force of up to 70N and 30N when attached using teeth and vacuum suction, respectively.

This paper presents a highly compact fibre-optic system based on light intensity modulation for multi-segment flex sensing in pliable robot arms, e.g., articulated surgical instruments. This fibre-optic arrangement is 7.5 mm in diameter and is comprised of a two-segment flexible and stretchable Steiner chain arm section with twelve housings at the distal side which accommodates passive cables. The displacement of each cable will be used to determine the bending. This Steiner chain section is followed by a basal rigid fibre-optic sensing unit integrated with a low-friction retractable distance modulation array which couples the motion of the passive cables with light-emitting optical fibres. The low-friction retractable distance modulation array uses steel spring-needle double sliders to reduce the hysteresis and to recover reference sensor values when the arm returns to its original straight configuration. The U-shape loopback design of the optical fibres allows integration of all electronics away from the sensing site. The experimental results indicate a maximum bending angle error of 6° in one individual segment of the two-segment arm with respect to reference angle values calculated from camera images.

This paper presents an innovative approach of utilising visual feedback to determine physical interaction forces with soft tissue during Minimally Invasive Surgery (MIS). This novel force sensing device is composed of a linear retractable mechanism and a spherical visual feature. The sensor mechanism can be adapted to endoscopic cameras used in MIS. As the distance between the camera and feature varies due to the sliding joint, interaction forces with anatomical surfaces can be computed based on the visual appearance of the feature in the image. Hence, this device allows the measurement of forces without introducing new stand-alone sensors. A mathematical model was derived based on validation data tests and preliminary experiments were conducted to verify the model's accuracy. Experimental results confirm the effectiveness of our vision based approach.

This paper introduces an optical based three axis force sensor which can be integrated with the robot arm of the EU project STIFF-FLOP (STIFFness controllable Flexible and Learnable Manipulator for Surgical Operations) in order to measure applied external forces. The structure of the STIFF-FLOP arm is free of metal components and electric circuits and, hence, is inherently safe near patients during surgical operations. In addition, this feature makes the performance of this sensing system immune against strong magnetic fields inside magnetic resonance (MR) imaging scanners. The hollow structure of the sensor allows the implementation of distributed actuation and sensing along the body of the manipulator. In this paper, we describe the design and calibration procedure of the proposed three axis optics-based force sensor. The experimental results confirm the effectiveness of our optical sensing approach and its applicability to determine the force and momentum components during the physical interaction of the robot arm with its environment.

Robotic manipulators for Robot-assisted Minimally Invasive Surgery (RMIS) pass through small incisions into the patient's body and interact with soft internal organs. The performance of traditional robotic manipulators such as the da Vinci Robotic System is limited due to insufficient flexibility of the manipulator and lack of haptic feedback. Modern surgical manipulators have taken inspiration from biology e.g. snakes or the octopus. In order for such soft and flexible arms to reconfigure itself and to control its pose with respect to organs as well as to provide haptic feedback to the surgeon, tactile sensors can be integrated with the robot's flexible structure. The work presented here takes inspiration from another area of biology: cucumber tendrils have shown to be ideal tactile sensors for the plant that they are associated with providing useful environmental information during the plant's growth. Incorporating the sensing principles of cucumber tendrils, we have created miniature sensing elements that can be distributed across the surface of soft manipulators to form a sensor network capable of acquire tactile information. Each sensing element is a retractable hemispherical tactile measuring applied pressure. The actual sensing principle chosen for each tactile makes use of optic fibres that transfer light signals modulated by the applied pressure from the sensing element to the proximal end of the robot arm. In this paper, we describe the design and structure of the sensor system, the results of an analysis using Finite Element Modeling in ABAQUS as well as sensor calibration and experimental results. Due to the simple structure of the proposed tactile sensor element, it is miniaturisable and suitable for MIS. An important contribution of this work is that the developed sensor system can be ”loosely” integrated with a soft arm effectively operating independently of the arm and without affecting the arm's motion during bending or elongation.