Avoidance Method Research Articles

Formation collaborative obstacle avoidance with multiple UGVs in restricted environments based on adaptive DWA

Multiple unmanned ground vehicles (UGVs) can collaborate to complete tasks in complex scenarios and are increasingly attracting attention due to their wide applications. Developing a formation cooperative obstacle avoidance policy for multiple UGVs in restricted environments is still challenging. To deal with the problem, we propose a formation cooperative obstacle avoidance method based on adaptive DWA called Multi-ADWA. First, the adaptive DWA algorithm is proposed to address the global navigation task for a single UGV, which dynamically adjusts the trajectory prediction time of the DWA algorithm according to the danger level of the current environment and the density of obstacles. To solve the deadlock problem between UGVs, a dual-priority mechanism is proposed to coordinate the motion order of UGVs. Then, a distributed optimization method is proposed to compute the optimal formation configuration under byte-level shared information. It provides each follower with its easy-to-reach desired goal in real time to improve the formation recovery efficiency. Furthermore, an event-triggered behavior switching strategy is designed and combined with the leader–follower method to make multiple UGVs system to coordinate global navigation, local obstacle avoidance, and formation maintenance. Finally, the numerical simulation and virtual simulation experiments are conducted to verify the effectiveness and superiority of Multi-ADWA. Moreover, a real-world experiment further proves the effectiveness of our approach in practical applications.

A storyline approach: integrating comprehensive, interdisciplinary research results to create narratives – in the context of the net-zero target in Germany

With the amendment to the German Climate Change Act in 2021, the Federal Government of Germany has set the target to become greenhouse gas neutral by 2045. Reaching this ambitious target requires multisectoral efforts, which in turn calls for interdisciplinary collaboration: the Net-Zero-2050 project of the Helmholtz Climate Initiative serves as an example of successful, interdisciplinary collaboration with the aim of producing valuable recommendations for action to achieve net-zero CO2 emissions in Germany. To this end, we applied an interdisciplinary approach to combining comprehensive research results from ten German national research centers in the context of carbon neutrality in Germany. In this paper, we present our approach and the method behind the interdisciplinary storylines development, which enabled us to create a common framework between different carbon dioxide removal and avoidance methods and the bigger carbon neutrality context. Thus, the research findings are aggregated into narratives: the two complementary storylines focus on technologies for net-zero CO2 emissions and on different framing conditions for implementing net-zero CO2 measures. Moreover, we outline the Net-Zero-2050 results emerging from the two storylines by presenting the resulting narratives in the context of carbon neutrality in Germany. Aiming at creating insights into how complementary and related expertise can be combined in teams across disciplines, we conclude with the project’s lessons learned. This paper sheds light on how to facilitate cooperation between different science disciplines with the purpose of preparing joint research results that can be communicated to a specific audience. Additionally, it provides further evidence that interdisciplinary and diverse research teams are an essential factor for defining solution spaces for complex, interdisciplinary problems.

Research on autonomous obstacle avoidance and automatic obstacle avoidance of minimally invasive surgical robots

Abstract. With the development of modern society, people pay more and more attention to health and safety, especially to the development of medical techniques. The demand for higher medical standards has led to minimally invasive surgery becoming a better choice for patients. Minimally invasive surgeries involve less trauma, lower blood loss, quicker recovery, and a lower risk of infection. However, compared to traditional surgeries, these surgeries demand higher precision and skill from the surgeons, making surgical robots the preferred tool for most minimally invasive operations. Surgical robots can eliminate the surgeon's hand tremors during the operation, enhance the precision of surgical operations, and offer greater flexibility with their small end size inside human organs. Despite these advantages, minimally invasive surgical robots can not avoid obstacles and plan the path of human organs and cavities in real-time. This paper reviews the dynamic path planning and real-time obstacle avoidance of modern minimally invasive surgical robots, including their development progress and research directions. It also introduces in detail the mainstream research methods of path planning and real-time obstacle avoidance in modern research. Finally, the current technical challenges and the prediction of the future research direction are pointed out and the reference suggestions are provided.

