High-quality Paths Research Articles

An Innovative Path Planning Algorithm for Complex Obstacle Environments with Adaptive Obstacle Density Adjustment: AODA-PF-RRT*

To address the limitations of low node utilization and inadequate adaptability in complex environments encountered by Rapidly-exploring Random Tree (RRT) algorithms during the expansion phase, this study presents an enhanced path planning algorithm—AODA-PF-RRT* (Adaptive Obstacle Density Adjustment-PF-RRT*). The proposed algorithm implements a random extension strategy for nodes that fail collision detection, thereby improving node efficiency. Furthermore, it dynamically partitions the area surrounding sampling points and calculates local obstacle density in real time. By leveraging this density information, the algorithm flexibly adjusts both the number of expansion points and the dichotomy threshold, significantly enhancing its responsiveness to environmental changes. We rigorously demonstrate the algorithm’s probabilistic completeness and asymptotic optimality. Simulation and benchmarking results demonstrate that the AODA-PF-RRT* algorithm not only generates smooth and high-quality paths compared to existing algorithms but also maintains low computational costs in complex environments, showcasing exceptional stability and robustness.

Centralized vs. Decoupled Dual-arm Planning Taking into Account Path Quality

The aim of coordinated planning is to avoid robot-to-robot collisions in a multi-robot system, and there are two standard solution approaches: centralized planning and decoupled planning. Our first contribution is a decoupled planning approach that ensures C2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{\\mathcal {C}^2}$$\\end{document}-continuous control commands with zero velocities and zero accelerations at the start and goal. We benchmark our decoupled approach with a centralized approach. Contrary to literature, we show that for a standard motion planning pipeline, such as the one used by MoveIt!, centralized planning is superior to decoupled planning in dual-arm manipulation: It has a lower computation time and a higher robustness. Our second contribution is an optimization that minimizes the rotational motion of an end-effector while considering obstacle avoidance. We derive the analytic gradients of this optimization problem, making the algorithm suitable for online motion planning. Our optimization extends an existing path quality improvement method. Integrating it into our decoupled approach overcomes its shortcomings and provides a motion planning pipeline that is robust at up to 99.9 % with a planning time of less than 1 s and that computes high-quality paths.

IBPF-RRT*: An improved path planning algorithm with Ultra-low number of iterations and stabilized optimal path quality

Due to its asymptotic optimality, the Rapidly-exploring Random Tree star (RRT*) algorithm is widely used for robotic operations in complex environments. However, the RRT* algorithm suffers from poor path quality, slow convergence, and unstable generation of high-quality paths in the path planning process. This paper proposes an Improved Bi-Tree Obstacle Edge Search Artificial Potential Field RRT* algorithm (IBPF-RRT*) to address these issues. First, based on the RRT* algorithm, this paper proposes a new obstacle edge search artificial potential field strategy (ESAPF), which speeds up the path search and improves the path quality simultaneously. Second, a bi-directional pruning strategy is designed to optimize the bi-directional search tree branch nodes and combine the bi-directional search strategy to reduce the number of iterations for convergence speed significantly. Third, a novel path optimization strategy is proposed, which enables high-quality paths to be generated stably by creating an entirely new node between two path nodes and then optimizing the paths using a pruning strategy based on triangular inequalities. Experimental results in three different scenarios show that the proposed IBPF-RRT* algorithm outperforms other methods in terms of optimal path quality, algorithm stability, and the number of iterations when compared to the RRT*, Q-RRT*, PQ-RRT*, F-RRT* and CCPF-RRT* algorithms, and proves the effectiveness of the proposed three strategies.

