Planning Algorithms Research Articles

Clustering-based hyper-heuristic algorithm for multi-region coverage path planning of heterogeneous UAVs

In the context of multi-heterogeneous UAV coverage path planning, an effective solution method has been proposed. Firstly, regions are set up as fully connected graphs which are cut into multiple subgraphs by spectral clustering method to assign tasks to multi-heterogeneous UAVs. Additionally, an RL-based hyper-heuristic algorithm is proposed. Heuristic space is parameterized by GNN which is trained with the reward provided by the optimization goal to automate design and enhance the heuristic metrics, avoiding the inefficiency and suboptimality of expert design and manual parameter tuning. Compared with existing methods, the proposed algorithm has a better performance in task completion time, execution time and deviation rate, which shows its potential application in the coverage path planning problem of multi-heterogeneous UAVs.

Full Coverage Path Planning for Torpedo-Type AUVs’ Marine Survey Confined in Convex Polygon Area

In this paper, we present a full coverage path planning (CPP) algorithm for the marine surveys conducted in the convex polygon shaped search area. The survey is supposed to carry out by torpedo-type AUVs (autonomous underwater vehicles). Due to their nonholonomic mechanical characteristics, these vehicles have nonzero minimum turning radius. For any given polygon shaped search area, it can always be partitioned into one or more convex polygons. With this in mind, this paper proposes a novel search algorithm called CbSPSA (Calculation based Shortest Path Search Algorithm) for full coverage of any given convex polygon shaped search area. By aligning the search inter-tracks alongside the edge with the minimum height, we can guarantee the minimum number of the vehicle’s turns. In addition, the proposed method can guarantee the planned path is strictly located inside the polygon area without overlapped or crossed path lines, and also has the total path length as short as possible. Considering the vehicle’s nonzero minimum turning radius, we also propose a sort of smoothing algorithm which can smooth the waypoint path searched by CbSPSA so that the vehicle can exactly follow it. The smoothed path is also guaranteed to be strictly located inside the polygon. Numerical simulation analyses are also carried out to verify the effectiveness of the proposed schemes.

Nonlinear dynamical social and political prediction algorithm for city planning and public participation using the impulse pattern formulation

A nonlinear-dynamical algorithm for city planning is proposed as an impulse pattern formulation (IPF) for predicting relevant parameters such as health, artistic freedom, or financial developments of different social or political stakeholders over the cause of a planning process. The IPF has already shown high predictive precision at low computational cost in musical instrument simulations, brain dynamics, and human–human interactions. The social and political IPF consists of three basic equations of system state developments, self-adaptation of stakeholders, two adaptive interactions, and external impact terms suitable for respective planning situations. Typical scenarios of stakeholder interactions and developments are modeled by adjusting a set of system parameters. These include stakeholder reaction to external input, enhanced system stability through self-adaptation, stakeholder convergence due to adaptive interaction, as well as complex dynamics in terms of fixed stakeholder impacts. A workflow for implementing the algorithm in real city planning scenarios is outlined. This workflow includes machine learning of a suitable set of parameters suggesting best-practice planning to aim at the desired development of the planning process and its output.

Designing an Autonomous Vehicle Using Sensor Fusion Based on Path Planning and Deep Learning Algorithms

Designing an Autonomous Vehicle Using Sensor Fusion Based on Path Planning and Deep Learning Algorithms

Robot Path Planning and Tracking with the Flower Pollination SearchOptimization Algorithm

Robot path planning and tracking involve two critical aspects of autonomous navigation systems: determining an optimal route for the robot to follow and ensuring that it accurately adheres to this path in real-time. Path planning focuses on generating a feasible and efficient trajectory from a starting point to a destination while considering obstacles, dynamic environments, and constraints. This paper investigates the effectiveness of the Flower Pollination Search Optimization (FPSO) algorithm for robot path planning and compares its performance with traditional algorithms such as A* Algorithm, Dijkstra, and Rapidlyexploring Random Tree (RRT). The FPSO algorithm was evaluated across three distinct scenarios, demonstrating superior performance in terms of path length, computation time, and path smoothness. In Scenario 1, FPSO achieved an optimized path length of 10.5 meters with a computation time of 3.2 seconds, a path smoothness score of 8.9, and a path efficiency of 95%. In Scenario 2, FPSO resulted in a path length of 12.3 meters and a computation time of 3.5 seconds, with a smoothness score of 8.7 and an efficiency of 93%. Scenario 3 showed FPSO's best performance, with a path length of 9.8 meters, computation time of 3.0 seconds, a smoothness score of 9.0, and a path efficiency of 96%. Comparatively, the A* Algorithm, Dijkstra, and RRT exhibited longer path lengths and higher computation times, with lower smoothness scores and efficiencies.