Piecewise Time Polynomials-Based Control Methods for Obstacle Avoidance and Precision Positioning of Tower Crane Systems with Varying Cable Lengths

During the hoisting and lowering operations of a tower crane, dynamic variations in cable lengths significantly influence the oscillation frequency and amplitude of the load. These variations complicate the oscillation characteristics, heightening the challenge of balancing obstacle avoidance with precise positioning. To tackle this issue, we propose a trajectory planning and tracking control method that integrates hoisting control to reduce the impact of varying cable lengths on load swinging and achieve accurate positioning during obstacle navigation. A novel definition of swing angle is introduced to model the crane’s rigid and swinging components separately, enhancing model accuracy while simplifying complexity. A piecewise polynomial constructs the load trajectory in a low-dimensional flat space, which is then mapped to a high-dimensional generalized state space through a homeomorphic transformation, ensuring trajectory smoothness and traceability. A fractional-order sliding mode controller is employed to facilitate rapid and precise tracking of the actuated degrees of freedom, suppressing load oscillation while maintaining positioning accuracy. Experimental validation on a tower crane platform shows that the proposed strategy enables smooth obstacle avoidance and precise target point reaching, even with varying cable lengths.

An Analysis of Nonprofit Organization Federal Income Tax Avoidance Using Publicly Available Income Tax Return Data

ABSTRACT Nonprofit organizations are subject to an Unrelated Business Income Tax (UBIT) on activities unrelated to their tax exempt purpose. In 2007 nonprofits became the first U.S. federal taxpayers required to publicly disclose their income tax returns. Using this data I find that nonprofits avoid the UBIT by combining losses from some activities to offset the profits of other activities. A provision in the Tax Cut and Jobs Act of 2018 was effective in mitigating the use of loss activities as a UBIT avoidance method. I also find that nonprofits continue to avoid the UBIT by shifting common costs from tax exempt to taxable activities. I find that UBIT avoidance is increasing in tax rates. Finally, tax avoidance is smaller for the set of nonprofits organized as charitable trusts rather than as corporations or associations, and that public donors play a governing role by mitigating UBIT avoidance. Data Availability: Data are available from sources identified in the text. JEL Classifications: H26; L30.

A Review of UAV Path-Planning Algorithms and Obstacle Avoidance Methods for Remote Sensing Applications

The rapid development of uncrewed aerial vehicles (UAVs) has significantly increased their usefulness in various fields, particularly in remote sensing. This paper provides a comprehensive review of UAV path planning, obstacle detection, and avoidance methods, with a focus on its utilisation in both single and multiple UAV platforms. The paper classifies the algorithms into two main categories: (1) global and local path-planning approaches in single UAVs; and (2) multi-UAV path-planning methods. It further analyses obstacle detection and avoidance methods, as well as their capacity to adapt, optimise, and compute efficiently in different operational environments. The outcomes highlight the advantages and limitations of each method, offering valuable information regarding their suitability for remote sensing applications, such as precision agriculture, urban mapping, and ecological surveillance. Additionally, this review also identifies limitations in the existing research, specifically in multi-UAV frameworks, and provides recommendations for future developments to improve the adaptability and effectiveness of UAV operations in dynamic and complex situations.

Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning

Abstract The autonomous navigation and obstacle avoidance capabilities of autonomous underwater vehicles (AUVs) are essential for ensuring their safe navigation and long-term, efficient operation. However, the complexity of the marine environment poses significant challenges to safe and effective obstacle avoidance. To address this issue, this study proposes an AUV obstacle avoidance control algorithm based on offline reinforcement learning. This method adopts the Conservative Q-learning (CQL) algorithm, which is based on the Soft Actor-Critic (SAC) framework. It learns from obtained historical obstacle avoidance data and ultimately achieves a favorable obstacle avoidance control strategy. In this method, PID and SAC control algorithms are utilized to generate expert obstacle avoidance data to construct a diversified offline database. Additionally, based on the line-of-sight (LOS) guidance method and artificial potential field (APF) method, information regarding the distance and orientation of targets and obstacles is incorporated into the state space, and heading and obstacle avoidance reward terms are integrated into the reward function design. The algorithm successfully guides the AUV in autonomous navigation and dynamic obstacle avoidance in three-dimensional space. Furthermore, the algorithm exhibits a certain degree of anti-interference capability against uncertain disturbances and ocean currents, enhancing the safety and robustness of the AUV system. Simulation results fully demonstrate the feasibility and effectiveness of the intelligent obstacle avoidance method based on offline reinforcement learning. This study highlights the profound significance of offline reinforcement learning in enabling robust and reliable control systems for AUVs, paving the way for enhanced operational capabilities in challenging marine environments.