A Sampling-Based Approach to Solve Difficult Path Planning Queries Efficiently in Narrow Environments for Autonomous Ground Vehicles

Path planning is an essential subproblem of autonomous robots’ navigation. Reaching a given goal pose or covering the available space are typical navigation missions, that require different planning approaches. We focus on such problems in this paper, where a goal pose must be reached by a wheeled autonomous ground vehicle in challenging situations, i.e. in complex environments with limited free space. Many path-planning methods are available, from which the sampling-based approaches gained the highest interest due to their computational efficiency. However, the performance of such methods degrades if the free space is limited and narrow passages have to be crossed on the way to the goal. Finding real-time planning methods to deliver high-quality paths in such situations is challenging. This paper aims to take steps toward solving this problem. On the one hand, an approach is presented to characterize free space narrowness and the difficulty of planning tasks. This can be used as a tool to compare planning queries and evaluate the performance of planning methods from the perspective of their sensitivity to environmental narrowness. On the other hand, an improved variant of our previously proposed RTR planner, an incremental sampling-based path-planning method, is introduced that exhibits good performance even in narrow and difficult planning situations. It is shown by simulations that it outperforms the popular RRT and RRT* planners in terms of running time and path quality, and that it is less sensitive to the narrowness of the environment where the planning task has to be solved.

A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density

Path planning is one of the most essential parts of autonomous navigation. Most existing works are based on the strategy of adjusting angles for planning. However, drones are susceptible to collisions in environments with densely distributed and high-speed obstacles, which poses a serious threat to flight safety. To handle this challenge, we propose a new method based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density (MSAO2H). Firstly, we propose to extend the obstacle avoidance decisions of drones into angle adjustment, speed adjustment, and obstacle clearance. Hybrid action space is adopted to model each decision. Secondly, the state space of the obstacle environment is constructed to provide effective features for learning decision parameters. The instant reward and the ultimate reward are designed to balance the learning efficiency of decision parameters and the ability to explore optimal solutions. Finally, we innovatively introduced the interferometric fluid dynamics system into the parameterized deep Q-network to guide the learning of angle parameters. Compared with other algorithms, the proposed model has high success rates and generates high-quality planned paths. It can meet the requirements for autonomously planning high-quality paths in densely dynamic obstacle environments.

A Random Path Sampling-Based Method for Motion Planning in Many Dimensions

In this paper, we present GRPMP (Gaussian Random Paths Motion Planner), a method for solving robot motion planning problems. Compared with traditional sampling-based methods which build large-scale random trees or roadmaps by sampling random nodes in configuration-spaces, GRPMP generates a set of smooth random paths discretized into sparse nodes, using a random path sampler based on Gaussian process, to reduce resource space occupation and improve planning success rate. Employing the high-quality path selecting strategy with the lazy collision checking technique, GRPMP can fast select the collision-free high-quality path that has the lowest cost in the set of smooth random paths, so as to improve the efficiency and quality of the motion planning. We present challenging experiments with GRPMP on pick-and-place tasks, using a humanoid robot, to show that GRPMP has the ability to find high-quality paths with efficiency and stability.

Dynamic Three-Dimensional Lift Planning for Intelligent Boom Cranes

Manually planning a crane's lifting path is complicated and laborious since it typically depends on the planner's experience. We examined dynamic three-dimensional lift planning, including problem formulation for multiobjective planning and problem-solving with an improved A* algorithm. First, we quantified various feasible movements by introducing energy and time consumption models to determine the most efficient and quickest path. Second, we examined a new method for determining the visible lifting path in Configuration space (C-space) and derived an obstacle enveloping method for collision detection in C-space. Our method is both safe and feasible. Finally, we proposed an improved A* algorithm for lift planning considering multiple objectives and featuring better computational efficiency. The experimental and simulation results demonstrate that the proposed lift planning method can comprehensively consider multiple objectives, thus providing a high-quality path for actual lifting. Furthermore, compared with the ordinary A* algorithm, the improved A* algorithm had better computational efficiency and consumed less energy and time. To our best knowledge, this is the first novel algorithm for planning crane lifts considering energy consumption and visibility in C-space.