An improved whale optimization algorithm for multi-robot path planning

Multi-robot path planning is challenging and has increasingly attracted attention with its widespread applications. This article proposes an improved Whale Optimization Algorithm (WOA) with Refracted opposition-based learning and Quantum behaviour (RQWOA). The algorithm is able to plan smooth and collisionless paths for robots combining cubic spline interpolation and multi-robot coordination. A quantum behavioural mechanism is used to coordinate the evolution of the whale population during the variable phase to increase the population quality and balance the exploration and exploitation capabilities of the WOA. Simultaneously, refracted opposition-based learning is introduced to improve the algorithm's optimization accuracy and convergence speed. The RQWOA was compared with seven efficient algorithms in experiments on classical test functions and multi-robot path planning cases. The results of these methods were tested statistically. The experimental results indicate that the RQWOA has superior solution accuracy. The RQWOA is highly competitive in terms of pathlength and stability in solving multi-robot path planning problems.

Continuous–Discrete Student Psychology-Based Optimization Algorithm for Optimal Planning of Distributed Generators and Distribution Static Compensators in Power Distribution Network

Continuous–Discrete Student Psychology-Based Optimization Algorithm for Optimal Planning of Distributed Generators and Distribution Static Compensators in Power Distribution Network

Identification of patterns of the stress-strain state of a standard plastic tank for liquid mineral fertilizers

In this study, using the method of probabilistic deterministic planning (PDP), the optimum design parameters of a standard polyethylene tank used worldwide for transporting liquid mineral fertilizers (LMF) were determined. By the finite element method, the effect of the density of liquid mineral fertilizer, tank wall thickness and four motion modes (braking, acceleration, jump and landing) on the strength of standard polyethylene tanks was studied. According to the results of the study, the five most informative areas in the tank design were identified, for which the values of maximum stresses (σmax) were obtained: filler neck, pockets, walls, tap-in points and wall transition to the tank roof. As the LMF density increases, σmax in the tank increases linearly. Increasing the tank wall thickness by 1.5 times reduces the maximum stresses by 30 to 50 %. It was found that motion mode has a significant effect on the stress-strain state of a standard tank. The “heaviest” mode for a standard tank is “braking”. The “acceleration” motion mode causes σmax of no more than 60 % of the “braking” mode values. The “lightest” mode is “landing”, in which σmax is no more than 28 % relative to “braking”. Based on the PDP method, equations were derived for calculating maximum stresses depending on LMF density, wall thickness and motion mode of the tank. Nomograms were built that make it possible to quickly determine the wall thickness of a standard tank without calculations, depending on external factors. The results of the study can be used in practice when designing safe and durable tanks for transporting liquid mineral fertilizers.

Evolutionary computation for unmanned aerial vehicle path planning: a survey

Unmanned aerial vehicle (UAV) path planning aims to find the optimal flight path from the start point to the destination point for each aerial vehicle. With the rapid development of UAV technology, UAVs are required to tackle missions in increasingly complex environments. Consequently, UAV path planning encounters more challenges, causing traditional deterministic algorithms to struggle to find the optimal path within a certain time. Evolutionary computation (EC) is a series of nature-inspired methodologies and algorithms, which have shown effectiveness and efficiency in solving many complex optimization problems in real-world applications. Recently, EC algorithms have been effectively applied in UAV path planning and have shown encouraging performance in obtaining high-quality solutions. Therefore, it is crucial to review the related research experience and literature in the field of using EC for UAV path planning. This paper presents a comprehensive survey to showcase the existing studies on EC in UAV path planning, especially in complex environments. The paper first proposes a novel taxonomy to categorize the relevant studies into three different categories according to the complex environmental properties of the application scenarios. These environmental properties include complex search space, complex time control, and complex optimization objectives. Then, the EC algorithms for UAV path planning in these complex environments are further systematically surveyed as constrained search space and large-scale search space in complex search space, dynamic UAV path planning and multi-UAV concurrent path planning in complex time control, and expensive objective and multiple objectives in complex optimization objectives. Finally, some potential future research directions for applying EC algorithms to UAV path planning are presented and discussed.