Comparison and mathematical modeling of surge avoidance methods in turbojet engine compressor

Comparison and mathematical modeling of surge avoidance methods in turbojet engine compressor

Risk Prediction and Prevention Strategies Used by the Financial Industry Are Supported by Artificial Intelligence Technologies

With the advancement of artificial intelligence (AI) and big data technology, traditional financial management faces challenges in meeting modern needs. China's financial sector, although late to start, is embracing AI to mitigate risks like business and credit risks. This study explores risk prediction and avoidance methods in finance using AI. It employs elementary catastrophe theory to analyze dynamic system discontinuity, identifying high-risk periods when the calculated financial stress index (FSI) exceeds 1. Each unit increase in FSI affects its lag period by 0.7327 units. Macro-economic pressure, reflected in the macroeconomic pressure index (MSI), significantly impacts FSI, with the current period having a more pronounced effect than the lag period. Financial institutions must evolve talent into hybrid professionals and invest in AI to enhance risk management while pursuing profitability.

A game-based decision-making method for multi-ship collaborative collision avoidance reflecting risk attitudes in open waters

To accurately reflect risk attitudes towards ship intentions in multi-ship encounters, this paper develops a novel two-stage collaborative collision avoidance decision-making (CADM) model by incorporating intention prediction and real-time decision-making. We acquire prior knowledge of risk attitudes by analyzing Automatic Identification System (AIS) data and further estimate the probability distributions of encountering ship's risk attitude using Bayesian reasoning. By treating collision avoidance procedure as a static game with incomplete information, a predictive model for collision avoidance intentions is developed by taking account into risk attitude probabilities. Real-time decisions are then implemented according to different stages, and a collaborative CADM model is established by a game-decision cycle. Finally, a multi-ship encounter scenario is simulated under all combinations of risk attitudes, and the results are compared with those obtained under complete information. The results demonstrate that the proposed model can formulate avoidance actions that meet safety requirements under all combinations of risk attitudes. Further comparison with complete information proves the effectiveness of the risk attitude probability model, which is conducive to improving the decision-making flexibility and reducing complexity. The research findings enhance the collaborative decision-making, contributing to the development of autonomous navigation in open waters.

Leader-follower control and APF for Multi-USV coordination and obstacle avoidance

To overcome the limitations of a single USV in task execution, this paper investigates the issues of cooperative motion and obstacle avoidance for USV swarm. The leader-follower control approach is chosen as the strategy for coordinating the formation of multiple USVs. Kinematic and kinetic controllers are designed using sliding mode control, with a saturation function replacing the sign function to avoid chattering. The validity of the kinematic and kinetic controllers is demonstrated using the Lyapunov stability theorem. To tackle the problem of goal reachability when obstacles are nearby in the artificial potential field method for obstacle avoidance, the repulsive potential field function is modified by including the distance factor between the USV and the target point. To address the problem of local minimum, the simulated annealing algorithm is incorporated into the traditional artificial potential field method. Multi-condition simulation experiments are conducted using MATLAB. The experimental results demonstrate that multiple USVs can form a formation in a short time and maintain the formation while navigating, exhibiting high robustness. Furthermore, they are able to effectively navigate through complex marine environments, successfully avoiding obstacles and ultimately reaching their target destination.

Security Analysis of Smart Contract Migration from Ethereum to Arbitrum

When migrating smart contracts from one blockchain platform to another, there are potential security risks. This is because different blockchain platforms have different environments and characteristics for executing smart contracts. The focus of this paper is to study the security risks associated with the migration of smart contracts from Ethereum to Arbitrum. We collected relevant data and analyzed smart contract migration cases to explore the differences between Ethereum and Arbitrum in areas such as Arbitrum cross-chain messaging, block properties, contract address alias, and gas fees. From the 36 types of smart contract migration cases we identified, we selected four typical types of cases and summarized their security risks. The research shows that smart contracts deployed on Ethereum may face certain potential security risks during migration to Arbitrum, mainly due to issues inherent in public blockchain characteristics, such as outdated off-chain data obtained by the inactive sequencer, logic errors based on time, failed permission checks, and denial of service (DOS) attacks. To mitigate these security risks, we proposed avoidance methods and provided considerations for users and developers to ensure a secure migration process. It is worth noting that this study is the first to conduct an in-depth analysis of the secure migration of smart contracts from Ethereum to Arbitrum.

