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Obstacle is an object positioned along a path of propagation with the potential to cause a collision and hence, an accident. While there are many computer vision models which can detect objects, there is gap in their ability to different between actual objects and obstacles. The aim of this paper is to develop a blind navigation guide model for obstacle avoidance using distance vision estimation-based YOLO-V8n. To achieve this, an improved data model was developed using the MS COCO dataset and primary data collected from several indoor environments. Then, the YOLOV8n architecture was improved by adding a Weighted Feature Enhancement (WFE) model to the backbone for improved feature extraction, and Bi-directional Feature Pyramid Network (Bi-FPN) was applied to the neck to improve multi-scale feature representation. In addition, a Distance Vision Estimation (DVE) algorithm was developed and applied to the Bi-FPN before connecting it to the head of the YOLO-V8n to facilitate simultaneous object detection and distance measurement in real-time video. Furthermore, the issue of bounding box overlap in the model was addressed by applying a Wise Intersection over Unit (WIoU) loss function. Collectively, these formulated the new transfer learning algorithm called YOLO-V8n+WFE+Bi-FPN+DVE+WIoU used in this work for high-level obstacle detection and distance estimation. The model was trained considering different experimental architectures of the YOLO-V8 and loss functions, respectively, and then evaluated with precision, recall, mean absolute precision, and average precision, respectively, before validation through comparative analysis. Upon selection of the best model, it was further validated through comparison with other state-of-the art algorithms before deployment for obstacle avoidance in an indoor environment, having satisfied the condition of reliability. Real world testing of the model was performed at four different indoor sites, and the results showed that while the model was able to correctly classify objects, it could also measure their distance accurately, thereby making it suitable for deployment as a blind vision guide navigation system. [ABSTRACT FROM AUTHOR]

In this paper, we present a global reactive motion planning framework designed for robotic manipulators navigating in complex dynamic environments. Utilizing local minima-free circular fields, our methodology generates reactive control commands while also leveraging global environmental information from arbitrary configuration space motion planners to identify promising trajectories around obstacles. Furthermore, we extend the virtual agents framework introduced in Becker et al. (2021) to incorporate this global information, simulating multiple robot trajectories with varying parameter sets to enhance avoidance strategies. Consequently, the proposed unified robotic motion planning framework seamlessly combines global trajectory planning with local reactive control and ensures comprehensive obstacle avoidance for the entire body of a robotic manipulator. The efficacy of the proposed approach is demonstrated through rigorous testing in over 4,000 simulation scenarios, where it consistently outperforms existing motion planners. Additionally, we validate our framework's performance in real-world experiments using a collaborative Franka Emika robot with vision feedback. Our experiments illustrate the robot's ability to promptly adapt its motion plan and effectively avoid unpredictable movements by humans within its workspace. Overall, our contributions offer a robust and versatile solution for global reactive motion planning in dynamic environments. [ABSTRACT FROM AUTHOR]

The editorial discusses the application of Artificial Intelligence (AI) technologies in collaborative and service robots. It highlights the definition and challenges of service and collaborative robots, emphasizing the importance of human wellness in robot interactions. The article also provides an overview of recent studies focusing on optimizing unmanned aerial vehicles, improving collision avoidance methods, simplifying robot programming, and enhancing robot learning capabilities. These advancements aim to make robot behavior adaptable to changes in production processes, particularly beneficial for small and medium-sized enterprises. [Extracted from the article]

Research on navigation of mobile robots in uncertain dynamic environments is of great importance. This article is focused on solving existing problems such as planning, optimization, failure in difficult situations, and error rate vector prediction under constantly changing uncertain conditions. The aim of the conducted research is to propose a multilayer fuzzy logic model based on decision making for robots to find safe path navigation by overcoming any kind of obstacles and to understand collision-free movement of mobile robots in an uncertain dynamic environment. In this study, fuzzy logic control prediction and multilayer solution priority rules are used to improve the quality of the next position based on the path length, safety and realization time. For this purpose, the article considers the topic of calculating the risk of collision with obstacles for planning the trajectory of a mobile robot with a fuzzy controller in an environment with dynamic obstacles. [ABSTRACT FROM AUTHOR]

Tea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. However, diseases severely jeopardize the production and quality of tea leaves, leading to significant economic losses. While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection, manual leaves removal remains time-consuming and expensive. Utilizing robots for pruning can significantly enhance efficiency and reduce costs. However, the accuracy of object detection directly impacts the overall efficiency of pruning robots. In complex tea plantation environments, complex image backgrounds, the overlapping and occlusion of leaves, as well as small and densely harmful leaves can all introduce interference factors. Existing algorithms perform poorly in detecting small and densely packed targets. To address these challenges, this paper collected a dataset of 1108 images of harmful tea leaves and proposed the YOLO-DBD model. The model excels in efficiently identifying harmful tea leaves with various poses in complex backgrounds, providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning process. The improvements proposed in this study encompass the Cross Stage Partial with Deformable Convolutional Networks v2 (C2f-DCN) module, Bi-Level Routing Attention (BRA), Dynamic Head (DyHead), and Focal Complete Intersection over Union (Focal-CIoU) Loss function, enhancing the model's feature extraction, computation allocation, and perception capabilities. Compared to the baseline model YOLOv8s, mean Average Precision at IoU 0.5 (mAP0.5) increased by 6%, and Floating Point Operations Per second (FLOPs) decreased by 3.3 G. [ABSTRACT FROM AUTHOR]

