Global Path Research Articles

Path planning strategies for logistics robots: Integrating enhanced A‐star algorithm and DWA

AbstractPath planning is the key part in the process of transportation conducted by logistics robots, and there often exist some problems with it. The path designed is not always smooth enough and its search efficiency is low, for example. As a common global path planning algorithm, A‐star is based on the traditional algorithm, which is unable to solve the problem of uneven path in the movement of logistics robots. Through improving the heuristic function of the traditional A‐star algorithm, weighing the heuristic function dynamically, removing the redundant points of the traditional star algorithm path with Floyd algorithm, and setting a safe distance to prevent the logistics robot from collision at the same time, the path is finally curved to be more appropriate to the movement path of the logistics robot. The MATLAB simulation of A‐star algorithm before and after the improvement shows that the turning points of the advanced A‐star algorithm reduced 61.5% on average compared to the traditional algorithm. The path length decreased 2.4% and the traversing points reduced 58.5%. At the same time, the DWA algorithm introduces dynamic weight coefficients, which can dynamically adjust the weight coefficients when encountering obstacles, so as to safely reach the target point.

A Study of Global Path Planning System for Traffic Information Aware On-demand Delivery Services Using Autonomous Mobilities

A Study of Global Path Planning System for Traffic Information Aware On-demand Delivery Services Using Autonomous Mobilities

Global Path Optimal Planning Method for Autonomous Vehicles Based on Image Feature Extraction

In order to solve the problem of autonomous vehicles driving safely on the road while searching for optimal paths to avoid traffic congestion and obstruction, this paper establishes an optimal path planning model based on image feature extraction. The model consists of four parts: the selection of key turning points, interpolation between turning points, definition of the evaluation function, and global optimal path search. First, based on the map information provided by the network, an image feature detection algorithm was proposed, and the key turning points of the map route were selected and stored in an open table. Then, based on mechanical analysis theory, the cubic interpolation between two key turning points of the optimal path was defined. This allows the car to drive along a continuous curve with a certain arc radius, enabling it to drive smoothly, effectively avoid obstacles, and ensure safety. Then, an evaluation function is defined as needed, and different evaluation functions are specified for different road sections to avoid excessive local deviations in planning and to reduce the impact of known obstacles on the path. Finally, the cost of the search path is calculated based on the evaluation function. According to the principle of minimum cost, a globally optimal heuristic search algorithm is proposed to find the globally optimal path. Experimental results show that the proposed global optimal path planning algorithm has excellent performance, with an average accuracy of 96.71% and a search speed of 2.78 ms. The model not only provides accurate path planning and fast path selection capabilities but also has strong robustness against interference.

A review of navigation techniques for telepresence robots

Nowadays the need for digital travel has increased more than ever. This is due to the need for social distancing for medical reasons, economy of travelling, and avoidance of risks involved in physical travelling or for convenience. The telepresence robot therefore serves as a surrogate for the pilot user. Telepresence robots are very resourceful and versatile. Their applications include education; medicine; business; research and ambient assisted living. A major consideration for the effective functioning of telepresence robots is the navigation scheme employed which is central to its overall reliability. Depending on the use to which the robot is to be put, several authors have considered various navigation schemes used to effectively control and navigate in workspace consisting of obstacles and/or order robots. This work aims to review the navigation technique that has been employed by researchers for telepresence robots with a view to identifying the strengths and weaknesses of the various navigation techniques and subsequently coming up with a relatively novel system of navigation that attemts to mitigate the various weaknesses of the previous methodologies for telepresence robot navigation. It has been observed that the general path planning and navigation for the usual autonomous robot is also applicable to the telepresence robot but bearing in mind that the robot is being piloted by humans and it is only when in a condition where it would be cumbersome for the humans that autonomous control system takes over. At such times path planning and navigation using AI methods have proved to be of importance. The path planning algorithms have been shown to be categorized into two viz: global path planning algorithms which are used for known environments and local path planning algorithms for unknown or partially known environments. The algorithms for local path planning are majorly reactive in attribute and are more relevant to telepresence robots. In any particular application, an appropriate blend of a number of these algorithms is employed to achieve desired navigation objectives. Furthermore, in this work, we propose the combination of a line follower robot with an obstacle avoidance algorithm for situations where the expected robot paths are foreknown.

VNS-BA*: An Improved Bidirectional A* Path Planning Algorithm Based on Variable Neighborhood Search.

