Optimal Collision Research Articles

Collision Avoidance for Unmanned Surface Vehicles in Multi-Ship Encounters Based on Analytic Hierarchy Process–Adaptive Differential Evolution Algorithm

Path planning and collision avoidance issues are key to the autonomous navigation of unmanned surface vehicles (USVs). This study proposes an adaptive differential evolution algorithm model integrated with the analytic hierarchy process (AHP-ADE). The traditional differential evolution algorithm is enhanced by introducing an elite archive strategy and adaptively adjusting the scale factor F and the crossover factor CR to balance global and local search capabilities, preventing premature convergence and improving the search accuracy. Additionally, the collision risk index (CRI) model is optimized and combined with the quaternion ship domain, enhancing the precision of CRI calculations and USV autonomous collision avoidance capabilities. The improved CRI model, the International Regulations for Preventing Collisions at Sea, and the optimal collision avoidance distance were incorporated as evaluation factors in a fitness function assessment, with weights determined through the AHP to enhance the rationality and accuracy of the fitness function. The proposed AHP-ADE algorithm was compared with the improved particle swarm algorithm, and the performance of the algorithm was comprehensively evaluated using safety, economy, and operational efficiency. Simulation experiments on the MATLAB platform demonstrated that the proposed AHP-ADE algorithm exhibited better performance in scenarios involving multiple ship encounters, thus proving its effectiveness.

Multitask-Based Anti-Collision Trajectory Planning of Redundant Manipulators.

During performing multiple tasks of a redundant manipulator, the obstacles affect the sequential order of task areas and the joint trajectories. The end-effector is constrained to visit multiple task areas with an optimal anti-collision path, while the joints are required to move smoothly and avoid predefined obstacles. A special encoding genetic algorithm (SEGA) is proposed for multitask-based anti-collision trajectory planning. Firstly, the spatial occupancy relationship between obstacles and manipulator is developed utilizing the theory of spherical enclosing box and spatial superposition. The obstacles are detected according to the relative position relationship between linear segments and spheres. Secondly, each joint trajectory between adjacent task areas is depicted with a sixth-degree polynomial. Additionally, each joint trajectory is improved via optimizing the unknown six-order coefficient. By searching for optimal sequential order of task areas, optimal collision detection results, and optimal joint trajectories, the multitask-based anti-collision trajectory planning problem is transformed into a parameter optimization problem. In SEGA, the cost function consists of two parts, including the end-effector path length and the variation of joint angles. Moreover, each chromosome consists of three categories of genes, including the sequential order of task areas, the sequential order of joint configurations corresponding to task areas, and the unknown coefficients for anti-collision joint trajectories. Finally, numerical simulations are carried out to verify the proposed method.

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.

Online ligand screening for cytochrome C from herbal medicines through “four-in-one” measurement

Affinity-oriented online ligand screening with LC coupled to different detectors is widely popular to capture active compounds from herbal medicines (HMs). However, false-positive extensively occurs because insufficient information is recorded for the existence and stability of ligand-protein complex. Here, efforts were made to advance the hit confidences via configuring post-column infusion-LC–energy-resolved-affinity MS (PCI-LC–ER-AMS) to achieve “four-in-one” monitoring of: 1) response decrement of potential ligands; 2) response decrement of protein; 3) ions relating to ligand-protein complexes; and 4) ligand-protein binding strength. Ligand fishing for Cyt C from HMs was conducted as a proof-of-concept. For utility justification, a mimic sample containing twelve well-defined ligands and two negative controls underwent LC separation and met Cyt C prior to Qtof-MS measurements. Compared to Cyt C- or ligand-free assay, twelve ligands instead of negative controls showed response decrements that were consistent with twelve negative peaks observed at retention times corresponding to the ligands in Cyt C ion current chromatogram. Serial ions correlating to each ligand-Cyt C complex were observed. After recording breakdown graphs, optimal collision energy (OCE) corresponding to the non-covalent bond dissociation was positively correlated with binding strength. Two HMs including Scutellariae Radix (SR) and Aconiti Lateralis Radix Preparata were investigated. Consequently, 24 compounds were merely fished from SR, and particularly, flavonoid glycosides exhibited greater OCEs and also binding strengths over aglycones. Affinity assays and cellular evaluations consolidated the significant interactions between each captured compound and Cyt C. Overall, PCI-LC–ER-AMS is eligible for confidence-enhanced online ligand screening for Cyt C from HMs through “four-in-one” measurement.