The real-time obstacle avoidance strategy remains an important technical demand in robotic navigation. Although some progress has been achieved in recent years, existing works cannot handle unknown background inference in complex contexts. To deal with this challenge, this paper proposes a dynamic obstacle avoidance method in robotic navigation control through YOLOv5-based object detection. Specifically, an attention mechanism-based YOLOv5 algorithm is introduced to enhance the expression capability of small target channel features and spatial features. A P2 small target detection layer is added to strengthen the fusion of feature information between high and low layers. This is expected to address the problem that some detailed features of small targets are lost due to multiple down-sampling operations. For the navigation control part, an abstract potential field is introduced to guide the robots to move toward the target position while avoiding obstacles. Finally, we conduct some experiments on a real-world simulation scenario to make a comparative analysis. The obtained results show that the proposal can improve the obstacle avoidance effect compared with some baseline methods. [ABSTRACT FROM AUTHOR]

Complete area-coverage path planners are essential for robots performing tasks like cleaning, inspection, and surveying. However, they often involve complex calculations, mapping, and determining movement directions, leading to high computational or processing overheads and the risk of deadlocks. This paper proposes an approach for cleaning, i.e., by linear wiping of generic and discontinuous surfaces in hospital settings using inhouse assembled mobile dual-arm (MDA) robotic system. The proposed framework introduces key features: (a) a less resource-intensive approach for MDA positioning and cleaning surface mapping, (b) Modified Glasius Bioinspired Neural Network through use of heuristics (GBNN+H) to optimize surface linear wiping while obstacle avoidance, and traversal across discontinuous surfaces. The advantages of the proposed algorithm are highlighted in simulation with GBNN+H significantly reduces the number of steps and flight time required for complete coverage compared to existing algorithms. The proposed framework is also experimentally demonstrated in a hospital setting, paving the way for improved automation in cleaning and disinfection tasks. Overall, this work presents a generic and versatile, applicable to various surface orientations and complexities. [ABSTRACT FROM AUTHOR]

In this study, a cooperative robotic system comprising two Selective Compliance Assembly Robot Arm (SCARA) robots was developed and simulated. An algorithm was proposed for the coordination of robots in cooperative tasks, along with a Rapidly exploring Random Tree (RRT) path planner for obstacle avoidance. The proposed system proved effective in transferring objects between robots and in handling various scenarios of variable complexity without collisions. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of robotic systems, allowing robots to predict and react to the movements of their counterparts and changes in the environment in real time. This capability is crucial for maintaining a safe and efficient work environment. The importance of synchronization and effective communication between robots is highlighted to avoid collisions and optimize trajectories and cycle times. All tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots' performance under controlled conditions. The positive results obtained in the simulations suggest that the system is well suited for future practical implementation in industrial and manufacturing applications, such as chemical handling, collaborative welding, quality inspection, among others. These findings underscore the potential of the cooperative SCARA system to improve the efficiency and safety in industrial applications using advanced algorithms and control techniques, establishing a solid foundation for future research and development in the field of cooperative robotics. [ABSTRACT FROM AUTHOR]

One of the critical challenges in mobile robotics is obstacle avoidance, ensuring safe navigation in dynamic environments. In this sense, this work presents a comparative study of two intelligent control approaches for mobile robot obstacle avoidance based on a fuzzy architecture. The first approach is a neuro-fuzzy interface that combines neural networks' learning capabilities with fuzzy logic's rule-based reasoning, offering a flexible and adaptable control strategy. The second is a classic Mamdani fuzzy system that relies on human-defined fuzzy rules, providing an intuitive approach to control. A key contribution of this work is the development of a fast comprehensive, model-based dataset for neural network training generated without the need for real sensor data. The results show the evaluation of these two systems' performance, robustness, and computational efficiency using low-cost ultrasonic sensors on a Pioneer 3DX robot within the Coppelia Sim environment. [ABSTRACT FROM AUTHOR]

Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, including classic techniques such as the Bug algorithm and Dijkstra's algorithm, and newer developments like genetic algorithms and approaches based on neural networks. It analyzes in detail the advantages, limitations, and application areas of these algorithms and highlights current research directions in obstacle avoidance robotics. This article aims to provide comprehensive insight into the current state and prospects of obstacle avoidance algorithms in robotics applications. It also mentions the use of predictive methods and deep learning strategies. [ABSTRACT FROM AUTHOR]

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda. The authors proposed a strategy for sensorimotor control using memristive H-H neurons, integrating bio-inspired neural circuits and computational capabilities of neurons' firing features with a robot for avoidance control. [ABSTRACT FROM AUTHOR]