The A* algorithm is an effective method for path planning; however, it has certain drawbacks, such as a high number of turns, low planning efficiency, and redundant searches. To address these issues, this paper proposes an improved bidirectional A* global path planning algorithm based on a variable neighborhood search strategy, named VNS-BA*. The new algorithm first employs an 8-11-13 neighborhood search method for node expansion. Then, the bidirectional search strategy is optimized by using the current nodes of the opposite path and the global target point, enabling the two paths to meet in the middle of the map. Finally, redundant turns are removed from the path, and cubic spline interpolation is applied to achieve local smoothing at the turns. The effectiveness of the improved algorithm was validated on different maps and compared with A* and its three derived improved versions. The simulation results indicate that VNS-BA* shows significant improvements in terms of the number of path turns, turn angles, and planning efficiency.

Integrated Navigation Method for Orchard-Dosing Robot Based on LiDAR/IMU/GNSS

To enhance the localization reliability and obstacle avoidance performance of the dosing robot in complex orchards, this study proposed an integrated navigation method using LiDAR, IMU, and GNSS. Firstly, the tightly coupled LIO-SAM algorithm was used to construct an orchard grid map for path planning and obstacle avoidance. Then, a global localization model based on RTK-GNSS was developed to achieve accurate and efficient initial localization of the robot’s coordinates and heading, and a Kalman filter was applied to integrate GNSS and IMU to improve robustness. Next, an improved A* algorithm was introduced to ensure the global operational path maintained a safe distance from obstacles, while the DWA algorithm handled dynamic obstacle avoidance. Field tests showed that the global localization model achieved an accuracy of 2.215 cm, with a standard deviation of 1 cm, demonstrating stable positioning performance. Moreover, the global path maintained an average safe distance of 50.75 cm from the obstacle map. And the robot exhibited a maximum absolute lateral deviation of 9.82 cm, with an average of 4.16 cm, while maintaining a safe distance of 1 m from dynamic obstacles. Overall, the robot demonstrated smooth and reliable autonomous navigation, successfully completing its tasks.

A novel autonomous exploration algorithm via LiDAR/IMU SLAM and hierarchical subsystem for mobile robot in unknown indoor environments

Abstract Autonomous exploration in unknown environments is an essential capability for mobile robots. The complexity of autonomous exploration, however, means that existing algorithms struggle to balance efficiency and comprehensiveness, causing low mapping accuracy and redundant path planning. To perform accurate and efficient exploration tasks, we have proposed a novel autonomous exploration algorithm via LiDAR/IMU (Inertial Measurement Unit) Simultaneous Localization and Mapping (SLAM) and hierarchical subsystem for mobile robot in unknown environments. Firstly, to enhance mapping accuracy for mobile robot exploration, LiDAR/IMU SLAM is improved with the assistance of backward propagation and iterated Kalman filter, and Bidirectional Rapidly–Exploring Random Trees* (BI–RRT*) is applied for efficient frontier point detection. Secondly, we optimize local path planning by leveraging information theory through perceptual quality evaluation, which is then integrated with global path planning utilizing an enhanced Travelling Salesman Problem solver and a sparse grid map to amplify exploration efficiency. Thirdly, an enhanced hierarchical autonomous exploration method for mobile robots is proposed, which incorporates local path planning for seamless navigation around highly promising exploration spots, coupled with global path planning to effectively interconnect various sub–regions. Finally, simulations and field tests have demonstrated that the proposed method explores an unknown indoor environment with a 30.8% reduction in exploration time and a 29.9% reduction in exploration path in comparison with Dynamic Stage Viewpoint Planner. The map constructed in this paper has more accurate details and exploration paths have been shortened to ensure effective exploration.

Efficient micro-mobility path planning without using high-definition maps

Efficient path planning is crucial for the safe autonomous operation of micro-mobility vehicles in unknown environments. When planning paths for micro-mobility, factors, such as the changes in the user's intended destination and user comfort, must be considered. Moreover, for driving in unknown areas, the path planner should not rely on predetermined detailed information like High-Definition maps (HD maps). In this paper, we propose an innovative path planning algorithm for automated driving that solely uses basic perception data from RGB camera, IMU and GPS, thereby eliminating the dependency on HD maps. We initially convert perception data into an occupancy grid map. Subsequently, a global path planner, based on a modified A* algorithm, computes an efficient trajectory, particularly adept to navigation in unknown environments and to scenarios where goal position moves. Additionally, we developed a local planner that optimize multi-clothoid curves using designed constraints to predict the best trajectory. Our experiments, conducted in both simulated and real-world environments, validate the effectiveness of our approach for micro-mobility path planning using only perception data.

