Search Results (14)

This paper presents a hybrid path planning method that integrates an enhanced A-Star algorithm with the Dynamic Window Approach (DWA). The proposed approach addresses the limitations of conventional A-Star algorithms in global path planning, particularly their inability to adaptively avoid obstacles in real-time. To improve navigation safety, the A-Star search strategy is enhanced by avoiding paths that intersect with obstacle vertices or pass through narrow channels. Additionally, a node optimization technique is introduced to remove redundant nodes by checking for collinearity in consecutive nodes. This optimization reduces the path length and ensures that the path maintains a safe distance from obstacles using parallel lines. An advanced Bézier curve smoothing method is also proposed, which adaptively selects control points to improve path smoothness and driving stability. By incorporating these improvements, the enhanced A-Star algorithm is combined with DWA to facilitate dynamic obstacle avoidance while generating global paths. The method accounts for the kinematic characteristics of the USV, as well as physical constraints such as linear and angular velocities, enabling effective handling of obstacles in dynamic environments and ensuring safe navigation. Simulation results demonstrate that the proposed algorithm generates secure global paths, significantly optimizing node count, path length, and smoothness, while effectively avoiding dynamic obstacles, thus ensuring safe navigation of the USV. Full article

This work investigates the possibility to improve the computational efficiency of a set-based method for the trajectory planning of a car-like vehicle through artificial intelligence. Planning is performed on a graph that represents the operating scenario in which the vehicle moves, and the kinodynamic feasibility of the trajectories is guaranteed through a series of set-based arguments, which involve the solution of semi-definite programming problems. Navigation in the graph is performed through a hybrid A* algorithm whose performance metrics are improved through a properly trained classificator, which can forecast whether a candidate trajectory segment is feasible or not. The proposed solution is validated through numerical simulations, with a focus on the effects of different classificators features and by using two different kinds of artificial intelligence: a support vector machine (SVM) and a long-short term memory (LSTM). Results show up to a 28% reduction in computational effort and the importance of lowering the false negative rate in classification for achieving good planning performance outcomes. Full article

This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra’s shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments. Full article

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Time to receive effective therapy is a primary determinant of mortality in patients with sepsis. Blood culture is the reference standard for the microbiological diagnosis of bloodstream infections, despite its low sensitivity and prolonged time to receive a pathogen detection. In recent years, rapid tests for pathogen identification, antimicrobial susceptibility, and sepsis identification have emerged, both culture-based and culture-independent methods. This rapid narrative review presents currently commercially available approved diagnostic molecular technologies in bloodstream infections, including their clinical performance and impact on patient outcome, when available. Peer-reviewed publications relevant to the topic were searched through PubMed, and manufacturer websites of commercially available assays identified were also consulted as further sources of information. We have reviewed data about the following technologies for pathogen identification: fluorescence in situ hybridization with peptide nucleic acid probes (Accelerate Pheno^TM), microarray-based assay (Verigene^®), multiplex polymerase chain reaction (cobas^® eplex, BioFire^® FilmArray^®, Molecular Mouse, Unyvero BCU System^TM), matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (Rapid MBT Sepsityper^®), T2 magnetic resonance (T2Bacteria Panel), and metagenomics-based assays (Karius^©, DISQVER^®, Day Zero Diagnostics). Technologies for antimicrobial susceptibility testing included the following: Alfed 60 AST^TM, VITEK^® REVEAL^TM, dRAST^TM, ASTar^®, Fastinov^®, QuickMIC^®, Resistell^TM, and LifeScale. Characteristics, microbiological performance, and issues of each method are described, as well as their clinical performance, when available. Full article

Aiming to improve on the poor smoothness and longer paths generated by the traditional Hybrid A-star algorithm in unstructured environments with multiple obstacles, especially in confined areas for autonomous vehicles, a Hybrid A-star path planning method based on hierarchical clustering and trichotomy is proposed. This method first utilizes the Prewitt compass gradient operator (Prewitt operator) to identify obstacle boundaries and discretize boundaries. Then, it employs a single linkage hierarchical clustering algorithm to cluster obstacles based on boundaries. Subsequently, the clustered points are enveloped using a convex hull algorithm, considering collision safety for vehicle expansion. This fundamentally addresses the ineffective expansion issue of the traditional Hybrid A-star algorithm in U-shaped obstacle clusters. Finally, the expansion strategy of Hybrid A-star algorithm nodes is improved based on the trichotomy method. Simulation results demonstrate that the improved algorithm can search for a shorter and smoother path without significantly increasing the computational time. Full article