USV formation navigation decision-making through hybrid deep reinforcement learning using self-attention mechanism

To address the challenging of balancing Unmanned Surface Vessel (USV) autonomous collision avoidance and formation maintenance in uncertain environments, a formation construction and navigation decision-making strategy based on Hybrid Deep Reinforcement Learning (HDRL) is proposed in this study. The novelty of this study is that: (1) A HDRL training approach is proposed, incorporating diverse DRL for virtual leader and followers, thereby significantly enhancing the decision-making adaptability of USVs formation. (2) The multi-head attention mechanism of decentralized Critic strategy is used to enhance the attentional focus of the HDRL algorithm towards different agents, thereby effectively improving convergence speed. (3) The method employs a meticulously designed hybrid reward function and incorporates the Optimal Reciprocal Collision Avoidance (ORCA) for speed selection, thereby providing optimal speed recommendations based on the current situation. It is worth emphasizing that the method exhibits exceptional performance in terms of success rate, navigation time, average reward value, and other relevant indicators within the simulation. Finally, the method is validated through the utilization of real-time navigation data in the Panama Canal, thereby substantiating the potential engineering applicability.

A Reverse Thinking Based on Partially Methylated Aldononitrile Acetates to Analyze Glycoside Linkages of Polysaccharides Using Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry.

Glycoside linkage analyses of medicine and food homologous plant polysaccharides have always been a key point and a difficulty of structural characterization. The gas chromatography-mass spectrometry (GC-MS) method is one of the commonly used traditional techniques to determine glycoside linkages via partially methylated alditol acetates and aldononitrile acetates (PMAAs and PMANs). Due to the simplicity of derivatization and the highly structural asymmetry of PMANs, reverse thinking is proposed using liquid chromatography-electrospray ionization-multiple reaction monitoring mass spectrometry (LC-ESI-MRM-MS) for the first time to directly determine the neutral and acidic glycosyl linkages of polysaccharides. The complete characterization of glycoside linkages deduced from PMANs was achieved using a combination of tR values, characteristic MRM ion pairs, diagnostic ESI+-MS/MS fragmentation ions (DFIs), and optimal collision energy (OCE). The DFI and OCE parameters were confirmed to be effective for the auxiliary discrimination of some isomers of the PMANs. The practicality of LC-ESI+-MRM-MS was further verified by analyzing the glycoside linkages of polysaccharides in five medicine and food homologous plants. This method can serve as an alternative to GC-MS for the simultaneous determination of neutral and acidic glycosyl linkages in polysaccharides.

Quantitative comparison of bile acid glucuronides sub-metabolome between intrahepatic cholestasis and healthy pregnant women.

Because of the pathological indication and the physiological functions, bile acids (BAs) have occupied the research hotspot in recent decades. Although extensive efforts have been paid onto BAs sub-metabolome characterization, as the subfamily, BA glucuronides (gluA-BAs) profile is seldom concerned. Here, we made efforts to develop a LC-MS/MS program enabling quantitative gluA-BAs sub-metabolome characterization and to explore the differential species in serum between intrahepatic cholestasis of pregnancy (ICP) patients and healthy subjects. To gain as many authentic gluA-BAs as possible, liver microsomes from humans, rats, and mice were deployed to conjugate glucuronyl group to authentic BAs through in vitro incubation. Eighty gluA-BAs were captured and subsequently served as authentic compounds to correlate MS/MS spectral behaviors to structural features using squared energy-resolved MS program. Optimal collision energy (OCE) of [M-H]->[M-H-176.1]- was jointly administrated by [M-H]- mass and glucuronidation site, and identical exciting energies corresponding to 50% survival rate of 1st-generation fragment ion (EE50) were observed merely when the aglycone of a gluA-BA was consistent with the suspected structure. Through integrating high-resolution m/z, OCE, and EE50 information to identify gluA-BAs in a BAs pool, 97 ones were found and identified, and further, quantitative program was built for all annotated gluA-BAs by assigning OCEs to [M-H]->[M-H-176.1]- ion transitions. Quantitative gluA-BAs sub-metabolome of ICP was different from that of the healthy group. More GCDCA-3-G, GDCA-3-G, TCDCA-7-G, TDCA-3-G, and T-β-MCA-3-G were distributed in the ICP group. Above all, this study not only offered a promising analytical tool for in-depth gluA-BAs sub-metabolome characterization, but also clarified gluA-BAs allowing the differentiation of ICP and healthy subjects.