Research on Global Off-Road Path Planning Based on Improved A* Algorithm

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments.

Adaptive Deep Ant Colony Optimization–Asymmetric Strategy Network Twin Delayed Deep Deterministic Policy Gradient Algorithm: Path Planning for Mobile Robots in Dynamic Environments

Path planning is one of the main focal points and challenges in mobile robotics research. Traditional ant colony optimization (ACO) algorithms encounter issues such as low efficiency, slow convergence, and a tendency to become stuck in local optima and search stagnation when applied to complex dynamic environments. Addressing these challenges, this study introduces an adaptive deep ant colony optimization (ADACO) algorithm, which significantly improves efficiency and convergence speed through enhanced pheromone diffusion mechanisms and updating strategies, applied to global path planning. To adapt to dynamically changing environments and achieve more precise local path planning, an asymmetric strategy network TD3 algorithm (ATD3) is further proposed, which utilizes global path planning information within the strategy network only, creating a new hierarchical path planning algorithm—ADACO-ATD3. Simulation experiments demonstrate that the proposed algorithm significantly outperforms in terms of path length and number of iterations, effectively enhancing the mobile robot’s path planning performance in complex dynamic environments.

Hybrid Mobile Robot Path Planning Using Safe JBS-A*B Algorithm and Improved DWA Based on Monocular Camera

This paper addresses the formidable challenge of enabling autonomous navigation in Mobile Robots (MRs), focusing on the development of advanced path planning strategies. Despite their pivotal role in diverse applications, they face challenges in dynamic settings due to limitation in existing Global Path Planning (GPP) and Local Path Planning (LPP) techniques. In response to this, we propose an innovative hybrid path planning approach that enhances the A* algorithm with a risk-aware heuristic function and integrates the Jump Point Search (JPS) technique for route optimization. Additionally, B-spline smoothing is employed for perceptually global trajectory refinement. Our approach also includes an innovative improvement to the Dynamic Window Approach (DWA) to align with the proposed enhanced A* algorithm for effective local navigation. Acknowledging the importance of high-quality input in path planning, we present substantial improvements to the IRDC-Net, a monocular-image semantic-segmentation model that we studied previously. Novel improvements include the integration of quantization and the Adam optimizer, along with the implementation of the Balanced Cross-Entropy loss function. These enhancements not only elevate the output quality of IRDC-Net but also reduce the model’s training parameters. The experimental results demonstrate the performance and viability of the proposed algorithm. Ultimately, the hybrid MR’s path planning algorithm exhibits proficiency across various tasks, particularly in addressing the challenge of evading moving obstacles to ensure the robot’s safety while adhering to the global path.

Global path planning of unmanned ship island region based on improved A* algorithm

Abstract To solve the path planning problem for unmanned boats in island and reef areas, this paper introduces an adaptive heuristic function specifically for island and reef areas based on the traditional A* algorithm, in order to effectively handle the obstacle avoidance problem. A new cost function is designed to balance the relationship between path length and safety. The sea chart is converted into a path planning map by using the grid method, and the path planning is carried out. The paper compares the performance of the new path-planning algorithm with other path-planning algorithms through experiments, and the improved A* algorithm for global path planning of unmanned boats in island and reef areas shows excellent performance in various complex environments, effectively improving the island and reef area navigation efficiency.

Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots

Addressing issues with traditional ant colony and reinforcement learning algorithms, such as low search efficiency and the tendency to produce insufficiently smooth paths that easily fall into local optima, this paper designs an improved ant colony optimization algorithm fusion with improved Q-Learning (IAC-IQL) algorithm for Bessel curve global path planning of search and rescue (SAR) robots. First, the heuristic function model in the ant colony algorithm is improved, the elite ant search strategy and the adaptive pheromone volatility factor strategy are introduced, and the initial path is searched in realize the motion environment with the help of the improved ant colony algorithm, and the initialized pheromone matrix is constructed. Second, the improved ant colony algorithm and Q-Learning (QL) algorithm are fused by utilizing the similarity between the pheromone matrix in the improved ant colony algorithm and the Q-matrix in the QL algorithm. A heuristic learning evaluation model is designed to dynamically adjust the learning factor and provide guidance for the search path. Additionally, a dynamic adaptive greedy strategy is introduced to balance the exploration and exploitation of the robot in the environment. Finally, the paths are smoothed using third-order Bessel curves to eliminate the problem of excessive steering angles. Through three sets of comparative simulation experiments conducted in Pycharm platform, the effectiveness, superiority, and practicality of the IAC-IQL algorithm were verified. The experimental results demonstrated that the IAC-IQL algorithm integrates the strong search capability of ant colony algorithm and the self-learning characteristics of QL algorithm. SAR robots equipped with the improved IAC-IQL algorithm exhibit significantly enhanced iterative search efficiency in grid simulation environment and image sampling simulation environment. The global path optimization indicators demonstrate high efficiency, and the paths are smoother.

South America is becoming warmer, drier, and more flammable

South America is experiencing severe impacts from climate change. Although the warming of the subcontinent closely follows the global path, the rise of temperatures has been more pronounced in some regions, which have also seen a parallel increment in the occurrence of droughts and weather conditions associated with enhanced fire risk. Here, we use reanalysis datasets to analyze the progression of the concurring warm, dry, and high fire risk conditions (i.e., dry compounds) since 1971. We show that the frequency of these compound extremes has surged in key South American regions including the northern Amazon, which have seen a 3-fold increase in the number of days per year with extreme fire weather conditions (including high temperatures, dryness, and low humidity). Our results also suggest that the surface temperature of the tropical Pacific Ocean modulates the interannual variability of dry compounds in South America. While El Niño enhances the fire risk in the northern Amazon, dry extremes in the Gran Chaco region appear to be more responsive to La Niña.

Research on Indoor Automatic Path Planning Algorithm for Robots

The field of robotics has significantly advanced, especially in indoor autonomous navigation, with applications in households, offices, factories, and tourism. This research focuses on developing and optimizing algorithms for indoor autonomous path planning, aiming to enhance robots' ability to navigate efficiently and safely in various indoor environments. The research adopts a comprehensive methodological approach, beginning with a literature review to understand current state-of-the-art techniques in path planning and obstacle avoidance. Novel algorithms were designed and implemented, focusing on efficiency and real-time performance. These algorithms were tested in simulation environments and validated on actual robotic platforms. Performance metrics such as path efficiency, computational time, and obstacle avoidance success rates were used to assess the algorithms. The study evaluated global path planning algorithms like A* and Dijkstra’s, and local path planning algorithms such as Rapidly-exploring Random Trees (RRT) and Dynamic Window Approach (DWA). A* and Dijkstra's algorithms proved effective for global planning but required optimization for real-time applications. RRT excelled in complex environments but needed improvements for path optimality. DWA provided robust real-time obstacle avoidance. Hybrid approaches combining these algorithms showed enhanced performance, balancing global and local planning strengths. Machine learning techniques further improved adaptability and efficiency. The integration of advanced sensor technologies and accurate map construction methods is crucial for effective indoor navigation. LIDAR, ultrasonic sensors, and depth cameras were key in providing precise environmental perception. Hybrid maps combining grid and topological approaches offered detailed local information and efficient long-distance planning. Optimization techniques like parameter tuning and algorithm fusion, along with machine learning, significantly enhanced algorithm performance. Future research should explore intelligent algorithms, multi-robot collaboration, and human-robot interaction to further advance the field.

A real time data-driven dynamic glasius bionic neural network path planning algorithm for polar under-ice feature scanning by “Xinghai 1000” AUV

Using Autonomous Underwater Vehicle (AUV) to survey polar under-ice feature is crucial for studying polar marine environment and global climate change. Compared with seafloor terrain scanning, it faces several challenges, such as uncertain under-ice environment, lack of reliable positioning methods, and stringent constraints on recovery location. Thus, taking “Xinghai 1000” as analysis object, this paper proposes a real time data-driven dynamic glasius bionic neural network path planning algorithm (RDGPP), which consists of an online bi-level path planning module. In global planning module, dynamically hierarchical strategy is used to realize task area layering. On this basis, global coverage path is planned by combining neuron activity value updated which utilize real-time environmental information obtained by FLS sonar with multivariant strategy. Moreover, due to AUV recovery mode limitation, return strategy has been devised in multivariant strategy to ensure AUV automatic return to launch point, thereby reducing energy usage. In local planning module, Flagged bit and GBNN-driven A-star algorithm (FGA) is introduced to minimize deadlock escape time for AUV. Simulation experiments verify RDGPP algorithm advancement in key indicators such as turning times, running time, etc., and effectiveness is verified by “Xinghai 1000” under-ice experiment.