A novel collaborative collision avoidance decision method for multi-ship encounters in complex waterways

With the continuous growth of maritime traffic volume, multi-ship collision avoidance has gradually become one of the most crucial issues in ensuring the safety of ship navigation. This study proposes a multi-ship collaborative collision avoidance decision model (MCCA) to address the issue of multi-ship encounters in complex waterways. The model takes into account the ship spatio-temporal collision risk, relative motion trends, as well as the safety and economic considerations of ship navigation. A comprehensive collision avoidance method is designed based on the velocity obstacle method, enabling simultaneous adjustments both course and speed, with the aim of minimizing economic losses incurred by ships due to avoidance operations while ensuring ship navigation safety. Meanwhile, the NSGA-II algorithm, which has faster convergence and better effect, is used to calculate the optimal collaborative collision avoidance decision of multiple ships. Besides, both simulation and example validation are used to test the validity of the model. The results indicate that the proposed model can effectively achieve multi-ship collaborative collision avoidance in the region. Simultaneously, it holds significant reference value for guaranteeing the safety of ship navigation, enhancing supervision efficiency, and promoting the development of intelligent ship navigation systems.

Automobile Intelligent Vehicle-machine and Human-computer Interaction System based on Big Data

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable.

Autonomous collision avoidance decision-making method for USV based on ATL-TD3 algorithm

To address the issue of decision-making for ship collision avoidance in complex encounter situations, an Autonomous Collision Avoidance Decision-making (ACAD) model based on Attention Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (ATL-TD3) algorithm for Unmanned Surface Vessel (USV) is proposed in this study. The novelty of this method is: (1) Incorporating LSTM and multi-head self-attention mechanism into the existing TD3 algorithm network enhances its focus on pertinent historical state information. Furthermore, the generalization ability of the trained model is further enhanced through optimization of the accumulated experience. (2) A comprehensive reward mechanism is designed, incorporating ship domain, arena, yaw, and other relevant factors, which solve the encounters defined in clauses 13–17 in Chapter 2 of COLREGs and immediate danger. The experiment involved a comparative analysis of the average reward curve and success rate during training under different environments. Additionally, 20 cases of Imazu classic encounters and real navigation data from the Suez Canal were utilized to validate the proposed algorithm. It is highlight that the ACAD model trained by the ATL-TD3 algorithm exhibits remarkable performance in the generalization verification, surpassing conventional collision avoidance methods.

Enhanced Multi-UAV Formation Control and Obstacle Avoidance Using IAAPF-SMC

In response to safety concerns pertaining to multi-UAV formation flights, a novel obstacle avoidance method based on an Improved Adaptive Artificial Potential field (IAAPF) is presented. This approach enhances UAV obstacle avoidance capabilities by utilizing segmented attraction potential fields refined with adaptive factors and augmented with virtual forces for inter-UAV collision avoidance. To further enhance the control and stability of multi-UAV formations, a Sliding Mode Control (SMC) method is integrated into the IAAPF-based obstacle avoidance framework. Renowned for its robustness and ability to handle system uncertainties and disturbances, the SMC method is combined with a feedback control system that utilizes inner and outer loops. The outer loop generates the desired path based on the leader’s state and control commands, while the inner loop tracks these trajectories and adjusts the follower UAVs’ motions. This design ensures that obstacle feedback is accounted for before the desired state information is received, enabling effective obstacle avoidance while maintaining formation integrity. Integrating leader-follower formation control techniques with SMC-based multi-UAV obstacle avoidance strategies ensures the effective convergence of the formation velocity and spacing to predetermined values, meeting the cooperative obstacle avoidance requirements of multi-UAV formations. Simulation results validate the efficacy of the proposed method in reaching otherwise unreachable destinations within obstacle-rich environments, while ensuring robust collision avoidance among UAVs.