A mixing algorithm of ACO and ABC for solving path planning of mobile robot

Path planning is involved in many applications such as trajectory planning, mobile robotics, pipeline layout, etc. Researchers use artificial intelligence algorithms to solve path planning efficiently, among which the ant colony algorithm (ACO) is one of the common intelligent algorithms to solve path planning problems. However, the traditional ACO has defects such as low early search efficiency and easy to fall into local optimum, while the artificial bee colony algorithm (ABC) has high search efficiency. Therefore, an improved ant colony optimization-artificial bee colony algorithm (IACO-IABC) is proposed in this study. IACO-IABC contains three mechanisms. First, the heuristic mechanism with directional information for the ACO is improved to enhance the efficiency of steering towards the target direction. Secondly, the novel neighborhood search mechanism of the employed bee and the onlooker bee in the ABC is presented to enhance the exploitation of optimal solutions. Then, the path optimization mechanism is introduced further to reduce the number of turn times in the planned path. To verify the performance of the IACO-IABC, a series of experiments are conducted with 10 different maps. The experiments compare nine variants of ACO and eight commonly used intelligent search algorithms, and the results show the advantages of the IACO-IABC in reducing the number of turn times and path lengths and enhancing the convergence speed of the algorithm. Compared to the best results of other algorithms, the average improvement percentages of the proposed algorithm in terms of the path turn times are 375%, 258.33%, 483.33%, 186.67%, 166.77% and 255.33%, further demonstrating the ability of IACO-IABC to obtain high-quality path planning result.

Hybrid path planning based on adaptive visibility graph initialization and edge computing for mobile robots

This paper presents a new initialization method that combines adaptive visibility graphs and the A* algorithm to improve the exploration, accuracy, and computing efficiency of hybrid path planning for mobile robots. First, segments/links in the full visibility graphs are removed randomly in an iterative and adaptive manner, yielding adaptive visibility graphs. Then the A* algorithm is applied to find the shortest paths in these adaptive visibility graphs. Next, high-quality paths featuring low fitness values are chosen to initialize the subsequent heuristic optimization in hybrid path planning. Specifically, in the present study, the genetic algorithm (GA) is implemented on a CPU/GPU edge computing device (Jetson AGX Xavier) to exploit its massively parallel processing threads, and the strategy for judicious CPU/GPU resource utilization is also developed. Numerical experiments are conducted to determine proper hyperparameters and configure GA with balanced performance. Various optimal paths with differential consideration of practical factors for robot path planning are obtained by the proposed method. Compared to the other benchmark methods, ours significantly improves the diversity of initial path and exploration, optimization accuracy, and computing speed (within 5 s with most less than 2 s). Furthermore, real-time experiments are carried out to demonstrate the effectiveness and application of the proposed algorithm on mobile robots.

Graph-based robot optimal path planning with bio-inspired algorithms

Recently, bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps. However, these approaches endure performance degradation as problem complexity increases, often resulting in lengthy search times to find an optimal solution. This limitation is particularly critical for real-world applications like autonomous off-road vehicles, where high-quality path computation is essential for energy efficiency. To address these challenges, this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm, improved seagull optimization algorithm (iSOA) for rapid path planning of autonomous robots. A modified Douglas–Peucker (mDP) algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains. The resulting mDP-derived graph is then modeled using a Maklink graph theory. By applying the iSOA approach, the trajectory of an autonomous robot in the workspace is optimized. Additionally, a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints. The proposed model is validated through simulated experiments undertaken in various real-world settings, and its performance is compared with state-of-the-art algorithms. The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

Spatial Path Smoothing for Car-like Robots Using Corridor-Based Quadratic Optimization

The global planning module of car-like robots usually plans a coarse spatial path, which may be non-smooth and kinematically infeasible for car-like robots. This study proposes an efficient spatial path smoothing approach, which is capable of optimizing a rough spatial path to be a high quality one. Two novel designs contribute to the proposed approach. One is a direct corridor construction method that provides an optimization region for path optimization. Based on a redefined path representation in the generated corridor, the second one is a core spatial path optimization method, where the optimization problem is formulated as a multi-objective quadratic programming (QP) with corridor and maximum-curvature constraints. Meanwhile, integrating a two-steps strategy into an optimization process yields a good trade-off between efficiency and quality. Experiment results validate that the proposed approach has the ability to online generate a high quality path.