Pengaruh Variasi Jumlah Tumbukan Pada Perkerasan Hrs-Wc Dengan Metode Marshall Test

Roads are one of the transportation infrastructures that are really needed for daily life, so during their service period it is hoped that the condition of the road will be durable according to its intended lifespan. One of the influences that makes an asphalt mixture good is the compaction carried out during implementation. The compaction assessment is expected to determine the density and strength of the asphalt layer through the marshall limit to obtain the ideal strength value for the asphalt mixture, so it is important to know the ideal number of collisions during implementation. . The results of tests on the effect of variations in the number of collisions on marshall characteristics that have been carried out produce values ​​that meet the specifications set by SNI Bina Marga 2018 revision 2, including tests that obtained a stability value of 2086.99kg, a flow value of 3.55 mm, a VIM value of 5.84% , the VMA value is 18.69%, the VFB value is 68.73%, and the MQ value is 587.89 kg/mm, so that an optimum collision of 60 collisions is obtained. Thus it can be stated that it meets the HRS-WC specifications.

Relaxing the Limitations of the Optimal Reciprocal Collision Avoidance Algorithm for Mobile Robots in Crowds

Relaxing the Limitations of the Optimal Reciprocal Collision Avoidance Algorithm for Mobile Robots in Crowds

A collision-avoidance decision-making scheme based on artificial potential fields and event-triggered control

In this paper, a collision-avoidance decision-making scheme is proposed based on improved artificial potential field (APF) and event-triggered nonlinear model predictive control. A calculation model is used to calculate the collision avoidance dynamic parameters between ships. In accordance with relevant rules of the International Regulations for Preventing Collisions at Sea (COLREGs), abovementioned collision avoidance parameters are used as the main index and applied to construct the repulsive potential field. Then, an anti-collision decision-making scheme is developed by incorporating event-triggered mechanism and improved APF method into the nonlinear model predictive control algorithm. Based on improved repulsive potential field, heading of the ship and orientation of the target point, the event-triggered conditions are set to save considerable computational resources. Following this approach, optimal collision avoidance actions in line with COLREGs is realised, and the adverse effects of ignoring the manoeuvrability characteristics of ships in the process of preventing collisions are eliminated. Finally, simulation examples verify the effectiveness and rationality of the proposed scheme for multi-ship encounter scenarios.

A Collision Avoidance Strategy Based on Entropy-Increasing Risk Perception in a Vehicle–Pedestrian-Integrated Reaction Space

Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception in a vehicle–pedestrian reaction space. Our approach combines a limited range of reaction space regions with entropy to quantify the risk of pedestrian–vehicle collision. Then, multi-vehicle candidate trajectories are generated using the path and speed sequence method, and the uncertain states of pedestrians are predicted based on the social force model and Markov model accordingly. Finally, to determine the optimal collision avoidance trajectory, we use quantitative reaction-space entropy as a new “cost function” to measure potential risk and perform multi-objective trajectory optimization based on the elitist non-dominated-sorting genetic algorithm region-focused (NSGA-RF) approach. Simulation results show that our proposed strategy can enhance the safety of the planned trajectory interaction between vehicles and pedestrians for autonomous driving under normal and emergency conditions.