Extended Floor Field Cellular Automata Model for Pedestrian Emergency Evacuation Considering Panic Emotion

Panic emotion appears frequently in an emergency evacuation, affecting evacuation efficiency and pedestrian safety. Individual diversity in emotion propagation causes pedestrians to behave differently. To better describe the emotion propagation in the movement of pedestrians, we analyze the characteristics of the emotions of different pedestrians in emergencies. Considering pedestrian heterogeneity, we divide pedestrians into three types (conservative, moderate, and radical) according to the strength of the expression of individual emotions. Based on an analysis of the characteristics of the three types of pedestrian, an improved susceptible-infected-recovered (SIR) model is built to describe the pedestrian emotion propagation process. In addition, a conflict avoidance method that considers pedestrian speed is proposed to demonstrate pedestrian motion more realistically. Based on this foundation, a cellular automata (CA) model that integrates emotion propagation and conflict avoidance techniques is employed to replicate the process of pedestrian emergency evacuation. The simulation results suggest that a higher proportion of pedestrians with initial panic emotions and a radical behavior type leads to a prolonged evacuation time. Many pedestrians in emergency evacuations gather at the exit in a short time (approximately 37 time steps), the density then reaches a high-level (approximately 5.5 pedestrians/m2) for a period of time, and the duration increases with the increase in the number of pedestrians. Methods for guiding pedestrians to evacuate in a rational manner are worthy of promotion and research.

Integrated design method for protection against vibration of offshore platform plate structure

The paper integrates the modal avoidance method, pedestal design method, and dynamic vibration absorber theory to investigate vibration control technology for offshore platform plate structures. We develop an integrated design method for vibration suppression by controlling the vibration excitation load, vibration transmission, and vibration energy dissipation. Firstly, a modal avoidance design is implemented to suppress vibration transmission along the propagation path of the plate structure. Subsequently, pedestal optimization is conducted for pedestal structure to attenuate vibration excitation at the input end. Finally, dynamic vibration absorber theory is employed to control dominant vibration frequency responses further and achieve multi-target vibration control. Case simulation results demonstrate that the integrated design method reduces the acceleration vibrations level at 113.75, 145.61, and 153 Hz, and this method could guide multi-objective vibration control in offshore platform plate structures.

Monocular visual obstacle avoidance method for autonomous vehicles based on YOLOv5 in multi lane scenes

This study explores a more effective obstacle avoidance method for autonomous driving based on the monocular vision system of YOLOv5. The study utilizes the YOLOv5 model to detect obstacles and road signs in the environment in real-time, including vehicles, pedestrians, traffic signals, etc., identifying objects of different sizes and angles, providing accurate obstacle avoidance decision inputs. Then, we use a path planning algorithm based on deep reinforcement learning to combine the detected obstacle information with the current state of the vehicle, dynamically generating safe and efficient driving paths. In order to further improve the obstacle avoidance effect, a monocular visual autonomous driving obstacle avoidance aggregation network was introduced in the study, and the MMA (Multi lane monocular visual autonomous driving) obstacle avoidance method was established. The future movement trend of obstacles was predicted by setting an improved monocular obstacle avoidance loss function to sense historical data and the environment. The experimental results show that MMA has significantly enhanced obstacle avoidance and driving efficiency. The accuracy of the MMA obstacle avoidance method fluctuates between 78.76 % and 88.26 %, showing the best overall performance. The accuracy also shows an upward trend with the increase of the data sample size.

Multi-robot collision avoidance method in sweet potato fields.

Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency. One of the main challenges faced by multi robot navigation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting position to the expected target. In this article, we propose a low-cost multi-robot collision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts the possibility of collision based on the robot's position and environmental information, and changes the robot's path in advance, instead of waiting for the robot to make a collision avoidance decision when it is closer. Finally, we demonstrate that a multi-robot collision avoidance approach provides an excellent solution for safe and effective autonomous navigation of a single robot working in complex sweet potato fields. Our collision avoidance method allows the robot to move forward effectively in the field without getting stuck. More importantly, this method does not require expensive hardware and computing power, nor does it require tedious parameter tuning.