Multi-Robot Navigation System Design Based on Proximal Policy Optimization Algorithm

The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to provide an innovative and effective method for global path planning of multi-robot navigation. It can plan paths with fewer path conflicts for all robots so that the overall navigation time for the robots to complete all tasks can be reduced. Compared with existing methods of global path planning for multi-robot navigation, this study proposes new perspectives and methods. It emphasizes reducing the number of path conflicts first to reduce the overall navigation time. The system consists of a localization unit, an environment map unit, a path planning unit, and an environment monitoring unit, which provides functions for calculating robot coordinates, generating preselected paths, selecting optimal path combinations, robot navigation, and environment monitoring. We use topological maps to simplify the map representation for multi-robot path planning so that the proposed method can perform path planning for more robots in more complex environments. The proximal policy optimization (PPO) is used as the algorithm for deep reinforcement learning. This study combines the path selection method of deep reinforcement learning with the A* algorithm, which effectively reduces the number of path conflicts in multi-robot path planning and improves the overall navigation time. In addition, we used the reciprocal velocity obstacles algorithm for local path planning in the robot, combined with the proposed global path planning method, to achieve complete and effective multi-robot navigation. Some simulation results in NVIDIA Isaac Sim show that for 1000 multi-robot navigation tasks, the maximum number of path conflicts that can be reduced is 60,375 under nine simulation conditions.

A hybrid sampling-based RRT* path planning algorithm for autonomous mobile robot navigation

The path-planning algorithms for autonomous mobile robot navigation are crucial, often relying on sampling-based methods. RRT* is a robust, sampling-based path planning algorithm. The sampling process in RRT* plays a pivotal role, where uniform sampling can lead to slow convergence, while non-uniform sampling offers faster convergence but may struggle in complex environments due to its limited exploration. Thus, achieving a balance between exploitation and exploration is essential when selecting the sampling method for the RRT* path-planning algorithm. To address this issue, this research paper introduces Hybrid-RRT*, a path planning method that utilizes hybrid sampling. This unique approach generates samples using both non-uniform and uniform samplers. Hybrid-RRT* is evaluated against three baseline path planning algorithms—RRT*-N, Informed RRT*, and RRT*—across three different 384x384 2D simulation environments. Compared to these baseline methods, Hybrid-RRT* achieves superior results across all five performance metrics: convergence rate, success rate, number of nodes visited, path length, and planning time. According to the numerical results, the proposed algorithm achieves a faster average convergence rate that is 76.14% higher than RRT*, 24% higher than Informed RRT*, and 3.33% higher than RRT*-N. Moreover, it reduces node exploration by an average of 48.53% compared to RRT* and 40.83% compared to Informed RRT*. The simulation results demonstrate that the proposed Hybrid-RRT* algorithm effectively addresses the issue of slow convergence with uniform sampling and the challenge of limited exploration with non-uniform sampling methods.

Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach.

A Multiproject and Multilevel Plan Management Model Based on a Hybrid Program Evaluation and Review Technique and Reinforcement Learning Mechanism

It is very difficult for manufacturing enterprises to achieve automatic coordination of multiproject and multilevel planning when they are unable to make large-scale resource adjustments. In addition, planning and coordination work mostly relies on human experience, and inaccurate planning often occurs. This article innovatively proposes the PERT-RP-DDPGAO algorithm, which effectively combines the program evaluation and review technique (PERT) and deep deterministic policy gradient (DDPG) technology. Innovatively using matrix computing, the resource plan (RP) itself is used for the first time as an intelligent agent for reinforcement learning, achieving automatic coordination of multilevel plans. Through experiments, this algorithm can achieve automatic planning and has interpretability in management theory. To solve the problem of continuous control, the second half of the new algorithm adopts the DDPG algorithm, which has advantages in convergence and response speed compared to traditional reinforcement learning algorithms and heuristic algorithms. The response time of this algorithm is 3.0% lower than the traditional deep Q-network (DQN) algorithm and more than 8.4% shorter than the heuristic algorithm.

GVP-RRT: a grid based variable probability Rapidly-exploring Random Tree algorithm for AGV path planning

In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5–69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.

Path Planning for Floor Cleaning Robots Based on MATLAB Simulink

The increasing popularity of robotic vacuum cleaners in households in recent years has established them as indispensable devices in many homes and has also demonstrated their potential in various other domains. Nevertheless, numerous robotic sweepers available in the market exhibit certain constraints, including repetitive sweeping of the same area, collisions, or entrapment by obstacles. These limitations restrict their effectiveness in specific settings, such as hospitals, where comprehensive cleaning is essential. In an effort to enhance the efficiency and effectiveness of robotic sweepers, a research project was undertaken to investigate the feasibility of navigating a two-dimensional environment utilizing plannerAStar, a path planning algorithm in MATLAB. The study findings indicated a direct correlation between the quantity of goals established through the A-planner and the percentage of independent obstacles effectively circumvented, as well as the area traversed in a distinct "side-to-side" configuration. The enhanced pattern variant exhibited greater consistency and coverage in an environment characterized by walls and corridors. Improved path planning algorithms have been developed utilizing the plannerAStar function in MATLAB Simulink to address issues related to collisions and obstructions encountered by swinging robots in real-world scenarios. Simulation results demonstrate that the path planning algorithm utilizing plannerAStar significantly decreases the path length by 34%, reduces the number of collisions by 75%, and lowers the collision rate by 10% in comparison to the fundamental randomized algorithm. The findings indicate that utilizing the plannerAStar algorithm for path planning is anticipated to notably decrease both redundant collisions and obstacle collisions for the robot, consequently enhancing the robot's operational efficiency.

