Avoidance Method Research Articles

Research on ship collision avoidance path planning based on modified potential field ant colony algorithm

Owing to the rapid economic growth, the importance of the shipping industry has gained prominence. Navigation safety may be prone to hidden dangers if ship collision avoidance measures only depend on the decisions of the crew, especially since ship density has increased sharply and ship routes have become more complicated. The real-time path planning of collision avoidance requires more efficient algorithms, the carbon emission constraint is added in the model to limit the sudden speed change of the ship in each trajectory segments, and the path planned by the algorithm is smoother, which can reduce the probability of dangerous accidents. The ship collision avoidance path planning problem with carbon emission constraint is considered in this study, and a nonlinear programming model is established to minimize the mileage and carbon emission in the process of collision avoidance. A modified potential field ant colony algorithm is proposed to solve the model, in which the ant colony algorithm is combined with the modified artificial potential field method for real-time dynamic avoidance. The main idea is to use the potential field to guide the ant colony in the early iterations, and optimize the design of partial components to improve the convergence speed and global optimization of the algorithm. Finally, simulation results show that the modified potential field ant colony algorithm proposed in this study can help improve the accuracy of route prediction and anti-collision, and solve the ship collision avoidance path planning problem effectively based on automatic identification system (AIS) data.

A computation effective singularity avoidance method for the underwater manipulator with a non-spherical wrist

In order to perform increasingly complex underwater tasks, we have designed a hydraulic underwater non-spherical wrist manipulator with a binocular vision system and a six-dimensional force sensor mounted behind the end-effector, which can improve the path accuracy and automation capabilities of the system. The system lays a foundation for the high precision and autonomous deep-sea operation and can improve its intelligence level. In this paper, we focus on solving the singularity avoidance problem of the hydraulic underwater manipulator for accurate control, and propose a computationally efficient singularity avoidance method to improve the path accuracy of the manipulator with the non-spherical wrist. Firstly, the singular configurations of the manipulator are analyzed. Secondly, the Jacobian is decomposed into sub-block matrices to obtain the singular factors according to the singular configurations. Thirdly, the singularities are avoided by the approximate damped reciprocal method. Theoretical analysis shows that the computation costs of the proposed method are only one-third to one-half of that of the traditional methods. The simulation results prove that the proposed method can largely improve the path accuracy of the manipulator with less computation costs, which shows that our method would have a certain significance in improving the precision and real-time response of deep-sea operations.

Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

With the vast advancements in Information Technology, the emergence of Online Social Networking (OSN) has also hit its peak and captured the attention of the young generation people. The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user. However, the attackers also target this height of OSN utilization, explicitly creating the clones of the user’s account. Various clone detection mechanisms are designed based on social-network activities. For instance, monitoring the occurrence of clone edges is done to restrict the generation of clone activities. However, this assumption is unsuitable for a real-time environment and works optimally during the simulation process. This research concentrates on modeling and efficient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy. This model does not rely on assumptions. Here, an ensemble Adaptive Random Subspace is used for classifying the clone victims with k-Nearest Neighbour (k-NN) as a base classifier. The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results. When the weighted node’s threshold value is higher, the trust establishment is terminated, and the clones are ranked and sorted in the higher place for termination. Thus, the victims are alert to the clone propagation over the online social networking end, and the validation is done using the MATLAB 2020a simulation environment. The model shows a better trade-off than existing approaches like Random Forest (RF), Naïve Bayes (NB), and the standard graph model. Various performance metrics like True Positive Rate (TPR), False Alarm Rate (FAR), Recall, Precision, F-measure, and ROC and run time analysis are evaluated to show the significance of the model.

Collision Avoidance Among Dense Heterogeneous Agents Using Deep Reinforcement Learning

Navigating in a complex congested social environment without collision is a crucial and challenging task. Recent studies have demonstrated the considerable success of Deep Reinforcement Learning (DRL) in multi-agent collision avoidance. However, the assumption of these studies that agents are homogeneous circles deviates from reality, leading to performance deterioration in congested scenarios. The current work extends the DRL-based approaches to develop a collision avoidance method for congested scenarios wherein the heterogeneity of agents can no longer be disregarded. Considering shape heterogeneity, we use the Orientated Bounding Capsule (OBC) to model the agents and transform the interactive state space of Robot-Obstacle agent pair. For speed heterogeneity, we design a velocity-related collision risk function to shape the behavior of the robot. Experimental results demonstrate that our proposed method outperforms state-of-the-art DRL-based approaches in terms of success rate and safety. It also exhibits desired collision avoidance behavior.