D Matrix for Keplerian State Error Propagation

The D matrix is an error state transition matrix (STM) for Keplerian motion in cylindrical coordinates employing time as a state variable and reference true or eccentric anomaly as an independent variable. It has been only partially derived in previous articles and applied to different astrodynamics problems such as optimal collision avoidance and Lambert targeting. A rigorous and complete derivation of the full matrix in analytical form is presented. In addition, it is shown that the different terms of the proposed STM can lead to the definition of new variational equations and optimal steering laws applicable to impulsive and low-thrust orbit transfer optimization as well as reachability analysis. Finally, the effectiveness of the proposed STM is evaluated against the well-known Yamanaka–Ankersen STM showing more than one order of magnitude error reduction associated to the semimajor axis and eccentricity of the linearly propagated relative state.

Collision avoidance control for limited perception unmanned surface vehicle swarm based on proximal policy optimization

In order to ensure the safe and coordinated operation of unmanned surface vehicle (USV) swarm in complex marine environments, the primary problem is collision avoidance control (CAC). However, the limited perception, environmental uncertainty and multi-source complexity bring significant challenges to the efficient collaboration and CAC of the USV swarm. To overcome the above challenges, this paper aims to propose a distributed CAC method for USVs based on the proximal policy optimization (PPO). This method does not necessitate precise system models and is capable of autonomous learning, effectively adapting to unknown environments. In terms of CAC, unlike designing reward functions based solely on the distance from obstacles, we additionally consider the velocity of obstacles, and combine optimal reciprocal collision avoidance (ORCA) to design a reward function. We further consider the limited perception range of USVs and construct a bidirectional gated recurrent unit (BiGRU) network to extract features of variable length observation data, effectively overcoming the problem of dimensionality in observable data. Moreover, we construct a high-quality USV swarm simulation environment using the Gazebo 3D physics engine, which is used for testing the generalization capability of collision avoidance policy. Finally, to verify the effectiveness of the policy learning and optimization, a series of experiments are conducted in various scenarios, network architectures, and control methods. The experimental results indicate that our approach has remarkable superiority in terms of travel time, average velocity, average reward, and success rate.

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques.

A strategy of combining energy‐resolved mass spectrometry and density functional theory for the recognition of forsythoside isomers

RationaleIsomerism is very common in nature, and the mass spectrometry (MS) behavior of isomers is highly similar, making their spectral analysis and identification difficult. Computational chemistry can effectively assist the study of MS spectra and molecule structures. Herein, three isomers of forsythoside (For‐A, For‐H, and For‐I) were successfully identified using energy‐resolved MS combined with density functional theory (DFT).MethodsThe MS scan spectra of the isomers were collected in electrospray ionization MS. The possible positive/negative ion adduct sites were investigated using the DFT method, and the molecular formation energy of each precursor ion was obtained. In the multireaction monitoring mode, the spectra of the common precursor ion → product ion pair of the three isomers were acquired under different collision energy, and the optimal collision energy (OCE) and maximum relative intensity (MRI) of each product ion were obtained. The energy of the broken chemical bond was calculated.ResultsThe experimental data showed that the product ions of [M + H]+ of the three isomers have the greatest differences in OCE and MRI and were the most suitable to distinguish the three isomers. The DFT data showed that there was a good positive correlation between the bond fragmentation energy calculated by the quasi‐molecular ion model and OCE. The results indicated that the energy of precursor ions and corresponding fragments could be correlated with their experimental spectra.ConclusionsA strategy of combining energy‐resolved MS and DFT was implemented on the recognition of forsythoside isomers, and this strategy was expected to be applied to the identification of more isomers.

Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine.

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).

