Vision-Based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios

Whole-body motion and footstep planning for humanoid robots with multi-heuristic search

A novel intelligent stereo vision approach for blast-induced fragmentation size distribution: Case study at Golgohar open-pit mine, Iran

Abstract. Urban traffic congestion, obstacle avoidance, and driving efficiency are the challenges faced by autonomous-vehicle path-planning technology in cities. The traditional artificial potential field (APF) algorithm is insufficient to meet the requirements of efficiency and safety in path planning, as it easily gets trapped in local optima when dealing with complex environments. Therefore, this paper proposes a novel AS-IAPF path-planning algorithm to more efficiently enhance the target reachability of autonomous vehicles in complex traffic environments. Firstly, this paper analyzes and elucidates the macroscopic traffic model, achieving effective modeling of dynamic traffic flow stability based on Lyapunov stability theorem and a classical 1D flow model. Thus, the threshold discriminant formula for traffic element stability is obtained. Secondly, based on the aforementioned threshold discriminant formula, a new AS-IAPF algorithm is proposed. The algorithm mainly includes two aspects: firstly, by pre-generating initial paths and introducing a Gaussian oscillation coefficient of force fields, it avoids the algorithm falling into local optima; secondly, by using the aforementioned driving stability threshold discriminant formula as a dimensional adjustment for adaptively improving and adjusting the strength coefficient of the AS-APF repulsive field, the algorithm further improves the efficiency of path planning. Finally, the algorithm is subjected to joint simulations of 2D and 3D scenarios of different types. The research results show that the AS-IAPF algorithm outperforms other algorithms of the same type with respect to comprehensive performance based on multiple 2D scenario simulation experiments. In the 3D simulation experiments of three different typical traffic scenarios, the proposed algorithm can drive autonomous vehicles to effectively perform corresponding obstacle avoidance actions based on the actual traffic scenarios ahead, ultimately achieving safe obstacle avoidance. The path-planning method proposed in this paper can enhance driving efficiency while considering the safety and stability of vehicles, providing a promising approach and reference for the path planning of autonomous vehicles.

Obstacle detection and avoidance are the important components of any autonomous land vehicle. The ability of an autonomous system to safely maneuver from an obstacle must be unerring to prevent any injuries to the driver or to a pedestrian. Hence it becomes cardinal to evaluate all the methodologies available to safely navigate a vehicle to safety. This paper performs a detailed study on the various obstacle detection methodologies and also on the various ways to avoid collision with the obstacle. This study provides a clear mapping of the domains and subdomains used for obstacle detection and obstacle avoidance.

Counterfeit and substandard pharmaceuticals represent a critical global health crisis, with the World Health Organisation (WHO) reporting that falsified medicines comprise 10% of the global pharmaceutical trade, constituting one of the fastest-growing grey economies worldwide. This illicit market affects all regions, including high-income countries, causing devastating health and economic consequences that contribute to increased mortality and morbidity rates. The problem is particularly severe in developing nations with inadequate regulatory systems. Mobile health (mHealth) technologies have emerged as promising solutions for enhancing pharmaceutical supply chain integrity. However, comprehensive frameworks that integrate multiple authentication mechanisms remain limited in addressing the growing counterfeit drug crisis. The primary objective of this study is to design and implement a novel mobile health framework that integrates innovative packaging technology and computer vision to enhance pharmaceutical integrity and patient safety. This innovative approach enables consumers to authenticate genuine drugs and detect counterfeit/spurious products through advanced Quick Response (QR) code verification systems and artificial intelligence- powered tablet recognition capabilities. The proposed system utilises individual strip QR codes that enable real-time scanning at the point of sale to verify drug authenticity, directly addressing critical gaps identified in current pharmaceutical authentication methods. To overcome the practical challenge of expiration date loss when medication strips are cut at pharmacies, we developed a computer vision-based Artificial Intelligence (AI) model that automatically recognises the number of tablets remaining in a strip and correlates this information with the unique Identifier (ID). This approach leverages recent advances in computer vision for pharmaceutical applications and automated packaging inspection technologies. Each medication strip is assigned to an individual customer at the time of purchase, with pharmacists recording detailed customer information to ensure comprehensive tracking and accountability throughout the pharmaceutical supply chain. The integrated mobile application creates a robust anti-counterfeiting ecosystem by combining secure QR code authentication with intelligent visual recognition capabilities. The computer vision model provides accurate tablet counting and strip identification, maintaining continuity of medication tracking even when packaging is modified during dispensing processes, thus significantly enhancing supply chain transparency. This dual-authentication approach builds consumer confidence in pharmaceutical authenticity while directly addressing critical vulnerabilities identified in current regulatory frameworks. This comprehensive mobile health solution provides a scalable, evidence-based approach to pharmaceutical authentication that can be readily implemented across diverse healthcare systems globally, offering substantial potential for reducing the circulation of falsified medicines and improving patient safety outcomes in the ongoing battle against the pandemic of counterfeit pharmaceuticals. To overcome the practical challenge of expiration date loss when medication strips are cut at pharmacies, we developed a computer vision-based Artificial Intelligence (AI) model that automatically recognises the number of tablets remaining in a strip and correlates this information with the unique Identifier (ID). This approach leverages recent advances in computer vision for pharmaceutical applications and automated packaging inspection technologies. Each medication strip is assigned to an individual customer at the time of purchase, with pharmacists recording detailed customer information to ensure comprehensive tracking and accountability throughout the pharmaceutical supply chain. The integrated mobile application creates a robust anti-counterfeiting ecosystem by combining secure QR code authentication with intelligent visual recognition capabilities. The computer vision model provides accurate tablet counting and strip identification, maintaining continuity of medication tracking even when packaging is modified during dispensing processes, thus significantly enhancing supply chain transparency. This dual-authentication approach builds consumer confidence in pharmaceutical authenticity while directly addressing critical vulnerabilities identified in current regulatory frameworks. This comprehensive mobile health solution provides a scalable, evidence-based approach to pharmaceutical authentication that can be readily implemented across diverse healthcare systems globally, offering substantial potential for reducing the circulation of falsified medicines and improving patient safety outcomes in the ongoing battle against the pandemic of counterfeit pharmaceuticals.