Multi-Agent Collision Avoidance System Based on Centralization and Decentralization Control for UAV Applications

A collision avoidance method for multi-agent systems based on the centralization and decentralization effects for cooperative control is presented in this article. In this context, a matrix called Anti-Laplacian is proposed to control the agents when the UAVs are on a conflicting route. The matrix adjustment occurs through proportionality relations with the Laplacian Matrix from an interaction graph between the agents. The adjustment method aims a balance between centering and decentering to avoid collisions. Controlled quadcopters follow the trajectory indicated by virtual agents that act as guides for the real ones. The tests are performed via simulation for the most critical cases, with protocols involving flock centralization. As a virtual agent, a first-order model is used in the simulation, the method efficiency is observed by varying the number of agents involved. The proposed method presents allows different forms to control the collision avoidance parameter, such as deterministic and machine learning methods.

A Survey of Indoor UAV Obstacle Avoidance Research

UAV obstacle avoidance technology is one of the key factors to realize UAV autonomous flight, efficient and accurate obstacle avoidance is significant to complete the UAV autonomous flight task. In contrast, the dynamic, real-time and uncertainty of the environment in which the UAV is located makes the problem very tricky, especially in the indoor environment. At present, a large number of scholars have shown strong interest in the indoor UAV obstacle avoidance problem. With the rapid development of computer technology and hardware devices, many intelligent algorithms have been proposed to solve the obstacle avoidance problem. However, the research on indoor UAV obstacle avoidance technology is not comprehensive enough, and there is a lack of summarization of the research results in recent years. This paper introduces the sensor modules commonly used for indoor UAV environment sensing, related obstacle avoidance methods based on sensory detection. Classifies and composes the commonly used UAV path planning obstacle avoidance algorithms, and gives several representative UAV flight control research methods. This paper summarizes the advantages and disadvantages of different perception modules and detection methods applied to UAV obstacle avoidance tasks, and compares various current path planning methods. Finally, the critical difficulties and challenges faced in the field of indoor UAV obstacle avoidance are discussed, and future research in the field of UAV obstacle avoidance has prospected.

Study of Pedestrian Avoidance Method by DWA Considering Personal Space

Study of Pedestrian Avoidance Method by DWA Considering Personal Space

Dense Scene Flow Estimation with the GVF Snake Model for Obstacle Detection Using a 3D Sensor in the Path-Planning Module

Abstract The novel framework for estimating dense scene flow using depth camera data is demonstrated in the article. Using these estimated flow vectors to identify obstacles improves the path planning module of the autonomous vehicle's (AV) intelligence. The primary difficulty in the development of AVs has been thought to be path planning in cluttered environments. These vehicles must possess the intelligence to recognize their surroundings and successfully navigate around obstacles. The AV needs a thorough understanding of the surroundings to detect and avoid obstacles in a cluttered environment. Therefore, when determining the course, it is preferable to be aware of the kinematic behavior (position and the direction) of the obstacles. As a result, by comparing the depth images between different time frames, the position and direction of the obstacles are calculated using a 3D vision sensor. The current study focuses on the extraction of the flow vectors in 3D coordinates from the differential scene flow method. Generally, the evaluation of scene flow algorithms is crucial in determining their accuracy and effectiveness in different applications. The gradient of the vector field snake model, which extracts changes in pixel values in three directions, is combined with the scene flow technique to identify both static and dynamic obstacles. Our goal is to create a single-vision sensor-based real-time obstacle avoidance method based on scene flow estimation. In addition, the common evaluation metrics such as endpoint error (EPE), average angular error (AAE), and standard deviation angular error (STDAE) are used to measure the accuracy of different algorithms in terms of computational errors with the benchmark Middlebury datasets. The proposed technique is validated with different experiments using a Pixel-Mixed-Device (PMD) camera and a Kinect sensor as 3D sensors. Finally, the numerical and experimental results are displayed and reported.