Optimal lane-changing and collision avoidance strategy for intelligent vehicles in high-speed driving environments

This paper proposes an optimal lane-changing and control strategy to solve the collision avoidance of intelligent vehicles in highway driving. A quintic polynomial lane-changing trajectory model is established in the Frenet frame, and the trajectory planning problem is transformed into the optimisation problem of lane-changing target coordinates. To consider the multiple environmental factors on vehicle lane-changing, the feasible region of the lane-changing target point was generated based on the lateral acceleration limitation and the safe distance constraints. The performance evaluation indexes were established, considering efficiency, comfort, lateral slip, and lane offset. The multi-objective optimisation problem is solved in the feasible region, and the optimisation results were applied to the collision detection process. In addition, considering the nonlinear characteristics of tire force caused by sudden lateral movements of vehicles at high-speed, a vehicle dynamics model based on the Magic Formula is established, and Model Predictive Control is applied to track the expected trajectory. Simulation results show that this strategy can plan suitable lane-changing trajectories in high-speed working conditions and achieve vehicle collision avoidance.

Application of Skyline software for detecting prohibited substances in doping control analysis.

As the number of prohibited drugs has been progressively increasing and analytical methods for detecting such substances are renewed continuously for doping control, the need for more sensitive and accurate doping analysis has increased. To address the urgent need for high throughput and accurate analysis, liquid chromatography with tandem mass spectrometry is actively utilized in case of most of the newly designated prohibited substances. However, because all mass spectrometer vendors provide data processing software that is incapable of handling other instrumental data, it is difficult to cover all doping analysis procedures, from method development to result reporting, on one platform. Skyline is an open-source and vendor-neutral software program invented for the method development and data processing of targeted proteomics. Recently, the utilization of Skyline has been expanding for the quantitative analysis of small molecules and lipids. Herein, we demonstrated Skyline as a simple platform for unifying overall doping control, including the optimization of analytical methods, monitoring of data quality, discovery of suspected doping samples, and validation of analytical methods for detecting newly prohibited substances. For method optimization, we selected the optimal collision energies for 339 prohibited substances. Notably, 195 substances exhibited a signal intensity increase of >110% compared with the signal intensity of the original collision energy. All data related to method validation and quantitative analysis were efficiently visualized, extracted, or calculated using Skyline. Moreover, a comparison of the time consumed and the number of suspicious samples screened in the initial test procedure highlighted the advantages of using Skyline over the commercially available software TraceFinder in doping control.

The relationship between and eccentricities based on Glauber model

This report aims to determine the probability range of the number of collisions under the optimum collision condition. The optimum condition was obtained by determining the average values of the given variables in the Glauber Model. Graphs of against eccentricity 1, 2, and 3 (Ecc1, 2, and 3) were plotted respectively according to the data generated from the Glauber Model Simulation in CERN ROOT. Then, the optimum average value was plotted as a vertical line on the graph to determine the range of . The probability ratio of an optimum collision versus maximum probability in an event was concluded to fall in a certain range of 0.30 0.07, and this range is verified to be a stable range that could be used for prediction of optimum collision numbers in future nuclei collision experiment. This probability ratio can be used to predict the optimum collision with only provided.

Coordinated multi-stage and multi-objective optimization approach for ship collision avoidance decision-making

Appropriate decision-making is essential to prevent ship collisions and further ensure navigational safety at sea. Current research on collision avoidance decision-making (CADM) primarily aims to find limited feasible solutions based on a designated safe distance under the strong assumption that ships take no or little maneuvering actions. To overcome such limitations, a multi-stage and multi-objective CADM optimization approach is proposed to satisfy the requirements of safety and economy, while considering the ship path uncertainties and risk preferences of ship officers. First, a comprehensive collision risk estimation method is formulated by quantifying degree of domain violation and probability of domain violation (PDV) in dynamic and uncertain situations. Then, the CADM for each individual ship is treated as a multi-objective optimization problem, and a three-stage optimization approach outlines the process to find optimal collision avoidance path. Preliminary non-dominated solutions are obtained and filtered to enhance the robustness by an accepted threshold of the PDV. Optimal solutions are selected for ship officers with different risk preferences. Finally, the coordinated collision avoidance path for multi-ship encounter is determined based on the exchange of individual collision avoidance path through several rounds of coordination. The effectiveness of CADM method is validated through extensive case studies. The results show that it can provide feasible solutions for multi-objectives and adaptively determine the most appropriate scheme for ship officers with different risk preferences to make coordinated decisions.