Safe path planning of mobile robot based on improved particle swarm optimization

Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

Review on Security Range Perception Methods and Path-Planning Techniques for Substation Mobile Robots

The use of mobile robots in substations improves maintenance efficiency and ensures the personal safety of staff working at substations, which is a trend in the development of technologies. Strong electric and solid magnetic fields around high-voltage equipment in substations may lead to the breakdown and failure of inspection devices. Therefore, safe operation range measurement and coordinated planning are key factors in ensuring the safe operation of substations. This paper first summarizes the current developments that are occurring in the field of fixed and mobile safe operating range sensing methods for substations, such as ultra-wideband technology, the two-way time flight method, and deep learning image processing algorithms. Secondly, this paper introduces path-planning algorithms based on safety range sensing and analyzes the adaptability of global search methods based on a priori information, local planning algorithms, and sensor information in substation scenarios. Finally, in view of the limitations of the existing range awareness and path-planning methods, we investigate the problems that occur in the dynamic changes in equipment safety zones and the frequent switching of operation scenarios in substations. Furthermore, we explore a new type of barrier and its automatic arrangement system to improve the performance of distance control and path planning in substation scenarios.

Optimization method for collision avoidance paths of inland ships based on improved ant colony algorithm

With the rise of artificial intelligence, ships are gradually becoming intelligent. Ship path planning, as the foundation of ship intelligence, has become a current research hotspot. To achieve the planning of ship collision avoidance paths, a hybrid ant colony algorithm and artificial potential field for optimal safety path planning was proposed. The model makes up for the disadvantages of the slow convergence rate and the easy local optimum. At the same time, the ant colony algorithm in turn makes up for the poor global search ability of the artificial potential field method, so that the algorithm can achieve accurate path planning. The test results show that in a simple environment, the hybrid ant colony algorithm, improved tangent and Dijkstra algorithm, and improved fast extended random tree * algorithm iterated about 7, 25, and 40 times respectively before starting to converge; In complex environments, the improved tangent and Dijkstra algorithms do not converge, while the improved fast expanding random tree * algorithm and hybrid ant colony algorithm iterate about 40 and 8 times respectively to begin convergence. It is clear that the mixed ant colony algorithm converges fast and can obtain the optimal path in the shortest time. The number of unsafe path points for the optimal path in a simple environment is 6, 1, and 0, respectively, for the improved tangent and Dijkstra algorithm, the improved fast expanding random tree * algorithm, and the hybrid ant colony algorithm; The number of unsafe path points for the optimal path in a complex environment is 9, 3, and 0, respectively; It can be seen that the path planned by the hybrid ant colony algorithm has higher security. The above results show that the mixed ant colony algorithm can effectively shorten the search time of optimal paths while improving the security of pathways.

Automated parking trajectory planning: a hybrid curve search and nonlinear optimisation approach

ABSTRACT This paper focuses on the problem of automatic parking trajectory planning in unstructured scenarios. The proposed algorithm adopts a combination of graph search and optimization strategy. First, A* search is utilized to obtain paths connecting the global start and end points. Second, the path is divided into three segments and subpaths are searched using the hybrid A* algorithm and the proposed A*-iBspline algorithm. Finally, the three subpaths are merged and a valid initial guess is generated for the OCP. Additionally, an iterative framework is used to improve the quality of the OCP solution. Simulation results show that the proposed algorithm has a significant advantage over existing methods in terms of time and applicability.

Intelligent path planning for civil infrastructure inspection with multi-rotor aerial vehicles

This paper presents the development of algorithms for high-level control and intelligent path planning of multi-rotor aerial vehicles (MAVs) in the tasks of inspecting civil infrastructure. After revisiting the multicopter modeling, we describe the hierarchy of high-level control for MAVs and develop optimization algorithms for generating optimal paths and enabling automatic flight during inspection tasks, making use of the digital twin technology. A co-simulation framework is then established to simulate and evaluate inspection mission scenarios, integrating these essential components. Real-world examples from built infrastructure illustrate this concept. An advantage of this approach is its ability to rigorously test, validate, verify, and evaluate MAV operations under abnormal conditions without requiring physical implementation or field tests. This significantly reduces testing efforts throughout the development cycle, ensuring optimal cooperation, safety, smoothness, fault tolerance, and energy efficiency. The methodology is validated through simulations and real-world inspection of a monorail bridge.