Investigation of Resonance Avoidance Method for Vacuum Cleaner

Investigation of Resonance Avoidance Method for Vacuum Cleaner

Research on Obstacle Avoidance Method of Intelligent Car Based on Optimized Fuzzy Control Algorithm

Research on Obstacle Avoidance Method of Intelligent Car Based on Optimized Fuzzy Control Algorithm

Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery.

In the robot-assisted minimally invasive surgery, if a collision occurs, the robot system program could be damaged, and normal tissues could be injured. To avoid collisions during surgery, a 3-dimensional collision avoidance method is proposed in this paper. The proposed method is predicated on the design of 3 strategic vectors: the collision-with-instrument-avoidance (CI) vector, the collision-with-tissues-avoidance (CT) vector, and the constrained-control (CC) vector. The CI vector demarcates 3 specific directions to forestall collision among the surgical instruments. The CT vector, on the other hand, comprises 2 components tailored to prevent inadvertent contact between the robot-controlled instrument and nontarget tissues. Meanwhile, the CC vector is introduced to guide the endpoint of the robot-controlled instrument toward the desired position, ensuring precision in its movements, in alignment with the surgical goals. Simulation results verify the proposed collision avoidance method for robot-assisted minimally invasive surgery. The code and data are available at https://github.com/cynerelee/collision-avoidance.

Ship Collision Avoidance Based on the Vulnerability Assessment for Small Vessels in Bad Weather

In the conditions of bad weather, the small vessels maneuvering ability of collision avoidance is seriously reduced and the vulnerability to maritime accidents increases due to the influence of strong wind and wave. This paper proposes a collision avoidance decision-making and control method for small vessels using the integrated collision risk assessment based on fuzzy logic. First, the speed of wind and height of wave in the bad weather conditions are fuzzified. Then, the collision risk indices are selected for fuzzy inference to get the basic collision risk which will be integrated with the vulnerability to collision accidents and it helps to improve the accuracy of collision risk assessment. According to the current multi-ships encounter situations, the similar cases are retrieved from the case database, and the collision avoidance decision is given after fuzzy adjustments. To implement collision avoidance actions, a fuzzy algorithm for nonlinear ship heading dynamic surface control (DSC) control considering input saturation and uncertainties is designed. The problem of "computational expansion" that may occur in high-order systems is avoided by using DSC technology. Finally, simulation experiments are implemented to verify the effectiveness of the collision avoidance method.

Resolusi Konflik Internal Subak Taman Desa Taman Kecamatan Abiansemal Kabupaten Badung

Internal Conflict Resolution in Subak Taman, Taman Village, Abiansemal District, Badung Regency
 Subak is an organization that oversees farmers to regulate irrigation, in its activities Subak cannot avoid problems in the form of conflict. Internal conflicts within the subak organization can be triggered by various factors, namely water scarcity and weak coordination between tempek to distribute water resources. Various conflicts that can arise in subak as a consequence of interaction between individuals in the organization, in this case farmers are members of the subak, it is very necessary to handle conflicts that can be applied in the form of management of any problems that arise from the subak organization. This study aims to determine the conflicts that occur in Subak Taman and the conflict resolutions taken to overcome these internal conflicts. This research was conducted with a qualitative descriptive analysis using primary and secondary data. Data were collected through in-depth interviews, observations, documentation studies and literature studies. The results showed that the conflicts that occurred in Subak Taman, Taman Village, Abiansemal District, Badung Regency were lack of irrigation water, work situations where there was blurring of task boundaries, communication failures, cropping patterns and cropping schedules, irrigation water pollution, and individual characteristics, namely differences. traits and personalities and differences in values and needs. Conflict resolution carried out in Subak Taman is by the methods of cooperation (cooperation), avoidance, competition, compromise, accommodation, domination, and problem solving. From the results of the study it can be concluded that there are six internal conflicts that occur in Subak Taman and conflict resolution is carried out using seven methods. The results of this study are expected to be a reference for those who need it.