Green Finance, Industrial Structure Upgrading, and High-Quality Economic Development–Intermediation Model Based on the Regulatory Role of Environmental Regulation

Green finance, as a major policy innovation under the guidance of high-quality economic development, can optimize the economic development mode and structure through green investment to enhance the rationalization and advanced level of industrial structure, and ultimately enhance the level of high-quality development of China's economy. Based on the panel data of 30 provinces and cities in China from 2009-2019, this study examines the impact of green finance on economic high-quality development and its transmission path using the intermediary model. Based on this, a model with moderated intermediation effects is constructed to explore the mechanism of green finance on an economic high-quality path based on the intermediation path of industrial structure advancement and industrial structure rationalization and the moderated effect of environmental regulation. Finally, regional heterogeneity analysis is conducted. The study found that, firstly, green finance significantly contributes to high-quality economic development, and there is a positive mediating effect of both advanced and rationalized industrial structure. Secondly, environmental regulation positively regulates the positive impact of green finance development on industrial structure upgrading and strengthens the role of industrial structure upgrading in promoting economic quality development, as well as playing a significant positive regulating role in the direct effect of green finance on economic quality development. Finally, there are regional differences in the impact of green finance on high-quality economic development.

The “Double Trap” in China—Multiple Equilibria in Institutions and Income and their Causal Relationship

While institutions are said to be poor in China in cross-country comparison, recent research indicates that at the provincial level, institutional quality plays in fact an important role for the economic success of a province, municipality, or autonomous region in China. Our paper aims to add further arguments to this discussion by focusing on the concept of club convergence. In particular, we analyze whether institutional quality in low-income provincial level administrative divisions converges to the level experienced by relatively highly developed ones or whether there exist multiple institutional clubs over the period 1997–2007 by using the log t test proposed by Phillips and Sul (Econometrica 75(6):1771–1855, 2007). Our findings indicate that there exist multiple institutional clubs within China, three rather small clubs which follow an above-average high institutional quality path and two clubs which find themselves on a relatively low institutional quality path and which together account for the majority of provinces and autonomous regions. Using the same methodology, we find that various members of the poor institutional clubs are additionally caught in a low-income trap. In a next step, we analyze the causal relationship between poor institutional traps and low-income traps in China by using a recursive bivariate probit model. We find evidence that institutional traps are important determinants of income traps, giving rise to the recently identified phenomenon of a ‘double trap’. Finally, our findings indicate that human capital and urbanization are additional important determinants of income traps, while globalization is decisive for avoiding poor institutional traps.

Cooperative path planning optimization for multiple UAVs with communication constraints

Path planning is a complicated optimization problem that is crucial for the safe flight of unmanned aerial vehicles (UAVs). Especially in the scenarios involving multiple UAVs, this problem is highly challenging due to the constraints of complex environments, various tasks and inherent UAV maneuverability. In this paper, a cooperative path planning model for multiple UAVs is presented. In addition to common limitations such as the path length minimization, UAV maneuverability limitation and collision avoidance, the communication requirements between UAVs and the impact of obstacles in the flight environment on the quality of communication are also taken into account in the presented path planning model. On this basis, the corresponding objective function is designed. Then, an improved particle swarm optimization (PSO) algorithm is proposed to solve the above path planning problem. Utilizing the ideas of the dynamic multi-swarm PSO (DMSPSO) algorithm and the comprehensive learning PSO (CLPSO) algorithm, the proposed algorithm, denoted as CL-DMSPSO, further improves the performance of both algorithms. The effectiveness and superiority of the novel CL-DMSPSO algorithm is verified on benchmark functions, especially for complex multimodal functions. Finally, we present an effective path planning method using CL-DMSPSO to generate optimized flyable paths for multiple UAVs. And simulation and comparison results on the designed scenario indicate the proposed UAV path planning method can efficiently plan high-quality paths for UAVs and demonstrate the advantages of the proposed CL-DMSPSO algorithm compared with other PSO algorithms in UAV path planning.