This paper introduces the software design of an Autonomous Surface Vehicle (ASV) named ASV Rybitwa. It was designed as an easily transportable platform capable of autonomous navigation and performing tasks during the RoboBoat 2024 competition, with emphasis on its modularity, generality and extendibility. The paper presents a software architecture for a small autonomous surface vehicle using novel tools that provide easy system integration and expansion. To meet such requirements, a generic control box connecting an autopilot (PX4) and a onboard computer (NVIDIA Jetson Nano) was developed so as to host the Robot Operating System 2 (ROS2) and to interact with sensors and actuators. Computer vision from three Oak-D cameras, equipped with AI algorithm support (YOLOv8), was used for navigation and obstacle avoidance. The decision-making system was designed using behavioural trees and computer vision, allowing the vehicle’s capabilities to be adapted to the needs of the current tasks and mission. In addition, an Omni X propulsion system was proposed, providing full holonomy and enhancing dynamic positioning and navigation capabilities in complex navigational conditions. In addition this paper describes lessons learned from ASV Rybitwa’s real-world testing during the aforementioned competition.

This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and a speculative AI 4.0 (Conscious AI). Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 is built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, the proposed AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly 70 years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data have stimulated each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI’s transformative potential benefits society.

Real-time obstacle avoidance is the basis to ensure the safe operation of the autonomous underwater vehicle (AUV), and it also represents the intelligent level of AUV. The main factors that affect AUV obstacle avoidance are environment perception ability, obstacle avoidance decision-making ability, and trajectory tracking control ability. This paper starts from the perspective of obstacle avoidance decision-making ability, that is, obstacle avoidance algorithm. Firstly, we summarized the structure and influencing factors of AUV real-time obstacle avoidance. Then, we introduced in detail the research progress of AUV intelligent obstacle avoidance algorithms, including fuzzy logic algorithms, neural network algorithms, and reinforcement learning algorithms. And we analysed the improvement methods of each algorithm from three-dimensional underwater environment, ocean current, AUV motion characteristics, and dynamic obstacles. Finally, we prospected the development of the AUV intelligent obstacle avoidance algorithm.

This study presents the path-following control design for a group of autonomous underwater vehicles in a plane with obstacle and collision avoidance. A distributed backstepping controller is exploited for an autonomous vehicle to design a path-following controller. Then, the controller is applied for the path-following of a group of autonomous vehicle systems in the formation setting. The graph theory is used to model the interconnection between the vehicles. To avoid obstacles and collisions, a dynamic limit-cycle-based algorithm is proposed. It is assumed that each obstacle is involved an elliptical shape and if the collision or conflict of one vehicle to the surrounded environmental obstacles is detected, the obstacle avoidance algorithm will be active to prevent collisions. Finally, the introduced algorithm is implemented and simulated for a group of underwater autonomous vehicles in MATLAB software. The simulation results illustrate the effectiveness and performance of the proposed distributed controller apparently.

This study presents a hybrid robotics automation system that optimizes real-time navigation, obstacle avoidance, and environmental mapping under difficult situations by combining AutoNav, LIDAR-based SLAM, and DenseNet with Leaky ReLU for robust motion planning. The main goals are to use AutoNav to enable autonomous navigation with optimal path planning, LIDAR-based SLAM to accomplish precise mapping and localization, and DenseNet and Leaky ReLU to improve motion planning and obstacle avoidance. Through the integration of various technologies, the study suggests an effective and flexible system for robotic operations in dynamic situations in real time. In order to provide the best trajectory planning and accurate obstacle recognition, the system constantly modifies motion planning based on real-time sensor data. Performance results show that the system outperforms individual parts, with a 95% gradient efficiency, a localization error of only 0.045 meters, and a pathfinding accuracy of 97.5%, the system's performance results illustrate its superiority over individual parts and highlight its real-time adaptability and optimization accuracy. For the future of robotics, this hybrid system offers breakthroughs in real-time decision-making and nimble autonomous robotic navigation.

A novel obstacle avoidance algorithm: “Follow the Gap Method”

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies in different environmental scenarios to showcase the convergence and efficacy of the proposed algorithm. Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M}

Advancements in computer vision have significantly improved motion and object recognition accuracy. These advancements should aid the automatic recognition of chemical experiments, potentially contributing to the recording of experiments. Creating an electronic laboratory notebook from experiment filming enhances convenience and allows more detailed information storage compared to traditional manual recording methods. Our previous research focused on employing object detection and action recognition to automate the recognition of chemical experiments. This paper presents a novel system that combines object detection, action recognition, multiple object tracking, and barcode recognition to automatically generate experimental flowcharts. We implemented our system as a graphical user interface-based application for laboratory use that successfully constructs flowcharts from videos of chemical experiments, including simple chemical manipulations.