Major respiratory diseases of goat and their epidemiology, prevention and control

Goats and sheep have the capacity to provide meat, milk, leather, and wool, making them valuable assets for the global livestock business, particularly in Southeast Asia, the Mediterranean region, and African nations. Respiratory diseases are the primary cause of death for these animals, accounting for about half of their deaths. Whatever their causes, goat and sheep infectious respiratory diseases account for 5.6% of all small ruminant diseases and disorders. Most of these have an infectious origin, particularly those that are bacterial, parasitic, fungal, or viral. PPR, caprine arthritis encephalitis, and bluetongue are the main viruses that cause respiratory diseases in goats. Through the use of a roadmap, the World Organization for Animal Health (WOAH) has established a global strategy for the eradication of PPR from the planet by 2030. Conversely, the two main bacterial causes of respiratory diseases are Pasteurella and Mycoplasma species, which can co-infect viral disorders or act as the primary cause. They can also exacerbate existing infections and lead to incorrect diagnoses. Numerous surveillances identified fungal infections, particularly Aspergillosis and Conidiobolomycosis, as well as parasite infections such as nasal myiasis and verminous pneumonia. In the review paper, we provide the most recent updated results on such diseases and their causative agents, taking into account elements such as distribution, transmission, spatiotemporal dynamics, possible risk factors, clinical signs and symptoms, treatment, avoidance, and control methods. Lastly, we offer some particular suggestions for addressing the respiratory conditions that affect goats. Bang. J. Livs. Res. Vol. 29 (1&2), 2022: P. 1-20

Dual-Horizon Reciprocal Collision Avoidance for Aircraft and Unmanned Aerial Systems

The aircraft conflict detection and resolution problem has been addressed with a wide range of centralised methods in the past few decades, e.g. constraint programming, mathematical programming or metaheuristics. In the context of autonomous, decentralized collision avoidance without explicit coordination, geometric methods provide an elegant, cost-effective approach to avoid collisions between mobile agents, provided they all share a same logic and a same view of the traffic. The Optimal Reciprocal Collision Avoidance (ORCA) algorithm is a state-of-the art geometric method for robot collision avoidance, which can be used as a Detect & Avoid logic on-board aircraft or Unmanned Aerial Vehicles. However, ORCA does not handle well some degenerate situations where agents operate at constant or near-constant speeds, which is a widespread feature of commercial aircraft or fixed-winged Unmanned Airborne Systems. In such degenerate situations, pairs of aircraft could end up flying parallel tracks without ever crossing paths to reach their respective destination. The Constant Speed ORCA (CS-ORCA) was proposed in 2018 to better handle these situations. In this paper, we discuss the limitations of both ORCA and CS-ORCA, and introduce the Dual-Horizon ORCA (DH-ORCA) algorithm, where two time horizons are used respectively for short-term collision avoidance and medium-term path-crossing. We show that this new approach mitigates the main issues of ORCA and CS-ORCA and yields better performances with dense traffic scenarios.

COLREGS-based collision avoidance algorithm for unmanned surface vehicles using modified artificial potential fields

In this paper, to improve the safety of navigation in dynamic environments, a new collision avoidance method is presented for Unmanned Surface Vehicles (USVs). It is mainly focused on dynamic obstacles and safety requirements. Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is introduced and the original artificial potential field (APF) algorithm is modified to ensure safe collision avoidance of USVs. The new attractive and repulsive potential field functions are designed and a new process of dynamic collision avoidance is constructed to solve the issue. At last, theoretical simulations and field tests are launched to validate our method. The simulation results show that the proposed method can perform safe navigation well. Through our USV platform, the effectiveness of the improved APF method for collision avoidance is also verified. Meanwhile, it is fast, effective, and deterministic.

An automated guided vehicle conflict-free scheduling approach considering assignment rules in a robotic mobile fulfillment system