The quest for efficient and safe trajectory planning in robotic manipulation poses significant challenges, particularly in complex obstacle environments where the risk of encountering singularities and obstacles is high. Addressing this critical issue, our study presents a novel enhancement of the Fast Marching Tree (FMT) algorithm, ingeniously designed to navigate the complex terrain of Cartesian space with an unprecedented level of finesse. At the heart of our approach lies a sophisticated two-stage path point sampling strategy, ingeniously coupled with a singularity avoidance mechanism that leverages geometric perception to assess and mitigate the risk of encountering problematic configurations. This innovative method not only facilitates seamless obstacle navigation but also adeptly circumvents the perilous zones of singularity, ensuring a smooth and uninterrupted path for the robotic arm. To further refine the trajectory, we incorporate a quasi-uniform cubic B-spline curve, optimizing the path for both efficiency and smoothness. Our comprehensive simulation experiments underscore the superiority of our algorithm, showcasing its ability to consistently achieve shorter, more efficient paths while steadfastly avoiding obstacles and singularities. The practical applicability of our method is further corroborated through successful implementation in real-world robotic arm trajectory planning scenarios, highlighting its potential to revolutionize the field with its robustness and adaptability. [ABSTRACT FROM AUTHOR]

For the problem that the RRT (rapidly-exploring random tree) path planning algorithm generates a huge number of nodes when planning the obstacle avoidance path of robotic arm in high-dimensional space, resulting in a large burden of algorithm operation, poor obstacle avoidance performance, and easy to fall into local extremes, an improved RRT algorithm combining A* judgment function is proposed. The sampling method of RRT is changed to generate a sequence of multiple randomly sampled points each time, and the improved A* judgment function is used for sorting. Distance judgment is performed on each generated node to prevent it from falling into local search. Finally, a repetitive greedy strategy is used to remove redundant nodes, and cubic B-spline is used to make path smooth. The performance of the algorithm is analyzed in 2D and 3D maps and robot arm simulations and prototype experiments. The improved RRT algorithm can effectively reduce the number of nodes for the robotic arm to reach the target poses, alleviate the local extremes, and avoid obstacles to reach the target poses quickly and stably, which proves the effectiveness and superiority of the improved RRT algorithm. [ABSTRACT FROM AUTHOR]

Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method's viability. [ABSTRACT FROM AUTHOR]

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Animals have evolved mechanisms to travel safely and efficiently within different habitats. On a journey in dense terrains animals avoid collisions and cross narrow passages while controlling an overall course. Multiple hypotheses target how animals solve challenges faced during such travel. Here we show that a single mechanism enables safe and efficient travel. We developed a robot inspired by insects. It has remarkable capabilities to travel in dense terrain, avoiding collisions, crossing gaps and selecting safe passages. These capabilities are accomplished by a neuromorphic network steering the robot toward regions of low apparent motion. Our system leverages knowledge about vision processing and obstacle avoidance in insects. Our results demonstrate how insects might safely travel through diverse habitats. We anticipate our system to be a working hypothesis to study insects' travels in dense terrains. Furthermore, it illustrates that we can design novel hardware systems by understanding the underlying mechanisms driving behaviour. Inspired by insects in nature, the authors develop a neuromorphic robotic system with obstacle avoidance, tunnel centering and gap crossing capabilities. Their robotic system accomplishes these multiple capabilities by steering towards regions of low apparent motion. [ABSTRACT FROM AUTHOR]

The subject of this study is unmanned aerial vehicle (UAV)-based wireless networks in an obstacle-ridden environment. The aim of this study is to develop methods and software to ensure reliable LiFi communication using swarm UAVs in an obstacle-ridden environment. The objectives are as follows: 1) to describe the problem of providing a reliable UAV swarm-based LiFi network, requirements for the composition and use of UAVs, and assumptions; 2) to develop the methodology for solving research tasks; 3) to develop the method and algorithms for solving the problem, considering the requirements, assumptions, and practical limitations; 4) to explore the algorithms by developing software for modeling and searching for rational UAV placement to ensure the required UAV-based LiFi network characteristics; 5) to provide experiments and illustrative examples of the developed tool's application. The following results were obtained. 1) The requirements for the composition and use of UAVs for creating LiFi networks, as well as assumptions and limitations for the methodology development and research task solving. 2) An obstacle avoidance method using the left and right angles algorithm. 3) A method for obstacle avoidance using the controlled waterfall algorithm. 4) A software tool for modeling and searching for rational UAV placement to ensure the required LiFi network characteristics. The tool allows route construction under obstacles in 2D space and a comparison of the developed algorithms for various variants of obstacle placement. Conclusions. The main contribution of this research is a set of methods, algorithms, and software tools for providing communications between two points using LiFi technologies and a swarm of UAVs supporting these communications as transmitters in conditions of mechanical obstacles. [ABSTRACT FROM AUTHOR]

The attainment of accurate motion control for robotic fish inside intricate underwater environments continues to be a substantial obstacle within the realm of underwater robotics. This paper presents a proposed algorithm for trajectory tracking and obstacle avoidance planning in robotic fish, utilizing nonlinear model predictive control (NMPC). This methodology facilitates the implementation of optimization-based control in real-time, utilizing the present state and environmental data to effectively regulate the movements of the robotic fish with a high degree of agility. To begin with, a dynamic model of the robotic fish, incorporating accelerations, is formulated inside the framework of the world coordinate system. The last step involves providing a detailed explanation of the NMPC algorithm and developing obstacle avoidance and objective functions for the fish in water. This will enable the design of an NMPC controller that incorporates control restrictions. In order to assess the efficacy of the proposed approach, a comparative analysis is conducted between the NMPC algorithm and the pure pursuit (PP) algorithm in terms of trajectory tracking. This comparison serves to affirm the accuracy of the NMPC algorithm in effectively tracking trajectories. Moreover, a comparative analysis between the NMPC algorithm and the dynamic window approach (DWA) method in the context of obstacle avoidance planning highlights the superior resilience of the NMPC algorithm in this domain. The proposed strategy, which utilizes NMPC, demonstrates a viable alternative for achieving precise trajectory tracking and efficient obstacle avoidance planning in the context of robotic fish motion control within intricate surroundings. This method exhibits considerable potential for practical implementation and future application. [ABSTRACT FROM AUTHOR]