Research on Operation and Maintenance Route Planning of Offshore Wind Farm Based on Multiagent

Recently, the ant colony algorithm has been widely used in the field of path planning, which is a key technology required for ship operations in offshore wind farms to improve navigation efficiency and power generation. However, the ant colony algorithm has the defects of a long search time and stagnation in the early stage of the operation and maintenance path planning of offshore wind farms, and it easily falls into the problem of local optima; furthermore, the ant colony algorithm uses incremental construction to build a complete itinerary path, it takes a lot of time to search the path, and only the suboptimal solution is obtained. To address the above problems, in this paper, we propose a multi-agent-based operation and maintenance model for offshore wind farms. Specifically, the introduction of the heuristic factor can optimize the local optimal solution and make the ant colony algorithm clearer when searching for the target. Based on this, increasing the pheromone adjustment factor can eliminate the invalid path from searching and select a high-quality path. In addition, by integrating the genetic algorithm, it is possible to select, cross, and mutate to simulate the natural evolution process to search for the optimal solution, reduce the similarity of the paths constructed by the ant colony, reduce the probability of algorithm stagnation, improve the convergence speed, and improve the time efficiency and solution accuracy of the algorithm. Simulation experiments on a series of benchmark datasets show that the proposed GA-PACO algorithm achieves better performance in global search and path planning than the existing three algorithms.

Wi-PIGR: Path Independent Gait Recognition With Commodity Wi-Fi

Wi-Fi based gait recognition has many potential applications. However, the gait information derived from Wi-Fi changes with the walking path. This makes the human identification through gait really challenging, the existing Wi-Fi based gait recognition systems require the subject walking along a predetermined path. This path dependence restriction impedes Wi-Fi based gait recognition from being widely used. In this paper, a path independent gait recognition system for a single subject, Wi-PIGR, is proposed. In Wi-PIGR, the subject is identified through the gait regardless of the walking path. Specifically, an extra receiver is introduced to get CSI data in orthogonal directions. A series of signal processing techniques are proposed to eliminate the differences among signals introduced by walking along the arbitrary paths and generate a high quality path independent signal spectrogram. Furthermore, a deep learning approach is integrated into the feature extraction. The experiment results in typical indoor environment demonstrate the superior performance of Wi-PIGR, with the average recognition accuracy of 77.15 percent, when the number of subjects is 50.

GMR-RRT*: Sampling-Based Path Planning Using Gaussian Mixture Regression

Mobile robot autonomous path planning is an essential factor for its wide deployment in real-world applications. Conventional sampling-based algorithms have gained tremendous success in the path planning field, but they usually take much time to find the optimal solution so that the planning quality (evaluated with time cost and path length) cannot be guaranteed. In this paper, based on Gaussian Mixture Regression (GMR) and the family of Rapidly-exploring Random Tree (RRT) schemes, we propose the GMR-RRT* algorithm to achieve fast path planning for mobile robots. The proposed GMR-RRT* consists of learning navigation behaviors from human demonstrations and planning a high-quality path for the robot. Using the GMR, the key features of human demonstrations are captured to form a probability density distribution of the human trajectory in the current environment. This distribution is further utilized to guide the RRT scheme’s sampling process to generate a feasible path in the current environment quickly. We test the proposed GMR-RRT* in different environments, comparing it with three state-of-the-art sampling-based algorithms. The experimental results demonstrate that the GMR-RRT* algorithm can achieve better performance in terms of time cost, memory usage, and path length.

RRT*-based Path Planning for Continuum Arms.