Factory safety inspections are crucial for maintaining a secure production environment. Currently, inspections are predominantly performed manually on a regular basis, leading to low efficiency and a high workload. Utilizing inspection robots can significantly improve the reliability and efficiency of these tasks. The development of robot localization and path planning technologies ensures that factory inspection robots can autonomously complete their missions in complex environments. In response to the application requirements of factory inspections, this paper investigates mapping, localization, and path planning methods for robots. Considering the limitations of cameras and laser sensors due to their inherent characteristics, as well as their varying applicability in different environments, this paper proposes SLAM application systems based on multi-line laser radar and visual perception for diverse scenarios. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that combines the A-star algorithm and time elastic band method is introduced. This approach effectively resolves the problem of path planning becoming trapped in local optima in complex environments, subsequently enhancing the inspection efficiency of robots. Experimental results demonstrate that the designed SLAM and path planning methods can satisfy the inspection requirements of robots in complex scenarios, exhibiting excellent reliability and stability. Full article

This paper focuses on the trajectory planning and trajectory tracking control of articulated tracked vehicles (ATVs). It utilizes the path planning method based on the Hybrid A-star and the minimum snap smoothing method to obtain the feasible kinematic trajectory. To overcome the highly non-linearity of ATVs, we proposed a linear-parameter-varying (LPV) kinematic tracking-error model. Then, the kinematic controller was formulated as the adaptive model predictive controller (AMPC). The simulation of the path planning algorithm showed that the proposed planning strategy could provide a feasible trajectory for the ATVs passing through the obstacles. Moreover, we compared the AMPC controller with the developed controller in four scenarios. The comparison showed that the AMPC controller achieved satisfactory tracking errors regarding the lateral position and orientation angle errors. The maximum lateral distance error by the AMPC controller has been reduced by 72.4% compared to the standard-MPC controller. The maximum orientation angle error has been reduced by 55.53%. The simulation results confirmed that the proposed trajectory planning and tracking control system could effectively perform the automated driving behaviors for ATVs. Full article

In this study, an autonomous driving system of a patient-transfer robot is developed. The developed autonomous driving system has a path-planning module and a motion-control module. Since the developed autonomous driving system is applied to medical robots, such as patient-transfer robots, the main purpose of this study is to generate an optimal path for the robot’s movement and to ensure the patient on board moves comfortably in the PTR. In particular, for the patient’s comfortable movement, a lower controller is needed to minimize the sway angle of the patient. In this paper, we propose a hybrid path-planning algorithm that combines the A-STAR algorithm as a global path-planning method and the AHP (Analytic Hierarchy Process)-based path-planning algorithm as a local path-planning method. In addition, model-based controllers are designed to move patient-transport robots along planned paths. In particular, the LQR controller with the Kalman filter is designed to be robust to the uncertainty and disturbance of the model including the patient. The optimal path generation and patient shaking angle reduction performance of the proposed autonomous driving system have been demonstrated via a simulation on a map that mimics a hospital environment. Full article

Omni-wheeled mobile robots (Omni WMRs) are commonly used in indoor navigation applications like surveillance, search and rescue, and autonomous transportation. They are always characterized by their versatility, mobility and high payload. This paper presents the mechatronic design, low-level control and high-level control of an indoor 4 Omni-Wheeled Mobile Robot (4OWMR). Since autonomy and path planning are research necessities for WMRs, four heuristic and probabilistic path-planning techniques are chosen for experimental implementation. The selected techniques are PRM (Probabilistic Roadmaps), RRT (Rapidly exploring Random Tree), RRTSTAR (RRT*), and ASTAR (A*) algorithms. The proposed environments are static, expressed by maps with unknown nodes and obstacles. Local path planning is implemented with simultaneous localization and mapping (SLAM). Path planning techniques are programmed, and the obtained paths are optimized by a multi-objective genetic algorithm technique to ensure the shortest path and its smoothness. The optimized paths are deployed to the 4OWMR. The obtained results are compared in terms of travel time, travel distance, average velocity and convergence error. A ranking technique is utilized to rank the obtained results and show the most preferred technique in terms of energy consumption and convergence accuracy in addition to the overall ranking. Experimental results showed that the Hybrid A* algorithm produced the best-generated paths with respect to other techniques. Full article

Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships. Full article

In this study, we concisely investigate two phases in the hybrid A-star algorithm for non-holonomic robots: the forward search phase and analytic expansion phase. The forward search phase considers the kinematics of the robot model in order to plan continuous motion of the robot in discrete grid maps. Reeds-Shepp (RS) curve in the analytic expansion phase augments the accuracy and the speed of the algorithm. However, RS curves are often produced close to obstacles, especially at corners. Consequently, the robot may collide with obstacles through the process of movement at these corners because of the measurement errors or errors of motor controllers. Therefore, we propose an improved RS method to eventually improve the hybrid A-star algorithm’s performance in terms of safety for robots to move in indoor environments. The advantage of the proposed method is that the non-holonomic robot has multiple options of curvature or turning radius to move safer on pathways. To select a safer route among multiple routes to a goal configuration, we introduce a cost function to evaluate the cost of risk of robot collision, and the cost of movement of the robot along the route. In addition, generated paths by the forward search phase always consist of unnecessary turning points. To overcome this issue, we present a fine-tuning of motion primitive in the forward search phase to make the route smoother without using complex path smoothing techniques. In the end, the effectiveness of the improved method is verified via its performance in simulations using benchmark maps where cost of risk of collision and number of turning points are reduced by up to around 20%. Full article

To overcome the limitation of poor processing times for long-distance off-road path planning, an improved A-Star algorithm based on terrain data is proposed in this study. The improved A-Star algorithm for long-distance off-road path planning tasks was developed to identify a feasible path between the start and destination based on a terrain data map generated using a digital elevation model. This study optimised the algorithm in two aspects: data structure, retrieval strategy. First, a hybrid data structure of the minimum heap and 2D array greatly reduces the time complexity of the algorithm. Second, an optimised search strategy was designed that does not check whether the destination is reached in the initial stage of searching for the global optimal path, thus improving execution efficiency. To evaluate the efficiency of the proposed algorithm, three different off-road path planning tasks were examined for short-, medium-, and long-distance path planning tasks. Each group of tasks corresponded to three different off-road vehicles, and nine groups of experiments were conducted. The experimental results show that the processing efficiency of the proposed algorithm is significantly better than that of the conventional A-Star algorithm. Compared with the conventional A-Star algorithm, the path planning efficiency of the improved A-Star algorithm was accelerated by at least 4.6 times, and the maximum acceleration reached was 550 times for long-distance off-road path planning. The simulation results show that the efficiency of long-distance off-road path planning was greatly improved by using the improved algorithm. Full article

The subsea production control system, characterized by a complex and diverse structure and high cost, is one of the essential parts of a subsea production system. The rational layout of the subsea production control system is essential to reduce development costs and ensure safe production in offshore fields. Most previous studies on layout design in offshore fields have focused on the oil- and gas-gathering system. However, the layout of the subsea production control system has not thoroughly been researched to date and the seabed terrain and integral optimization have rarely been discussed. This paper focuses on the multi-layer star structure and multi-layer star-tree structure, two common layout structures of subsea production control systems, and establishes the corresponding model with obstacle and seabed terrain conditions. Obtaining the lowest possible total cost was the aim of the model. A hybrid algorithm combining the adaptive mutation particle swarm algorithm and the A-star algorithm was applied to integrally optimize the subsea distribution unit and umbilical touch down point positions, the pipe connection topology and pipe routes. The practicality of this approach is demonstrated by designing a layout with one FPSO and 22 subsea control modules. The results indicate that the multi-layer star-tree layout structure has a lower total cost compared to that of the multi-layer star layout structure. In addition, the results were compared with a case that ignores the seabed terrain, indicating differences in the total construction cost. This method provides engineers with quantitative references and reliable cost estimates to make decisions regarding the layout of the subsea production control system. Full article

Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan the driving path and execute their motion. For addressing the issue of perception and path planning, in this paper, we introduce OctoPath, which is an encoder-decoder deep neural network, trained in a self-supervised manner to predict the local optimal trajectory for the ego-vehicle. Using the discretization provided by a 3D octree environment model, our approach reformulates trajectory prediction as a classification problem with a configurable resolution. During training, OctoPath minimizes the error between the predicted and the manually driven trajectories in a given training dataset. This allows us to avoid the pitfall of regression-based trajectory estimation, in which there is an infinite state space for the output trajectory points. Environment sensing is performed using a 40-channel mechanical LiDAR sensor, fused with an inertial measurement unit and wheels odometry for state estimation. The experiments are performed both in simulation and real-life, using our own developed GridSim simulator and RovisLab’s Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath in different driving scenarios, both indoor and outdoor, while benchmarking our system against a baseline hybrid A-Star algorithm and a regression-based supervised learning method, as well as against a CNN learning-based optimal path planning method. Full article