This vision paper covers the increased complexity of navigational environments and presents an innovative navigation system that can support the human in this task. Shipping in general also requires a mean to support navigators reduce the risk of accidents due to smaller passing distance or a shorter reaction time. An innovative approach called Velocity Obstacles might help the operator to avoid intruding into an object's protected zone by visualizing the problem space in relation to the maneuvering potential of the Own Ship. [ABSTRACT FROM AUTHOR]

Robotic Navigation Aids (RNAs) assist visually impaired individuals in independent navigation. However, existing research overlooks diverse obstacles and assumes equal responsibility for collision avoidance among intelligent entities. To address this, we propose Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA). Our FLC-ORCA method assigns responsibility for collision avoidance and predicts the velocity of obstacles using a LiDAR-based mobile robot. We conduct experiments in the presence of static, dynamic, and intelligent entities, recording navigation paths, time taken, angle changes, and rerouting occurrences. The results demonstrate that the proposed FLC-ORCA successfully avoids collisions among objects with different collision avoidance protocols and varying liabilities in circumventing obstacles. Comparative analysis reveals that FLC-ORCA outperforms other state-of-the-art methods such as Improved A* and Directional Optimal Reciprocal Collision Avoidance (DORCA). It reduces the overall time taken to complete navigation by 16% and achieves the shortest completion time of 1 min and 38 s, with minimal rerouting (1 occurrence) and the smallest angle change (12°). Our proposed FLC-ORCA challenges assumptions of equal responsibility and enables collision avoidance without pairwise manoeuvres. This approach significantly enhances obstacle avoidance, ensuring safer and more efficient robotic navigation for visually impaired individuals. [ABSTRACT FROM AUTHOR]

Although drone users have received the necessary training, the reflexes of making decisions against a sudden natural event such as wind and avoiding a nearby obstacle may not be sufficient. Therefore, whether drones fly autonomously or under user control, they must sense and act accordingly for an uninterrupted mission. In this study, a drone design and application for obstacle detection and obstacle avoidance were carried out. In the designed drone, Pixhawk was used as the flight control board, ultrasonic sensors were used to detect obstacles, and Arduino Uno was used as a microcontroller to obtain avoidance commands. The sensors used in obstacle detection and their performance are the most decisive factors in achieving the targeted goal. Because obstacle detection sensors are affected by electrical noises, the success of detecting obstacles decreases. For this reason, first of all, the integration of these sensors into the system was investigated and the drone was developed accordingly. Then, an algorithm was developed using a software filtering method both to minimize communication instabilities and to increase the clarity of detection. Finally, the ability to evade obstacles both while the drone is suspended in the air and while it is in motion has been investigated. In the experiments carried out, it was found that the drone was able to successfully avoid obstacles up to a flight speed of 3.94 m/s [ABSTRACT FROM AUTHOR]

In order to navigate safely and effectively with humans in close proximity, robots must be capable of predicting the future motions of humans. This study first consolidates human studies in motion, intention, and preference into a discretized human model that can readily be used in robotics decision making algorithms. Cooperative Markov Decision Process (Co-MDP), a novel framework that improves upon Multiagent MDPs, is then proposed for enabling socially aware robot obstacle avoidance. Utilizing the consolidated and discretized human model, Co-MDP allows the system to (1) approximate rational human behavior and intention, (2) generate socially-aware robotic obstacle avoidance behavior, and (3) remain robust to the uncertainty of human intention and motion variance. Simulations of a human-robot co-populated environment verify Co-MDP as a feasible obstacle avoidance algorithm. In addition, the anthropomorphic behavior of Co-MDP was assessed and confirmed with a human-in-the-loop experiment. Results reveal that participants can not directly differentiate agents that were controlled by human operators from Co-MDP, and the reported confidences of their choices indicates that the predictions from participants were backed by behavioral evidence rather than random guesses. Thus the main contributions for this paper are: consolidating past human studies of rational human behavior and intention into a simple, discretized model; the development of Co-MDP: a robotic decision framework that can utilize this human model and maximize the joint utility between the human and robot; and an experimental design for evaluation of the human acceptance of obstacle avoidance algorithms. [ABSTRACT FROM AUTHOR]

A wheelchair can provide limited but crucial mobility to an injured or disabled individual. This paper presents the first stage of the development of a smart wheelchair which is the customization of a manually controlled wheelchair with a novel implementation of octascopic vision. This relatively inexpensive design of an autonomous wheelchair consists of two monochromic camera arrays (each having four cameras) placed around the frame of the wheelchair to achieve a view of 360 degrees. The initial research goal was to design a wheelchair controlled by the embedded processor, allowing the wheelchair to navigate autonomously around an indoor facility with and without human intervention. Additionally, it was intended to allow those previously denied access to the world of automatic wheelchairs because of a low personal income. Through the testing of wheelchair functionality, (a) a large dataset of octascopic images was captured from this wheelchair, and (b) a YOLOv7-based object detection model was developed to avoid obstacles and autonomously control the movement. This paper presents the camera placement and the obstacle detection model using octascopic images. All the project design files have been granted an open-source license and can be reproduced publicly. [ABSTRACT FROM AUTHOR]

Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human–robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator's authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance. • Gaussian process regression effectively models human–robot-environment interactions. • Bayesian optimization uses eight times less data compared to exhaustive grid. • Obstacle avoidance shared control enhances performance of teleoperated land robots. • Statistical confidence metrics tune a control algorithm and quantify success rate. [ABSTRACT FROM AUTHOR]

This paper presents a full suite of Robotic Tower Crane (RTC) technologies that can be seamlessly implemented on traditional saddle-jib tower cranes to boost the construction safety and productivity. The robotisation of tower cranes enables the RTC capabilities of automatic path planning for point-to-point movement, and dynamic obstacle avoidance with re-planning. While the former fast generates the RTC path based on decoupling of vertical and horizontal movements, the latter takes a 'hoist-first' approach to prioritise safety. A motion compensation algorithm is developed for multi-step speed control to achieve the exact displacement based on dynamically optimizing the time duration at each planned velocity. The implementation of the RTC system has demonstrated a comprehensive approach that combines laboratory simulations, hardware-in-the-loop testing, and live demos for on-site deployment. A comparative performance and operational time study reveals the RTC's superior precision and consistency over human operators. • Robotised saddle-jib tower cranes based on multi-modal sensing. • Automated safe path generation for point-to-point movement. • Proposed a 'hoist-first' approach for dynamic obstacle avoidance with replanning. • Optimised the time duration at each planned velocity for multi-step speed control. • Implemented the robotic tower crane technologies in live construction settings. [ABSTRACT FROM AUTHOR]

A novel potential field‐based model curve fitting method (PF‐MCF) is presented in this paper to handle emergency collision avoidance in waypoint tracking (following waypoints from a leading vehicle). It is reported that the PF has high performance on real‐time obstacle avoidance in robotic path planning. However, some inherent limitations exist in the PF, such as no passage between closely spaced obstacles and oscillations in the presence of obstacles, which is rarely discussed in the research field of PF‐based autonomous vehicles (AVs), especially in waypoint tracking. To solve these problems, we propose the PF‐MCF method to transform the clothoid curve into a quadratic programming form and solve the optimization under reasonable constraints to consider the different waypoints from the PF and the leading vehicle. The proposed PF‐MCF method is validated via MATLAB/Simulink and CarSim to avoid multiple obstacle vehicles in waypoint tracking compared with the two latest methods (PF‐based model predictive controller and PF‐based curve fitting method). The simulation results confirm the efficiency of finding a passage in closely spaced obstacle vehicles and eliminating oscillations (under 0.5 [deg] in tire steering angle) in the presence of obstacles compared to other latest methods. [ABSTRACT FROM AUTHOR]

This paper proposes a kinematic obstacle avoidance algorithm for Space hyper-redundant manipulators, and its basic idea is to use a static and a dynamic curve to constrain the macroshape of the manipulators simultaneously. The static curve is constructed based on a traditional rapidly exploring random tree algorithm, and a backbone curve is utilized as the dynamic curve. For these two curves, two novel shape control methods are proposed to accomplish the shape constraining process. Finally, we verify the reliability and effectiveness of our algorithm through simulations. [ABSTRACT FROM AUTHOR]

Motion planning is a core issuein the field of robotic control, which directly affects the programming efficiency of robots. In this article, we study the motion planning problem of manipulators for simultaneous obstacle avoidance and target tracking, and propose a novel real-time planning method in a complex workspace. One important feature of the proposed method is that the robot can avoid colliding with obstacles by easily defining “virtual fences,” which are described by a group of level set functions. Thus, the feasible space can be abstracted as inequality constraints. Taking the predefined task, physical constraints, and feasible space constraints into consideration, the motion planning problem is formulated into a quadratic programming (QP) one, in which the redundant degrees of freedom are used to optimize the velocities of the robot. Then, the control command is obtained by an established recurrent neural network, which is capable of solving the QP problem in an online manner. Theoretical conduction and verification in several typical workspaces demonstrate the efficacy of the established method, such as the abilityto remove physical fences, quick rearrangement, and performance optimization. [ABSTRACT FROM AUTHOR]