Continuum arms are bio-inspired devices that exhibit continuous, smooth bending and generate motion through structural deformation. Rapidly-exploring random trees (RRT) is a traditional approach for performing efficient path planning. RRT approaches are usually based on exploring the configuration space (C-Space) of the robot to find a desirable work space (W-Space) path. Due to the complex kinematics and the highly non-linear mapping between the C-Space and W-Space of continuum arms, a high-quality path in the C-Space (e.g., a linear path) may not correspond to a desirable path/movement in the W-Space. Consequently, the C-Space RRT approaches that are based on C-Space cost functions do not lead to reliable and effective path planning when applied to continuum arms. In this paper, we propose a RRT* path planning approach for continuum arms that is based on exploring the W-Space of the robot as opposed to its C-Space. We show the successful applications of the proposed W-Space RRT* path planner in performing path planning with obstacle avoidance and in performing trajectory tracking. In all the aforementioned tasks, the quality of the paths generated by the proposed planner is superior to that of previous approaches and to its counterpart C-Space based RRT* approach, while the paths are generated in substantially less time.

Neighbor Coverage and Bandwidth Aware Multiple Disjoint Path Discovery in Wireless Mesh Networks

With the ease of appending new nodes without re-installing the whole network, the Internet of Things (IoT) builds several smart applications on Wireless Mesh Network (WMN). One of the important aspects of integrating WMN and smart IoT applications is to provide an energy-efficient and reliable routing protocol. Seeking the communication route that delivers the high-quality stream quickly over WMN is an important issue, but the maximum utilization of a single high-quality path leads to poor throughput and large communication delay, including route discovery and data forwarding delay. The broadcasting mechanism creates redundant transmissions of control packets into the network and reinitializes the blind route discovery process due to link disconnections leading to network resource constraints and high delay during the route discovery process. Moreover, the congestion in the communication route incurs data transmission latency. This paper proposes the Multiple Disjoint Path Determination (MDPD) mechanism based on-demand routing in WMN to formulate the path discovery and data transmission latency. Reducing the neighbor list into the uncommon neighbor set reduces the unnecessary latency in route discovery, and deriving high capacity multiple disjoint communication routes reduce the communication delay in the proposed work. The proposed work employs the queue dynamics in queuing delay, which mainly provides adaptability to the dynamics in network capacity and efficient diversity paths to the gateway node to infer the available bandwidth and optimize the network traffic. To fully utilize the advantage of heterogeneous routers, it disables the flooding of control packets across the stable mesh routers, excluding the initial route discovery process, because it enables the available route storage system in each mesh router. Hence, the proposed work efficiently supports wireless broadband internet access with reduced delay and control overhead. The simulation results demonstrate the fast detection of the multiple disjoint routes and data traffic optimization over the discovered disjoint routes in the proposed MDPD mechanism over WMN.

A Vision-Based Approach for Autonomous Motion in Cluttered Environments

In order to complete various tasks automatically, robots need to have onboard sensors to gain the ability to move autonomously in complex environments. Here, we propose a combined strategy to achieve the real-time, safe, and smooth autonomous motion of robots in complex environments. The strategy consists of the building of an occupancy grid map of the environment in real time via the binocular system, followed by planning a smooth and safe path based on our proposed new motion-planning algorithm. The binocular system, which is small in size and lightweight, can provide reliable robot position, attitude, and obstacle information, enabling the establishment of an occupancy grid map in real time. Our proposed new algorithm can generate a high-quality path by using the gradient information of the ESDF (Euclidean Signed Distance Functions) value to adjust the waypoints. Compared with the reported motion-planning algorithm, our proposed algorithm possesses two advantages: (i) ensuring the security of the entire path, rather than that of the waypoints; and (ii) presenting a fast calculation method for the ESDF value of the path points, one which avoids the time-consuming construction of the ESDF map of the environment. Experimental and simulation results demonstrate that the proposed method can realize the safe and smooth autonomous motion of the robot in a complex environment in real time. Therefore, our proposed approach shows great potential in the application of robotic autonomous motion tasks.