Automated guided vehicle (AGV), as a material handling equipment, has been widely used in various applications, especially in the automated warehouse of manufacturing. The working efficiency of the warehouse is greatly influenced by the organization of the order picking process. As the picking tasks of AGVs increase in a certain period, conflicts and deadlocks of simultaneously working AGVs occur more frequently, which are also defecting factors of efficiency. In this paper, a multi-AGV scheduling approach considering assignment rules (MASA) is proposed, and a stage conflict avoidance method is designed to generate a conflict-free working route for AGVs in an automated warehouse with multiple workstations. Different from the random assignment rule of workstations in previous studies, the crowding-based assignment rule (CAR) and opening-time-based assignment rule (OTAR) are proposed to adjust the AGV working route and alleviate the congestion of AGV at the workstations. The scheduling model of AGVs is constructed to minimize the total completion time based on a space–time network, and a cooperative optimization algorithm is designed to solve the AGVs conflict-free scheduling problem. Numerical experiments verify the effectiveness and stability of the proposed algorithm. With the same working loads, CAR and OTAR result in a 5.55% and 9.56% decrease in the total completion time than the traditional random assignment rule, and the completion time is reduced by 8.01% and 12.13% in class-based tasks, respectively. MASA has great potential to improve the system efficiency of the warehouse when there are massive picking tasks and workstations.

Comparison of Velocity Obstacle and Artificial Potential Field Methods for Collision Avoidance in Swarm Operation of Unmanned Surface Vehicles

As the research concerning unmanned surface vehicles (USVs) intensifies, research on swarm operations is also being actively conducted. A swarm operation imitates the appearance of nature, such as ants, bees, and birds, in forming swarms, moving, and attacking in the search for food. However, several problems are encountered in the USV swarm operation. One of these is the problem of collisions between USVs. A conflict between agents in a swarm can lead to operational failure and property loss. This study attempted to solve this problem. In this study, a virtual matrix approach was applied as a swarm operation. Velocity obstacle (VO) and artificial potential field (APF) methods were used and compared as algorithms for collision avoidance for USVs in a swarm when the formation is changed. For effective collision avoidance, evasive maneuvers should be performed at an appropriate time and location. Therefore, a closest point of approach (CPA)-based method, which considers both temporal and spatial factors, was used. The swarm operation was verified through a large-scale simulation in which 30 USVs changed their formation seven times in 3400 s. When comparing the averages of the distance, error to waypoint, and battery usage, no significant differences were noticed between the VO and APF methods. However, when comparing the cumulative time using the minimum distance, VO was demonstrably safer than APF, and VO completed the formation faster. In conclusion, both the APF and VO methods can evidently perform swarm operations without collisions.

A Dynamic Obstacle Avoidance Method for AGV Based on Improved Speed Barriers

The complexity and difficulty of dynamic obstacle avoidance for AGVs are increased by the uncertainty in a dynamic environment. The adaptive speed obstacle method allows the size of the collision cone to be dynamically changed to solve this problem, but this method may cause the AGV to turn too much when it is close to obstacles, as the collision cone expands too fast, which may lead to unstable operations or even collision. In order to address these problems, we propose an improved speed obstacle algorithm. The proposed algorithm uses Kalman filtering to estimate the positions of dynamic obstacles and adopts the idea of forward simulation to build a speed obstacle buffer according to the estimated positions of obstacles, such that the AGV can use the predicted positions of obstacles in the next moment, instead of the current positions, to build a speed obstacle model. Finally, an objective function that balances efficiency and safety was established to score all the candidate speeds, such that the highest-rated speed could be selected as the candidate speed for the next moment.

The Vagueness of COLREG versus Collision Avoidance Techniques—A Discussion on the Current State and Future Challenges Concerning the Operation of Autonomous Ships

With the development of Maritime Autonomous Surface Ships (MASS), considerable research is undertaken to secure their safety. One of the critical aspects of MASS is collision avoidance, and multiple collision avoidance algorithms have been developed. However, due to various reasons, collision avoidance of autonomous merchant vessels appears to be far from resolved. With this study, we aim to discuss the current state of Collision Avoidance Methods (CAMs) and the challenges lying ahead—from a joint academic and practical point of view. To this end, the key Rules from International Regulations for Preventing Collisions at Sea (COLREG) have been reviewed with a focus on their practical application for MASS. Moreover, the consideration of the COLREG Rules in contemporary collision avoidance algorithms has been reviewed. The ultimate objective is to identify aspects of COLREG requiring additional attention concerning MASS developments in terms of collision avoidance. Our conclusions indicate that although a lot of progress has been achieved recently, the feasibility of CAMs for MASS remains questionable. Reasons for so are the ambiguous character of the regulations, especially COLREG, as well as virtually all existing CAMs being at best only partly COLREG-compliant.