The finite‐time formation tracking control is investigated for a multi‐agent system (MAS) with obstacle avoidance. For the collision and obstacle avoidance problem in the formation process, the artificial potential field is used as the formation planning design, and the virtual structure is adopted to improve the organizational ability of the formation. The trajectory tracking control follows the back‐stepping scheme, and the finite‐time technique is developed in the control design. Considering the dynamics uncertainty of the agent system, a neural network is applied for estimating and the prediction error‐based adaptive law is established to achieve the precise estimation performance. Moreover, the predefined performance function is embedded to satisfy the output constraint. The uniformly ultimate boundedness of the system error signals and the finite‐time convergence of the MAS are guaranteed. The simulation study is performed to validate the proposed control for multiple autonomous underwater vehicles system, while the results manifest that the obstacle avoidance with high‐precision tracking and formation performance will be achieved under the formation trajectory tracking controller. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have been highlighted as a major growth area for future transportation systems and the deployment of large numbers of these vehicles is expected when safety and legal challenges are overcome. To meet the necessary safety standards, effective collision avoidance technologies are required to ensure that the number of accidents are kept to a minimum. As large numbers of autonomous vehicles, operating together on roads, can be regarded as a swarm system, we propose a bio-inspired collision avoidance strategy using virtual pheromones; an approach that has evolved effectively in nature over many millions of years. Previous research using virtual pheromones showed the potential of pheromone-based systems to maneuver a swarm of robots. However, designing an individual controller to maximise the performance of the entire swarm is a major challenge. In this paper, we propose a novel deep reinforcement learning (DRL) based approach that is able to train a controller that introduces collision avoidance behaviour. To accelerate training, we propose a novel sampling strategy called Highlight Experience Replay and integrate it with a Deep Deterministic Policy Gradient algorithm with noise added to the weights and biases of the artificial neural network to improve exploration. To evaluate the performance of the proposed DRL-based controller, we applied it to navigation and collision avoidance tasks in three different traffic scenarios. The experimental results showed that the proposed DRL-based controller outperformed the manually-tuned controller in terms of stability, effectiveness, robustness and ease of tuning process. Furthermore, the proposed Highlight Experience Replay method outperformed than the popular Prioritized Experience Replay sampling strategy by taking 27% of training time average over three stages. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative obstacle avoidance algorithm of multiple bionic snake robots in fluid based on IB-LBM. The method can make the multiple bionic snake robots avoid different obstacles in the fluid under the control of the improved Serpenoid curve function. The proposed method has high parallelism, can simulate the complex non-linear phenomenon of the multiple snake robots, deal with the complex boundary conditions of the robot, and reduce the conversion of the computational grid. Firstly, a non-linear fluid model is established by LBM, which solves the non-linear problem that the classical Navier–Stokes equations cannot explain the random motion. Secondly, the force source boundary model of multiple bionic snake robots is established by IBM, which saves the calculation time, improves the calculation efficiency and system stability. After that, each bionic snake robot is given a special force to make the robots collaborate with each other and non-colliding with each other in the process of the obstacle avoidance. Finally, through simulation experiments, the trajectory of multiple bionic snake robots avoiding different number of the obstacles in the fluid is analyzed and the collaborative obstacle avoidance process of multiple bionic snake robots in fluid is observed. The validity of the collaborative obstacle avoidance algorithm of multiple bionic snake robots in fluid based on the IB-LBM is verified. • The cooperative obstacle avoidance of bionic snake robots in fluid is realized. • The nonlinear control problem of stochastic motion is explained by IB-LBM. • The macro-micro and discrete-continuous relationships of the model are considered. • IBM realizes fluid-solid coupling control between fluid and robot immersed boundary. [ABSTRACT FROM AUTHOR]

This article proposes a control algorithm for obstacle avoidance and trajectory tracking for a redundant‐manipulator in smart‐homes. The redundancy provides dexterity and flexibility for the applications like picking, dropping, and transporting objects, tracking predefined paths while avoiding obstacles. The obstacle avoidance is one of the critical problems that need to be addressed. Our proposed algorithm, zeroing neural network with beetle antennae search (ZNNBAS), unifies these two problems into a single constrained optimization problem, which includes a penalty function to reward the optimizer on avoiding obstacles while tracking the reference trajectory. The penalty function is based on the principle of maximizing the minimum distance between the joints of the manipulator and the obstacles. Gilbert Johnson Keerthi (GJK) algorithm is used to calculate the distance between the manipulator and the obstacle as it uses the 3D geometry of the object and manipulator. To test the working of ZNNBAS, we simulated the model of the KUKA LBR robotic manipulator. We used two reference trajectories, that is, hypotrochoid and a character "M," with an arbitrarily shaped obstacle. The simulation results show that ZNNBAS was able to trace the reference path while successfully avoiding the obstacle accurately. [ABSTRACT FROM AUTHOR]

A trajectory optimization algorithm is developed in this article to compute a trajectory that avoids obstacles for an unmanned aerial vehicle and minimize the load factor. Quadratic and polyhedron obstacles are both considered in this article. To satisfy the safety distance requirements in engineering practice, a distance‐based obstacle avoidance constraint is formulated, which restricts the minimum distance that a vehicle can approach the obstacle. Strong duality equivalent converts the constraints into equivalent forms without introducing binary variables. Subsequently, the trajectory optimization problem is numerically solved via a hp‐pseudospectral sequential convex programming method, which iteratively solves a sequence of second‐order cone programming subproblems until convergence is attained. Furthermore, a modified trust‐region constraint and a customized nominal trajectory update rule are proposed to accelerate the convergence of the algorithm. A group of simulations are performed to demonstrate that the algorithm is computationally efficient and capable of generating a trajectory that satisfies safety distance requirements, preventing conservativeness in the trajectory, reducing the incidence of intersample constraints violation. [ABSTRACT FROM AUTHOR]