Bi-level task planning strategy for blended-wing-body underwater gliders

ABSTRACT This study focuses on the task planning problem of the blended-wing-body underwater glider (BWBUG), which must autonomously navigate through a sequence of underwater visitation points while ensuring safety and economy. Firstly, the task planning problem of the BWBUG is analysed, with attention given to operational space and task requirements. Subsequently, a bi-level planning framework that incorporates both the bottom-level and top-level planners is proposed. The bottom-level planner optimises the glide path between any two visit points by considering safety and glide angle constraints, with the objective of minimising energy usage. The top-level planner focuses on the task constraints and optimises the global visitation path of the BWBUG to achieve the objective of global energy minimisation. Simulations demonstrate the effectiveness of the bi-level based task planning strategy proposed in this study. The solution algorithms are highly competitive, capable of planning rational global visitation paths for the BWBUG.

Surrogate model based optimization of variable stiffness composite wingbox for improved buckling load with manufacturing and failure constraints

The buckling load of a wingbox is maximized by applying different automatic fiber placement (AFP) strategies on the skins of the wing having NACA 4412 airfoil profile. Specifically, the effect of single, dual and three region application of AFP on the optimal buckling load is studied to provide an outlook on the improvement of the optimal buckling load by increasing the number of skin panels, between rib stations, over which distinct reference fiber path definitions are made. Buckling load optimizations with manufacturing and failure constraints are performed via two different metaheuristic optimization algorithms to ensure that the global optimum reference fiber paths are obtained. For computational efficiency, separate radial basis function based surrogate models are generated for the buckling and the failure analysis. Our results show that the application of AFP separately with distinct reference fiber path definitions in each skin panel between the rib stations, the buckling load can be improved compared to the application of the AFP to a larger section of the wing skin. For the wingbox studied, compared to the wing with quasi-isotropic skins, 94% increase in the local buckling load of the root panel is obtained with the three region AFP application.

A dual-stage coverage path planning method for bathymetric survey using an AUV in graph-based SLAM framework considering positioning uncertainty

Autonomous underwater vehicles (AUVs) are becoming increasingly popular for seabed coverage survey tasks but suffer from a serious problem of positioning error accumulation, which leads to low path tracking accuracy and a low coverage rate of the target area. A dual-stage AUV path planning (DSPP) approach was proposed within a simultaneous localization and mapping (SLAM) framework for coverage bathymetric surveys. In the first stage, a global coverage path is designed in a lawnmower style. Crossed paths are added to generate periodic loop closures in SLAM, which can improve the efficiency of online replanning. In the second stage, with a probabilistic neural network (PNN) used for feature-rich bathymetric sub-map identification, an entropy-based online path replanning approach was implemented using 20 templates to adjust the global path to produce accurate loop closures for positioning error correction. Two bathymetric datasets were used to test the proposed DSPP method using a self-developed experimental simulation system. Experiments verified that DSPP can significantly improve the positioning accuracy and coverage rate with acceptably longer paths. In a 6000 m× 5000 marea,the DSPP approach reduced the positioning error by 76% compared with the pure dead reckoning method, and the coverage ratio reached 98.15%.

Trace the evolution path of pharmaceutical wastewater treatment technology based on a patent perspective

ABSTRACT This paper discusses the research hotspots and future development trends of pharmaceutical wastewater treatment technology, in order to provide valuable reference and guidance to promote the development of technology. First, the search path counting (SPC) algorithm is utilized to identify the global main path to perform a preliminary analysis of the technology evolution path from a macro perspective. Second, according to the life cycle, S-curve divides the time windows of the temporal themes and uses the Latent Dirichlet Allocation (LDA) modeling to identify the themes under each time window. Then, a visualized Sankey diagram is generated to determine the evolutionary relationship, which can be supplemented with the paths of technological evolution from the micro point of view. Finally, six research hotspots of pharmaceutical wastewater treatment technology were found, which are compounding agents, advanced oxidation technology, nanomaterials, artificial intelligence (AI) technology, membrane pollution prevention, and various combinations of wastewater treatment technology. The study also predicted its future development direction.