The autonomous vehicle parking problem has drawn increased attention in recent times. It can resolve the parking-related issues that involve minimizing human driving errors and saving fuel and time. In practice, parallel parking needs extra care when dynamicity is present in the environment. Many human drivers have the expertise to take quick action when obstacles appear on their driving path. An autonomous system should possess such intelligence that can mimic human-like behavior in the presence of obstacles. This work focuses on providing an intelligent autonomous parking design using a car-like mobile robot (CLMR) that efficiently parks the vehicle in dynamic environmental conditions. A novel fuzzy-based obstacle avoidance controller is proposed that integrates sensor information into the parking problem, obtained from the surrounding of a CLMR. Ultrasonic sensors' arrangement and their grouping provide inputs for the fuzzy system. A fuzzy-based obstacle avoidance controller can execute intelligent parking like a human by avoiding static as well as moving obstacles. The proposed work is tested in different challenging environmental conditions. Simulation results demonstrate that the proposed algorithm accomplishes autonomous parallel parking reasonably well in the presence of static and moving obstacles and can be used to park the vehicle in real-time. The proposed work helps to solve the autonomous parking problem with safety, especially in dynamic environmental conditions. [ABSTRACT FROM AUTHOR]

The article discusses the importance of motion and kinematics in the field of robotics. It highlights how robots have become a part of everyday life and are reshaping society in various fields. Kinematics plays a central role in the design of robotic systems, covering topics such as mechanism analysis and synthesis, robot control, path planning, and bioinspired robotics. The article also mentions the "Kinematics and Robot Design" (KaRD) series, which provides a platform for scientists to present and discuss new ideas and applications in an open science setting. The sixth edition of KaRD, published in 2023, included ten papers on topics such as mechanism synthesis, geometric error effects, medical robotics, and robot control. The article concludes by acknowledging the members of the KaRD series' Scientific Committee. [Extracted from the article]

A hierarchical vehicle collision avoidance control method based on model predictive control is presented in this study. In the upper level of the controller, a spatial-based two-degree-of-freedom vehicle model is used for dynamic path planning to decrease the computational burden of the algorithm. Obstacles and road boundaries are translated into spatial-based constraints on system states. An objective function that considers tire adhesion margin is introduced to the path tracking controller to enhance vehicle safety. Meanwhile, the dynamic constraints of vehicle lateral acceleration, sideslip, and tire slip angles are designed in accordance with the tire-road adhesion coefficient. A time-based nonlinear model predictive controller is also designed and compared with the proposed method to verify the effectiveness and superiority of the latter. Theoretical analyses and simulation results indicate that the proposed collision avoidance control system has good collision avoidance effect. [ABSTRACT FROM AUTHOR]

In this paper, an unmanned aerial vehicle (UAV) maneuver avoidance decision algorithm based on experiences and intuitions is proposed for threats UAVs encounter during flight processes. Firstly, the situation information obtained during the safety threat avoidance maneuver of the UAV is structured according to the maneuver avoidance area, and the corresponding maneuver decision tag set is recorded. A UAV scenario decision-making knowledge is also established based on the data. Secondly, the long and short-term memory network is used to map the relations between the data scenario and the maneuvering decision in the UAV scenario decision-making knowledge database. Thirdly, according to the principle that human make action decisions based on their intuitions, a UAV maneuver avoidance algorithm is designed based on intuitions. Finally, the simulation analysis is conducted to illustrate the effectiveness of the proposed real-time threat avoidance algorithm for UAVs. [ABSTRACT FROM AUTHOR]

Aiming at the operational problems of robots in dynamic and complex environments, the motion control method of manipulator tracking moving targets and avoiding obstacles is studied. The traditional PID control method is combined with DDPG algorithm. PID control is used to make the working plane of the manipulator approach the target quickly and coincide with it. Then DDPG algorithm is used to make the manipulator autonomously learn to track the projection of target and avoid the projection of obstacles in the plane, and finally achieve tracking and obstacle avoidance in the three-dimensional space. This method takes the manipulator as an agent which perceives the distance deviation between the target and the obstacle to learn obstacle avoidance and tracking strategies automatically. This method is applied to the path planning and obstacle avoidance task of multi-degree-of-freedom manipulator. Compared with DDPG algorithm, which only takes manipulator as agent to make decision control in space, the simulation results show that the proposed method has a better convergence effect and control performance, and the manipulator can stably track the target and avoid obstacles after training. [ABSTRACT FROM AUTHOR]

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Featured Application: This work provides a generalization of the velocity obstacle (VO) collision avoidance strategy to nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner. [ABSTRACT FROM AUTHOR]

In this paper, we present a decentralized and communication-free collision avoidance approach for multi-robot systems that accounts for both robot localization and sensing uncertainties. The approach relies on the computation of an uncertainty-aware safe region for each robot to navigate among other robots and static obstacles in the environment, under the assumption of Gaussian-distributed uncertainty. In particular, at each time step, we construct a chance-constrained buffered uncertainty-aware Voronoi cell (B-UAVC) for each robot given a specified collision probability threshold. Probabilistic collision avoidance is achieved by constraining the motion of each robot to be within its corresponding B-UAVC, i.e. the collision probability between the robots and obstacles remains below the specified threshold. The proposed approach is decentralized, communication-free, scalable with the number of robots and robust to robots' localization and sensing uncertainties. We applied the approach to single-integrator, double-integrator, differential-drive robots, and robots with general nonlinear dynamics. Extensive simulations and experiments with a team of ground vehicles, quadrotors, and heterogeneous robot teams are performed to analyze and validate the proposed approach. [ABSTRACT FROM AUTHOR]

Obstacle avoidance path planning capability, as one of the key capabilities of UAV (Unmanned Aerial Vehicle) to achieve safe autonomous flight, has always been a hot research topic in UAV research filed. As a commonly used obstacle avoidance path planning algorithm, RRT (Rapid-exploration Random Tree) algorithm can carry out obstacle avoidance path planning in real time and online. In addition, it can obtain the asymptotically optimal obstacle avoidance path on the premise of ensuring the completeness of probability. However, it has some problems, such as high randomness, slow convergence speed, long transit time, and curved flight trajectory, so that it cannot meet the flight conditions of the actual UAV. To solve these problems, the paper proposes an improved RRT algorithm. In the process of extending the random tree, ACO (ant colony optimization) is introduced to make the planning path asymptotically optimal. The optimized algorithm can set pheromones on the path obtained by RRT and select the next extension point according to the pheromone concentration. And then through a certain number of iterations, it converges to an ideal path scheme. In addition, this paper also uses MATLAB to verify the effectiveness and superiority of the algorithm: Although RRT is easy to fall into local optimization, since the optimization method in this paper can almost certainly converge to the optimal solution, when it is necessary to preplan the path before UAV takeoff, it can be used. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) path following issue is a significant part of the UAV mission arranging framework, which needs to get an ideal way following and handle collision avoidance instantaneously. To resolve this issue, a hybrid algorithm termed NFMGOA is presented by combining the neuro-fuzzy and modified grasshopper optimization algorithm (NFMGOA). In the proposed work, unlike regularly utilized strategies, the consequent parameters of the neuro-fuzzy are updated by utilizing the modified grasshopper optimization algorithm for efficient collision avoidance. Furthermore, possible collisions must be recognized and automatically settled as the vehicle moves toward the way. Emergency avoidance is designed by autonomously changing the heading and speed of every UAV. Initially, the position of the obstacle is attained by Kalman filtering, the input UAV data. Subsequently, cooperative path flow is produced by the Lyapunov function. Finally, effective NFMGOA is utilized for better collision avoidance. The examining outcomes of single and multiple UAVs demonstrated that the presented technique outperforms the existing methodologies regarding convergence and execution time performance assessments. [ABSTRACT FROM AUTHOR]

Spacecraft on-orbital services and docking require their autonomous rendezvous control system to have obstacle avoidance capability. Motivated by this, a suboptimal velocity artificial potential function-based control scheme is presented. An ellipsoid model is applied to describe the outer envelopes of the service spacecraft and the obstacles via an eigenvalue algorithm. This has better description precision than the traditional methods. The potential sigmoid function is used to generate repulsive force to avoid obstacles collision. A velocity artificial potential function-based controller is finally developed to ensure that the relative speed of the service spacecraft is reduced to zero before reaching the outer envelops of obstacles. The shaping parameters of the attractive potential function are adaptively optimized. Numerical simulations are performed to demonstrate that the approach can achieve a safe and autonomous rendezvous with fuel cost saved. [ABSTRACT FROM AUTHOR]

The rapid growth in Autonomous Vehicle (AV) technology endeavors increased attention towards road safety in recent days. Particularly, a higher number of road accidents occurs when the AV tries to overtake or change the lane. To cut down the number of accidents and improve traffic reliability, the AV should be capable of making intelligent decisions and communicating those to other AVs. Therefore, in this paper, a Cooperative Collision avoidance scheme for AVs at Overtaking and Lane Changing maneuver (CCAV-OLC) is proposed. The Inverse Reinforcement Learning (IRL) in the CCAV-OLC scheme, processes on the given number of expert demonstrations for automatically acquiring the reward function, and thereby imitating actual human driving strategy and decisions. However, the adaptability of IRL to a high-dimensional AV environment restricts the performance of the CCAV-OLC scheme. To overcome this, the IRL in CCAV-OLC leverages the Gaussian Process (GP) regression model (IRL-GP), which enables data-efficient Bayesian prediction even when the number of demonstrations is very low. After taking intelligent decisions in overtaking and lane changing maneuvers, the AVs cooperatively communicate and exchange the decisions with each other by 6th Generation Vehicle-to-Everything (6G-V2X) communications, which further improves the accuracy and lessens the time taken for making optimal decisions. The experimental results show that the AVs clone the expert's optimal driving strategy and avoid the collisions to a greater extent. [ABSTRACT FROM AUTHOR]

In this paper, the inverse kinematics (IK) of redundant manipulators is presented and studied, where the performance of end-effector path planning is guaranteed. A new Jacobian pseudoinverse (JP)-based IK method is proposed and studied using a typical numerical difference rule to discretize the existing IK method based on JP. The proposed method is depicted in a discrete-time form and is theoretically proven to exhibit great performance in the IK of redundant manipulators. A discrete-time repetitive path planning (DTRPP) scheme and a discrete-time obstacle avoidance (DTOA) scheme are developed for redundant manipulators using the proposed method. Comparative simulations are conducted on a universal robot manipulator and a PA10 robot manipulator to validate the effectiveness and superior performance of the DTRPP scheme, the DTOA scheme, and the proposed JP-based IK method. [ABSTRACT FROM AUTHOR]