Multi-objective Path Planning Research Articles

Research on Path Planning for Unmanned Surface Vessels Based on AIS Data

To address the issues of poor endpoint convergence and suboptimal path quality in global path planning for unmanned surface vessels (USVs), this paper proposes an endpoint-convergence oriented improved genetic algorithm based on AIS data. Firstly, utilizing a genetic algorithm with an endpoint-convergence objective function, a set of paths with strong endpoint convergence is selected. Secondly, to overcome the limitations of a single optimization algorithm, a multi-objective path planning approach is employed using a genetic algorithm, thereby improving the distance and trajectory smoothness in global path planning for unmanned ships. Finally, the planned paths are evaluated by comparing them with the real paths from AIS data. The simulation results demonstrate that the proposed method outperforms other traditional algorithms in terms of average path turn count and turn angle, reducing them by an average of 35.41% and 35.72%, respectively. Moreover, the proposed method exhibits the smallest error compared to the real trajectories, with an average reduction of 18.26%. These results validate the effectiveness and rationality of the proposed approach in improving path quality, reducing path turn count, and achieving better alignment with actual trajectories.

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

Efficient navigation of multiple autonomous underwater vehicles (AUVs) plays an important role in monitoring underwater and off-shore environments. It has encountered challenges when AUVs work in complex underwater environments. Traditional swarm intelligence (SI) optimization algorithms have limitations such as insufficient path exploration ability, susceptibility to local optima, and difficulty in convergence. To address these issues, we propose an improved multi-objective manta ray foraging optimization (IMMRFO) method, which can improve the accuracy of trajectory planning through a comprehensive three-stage approach. Firstly, basic model sets are established, including a three-dimensional ocean terrain model, a threat source model, the physical constraints of AUV, path smoothing constraints, and spatiotemporal coordination constraints. Secondly, an innovative chaotic mapping technique is introduced to initialize the position of the manta ray population. Moreover, an adaptive rolling factor “S” is introduced from the manta rays’ rolling foraging. This allows the collaborative-vehicle population to jump out of local optima through “collaborative rolling." In the processes of manta ray chain feeding and manta ray spiral feeding, Cauchy reverse learning is integrated to broaden the search space and enhance the global optimization ability. The optimal Pareto front is then obtained using non-dominated sorting. Finally, the position of the manta ray population is mapped to the spatial positions of multi-AUVs, and cubic spline functions are used to optimize the trajectory of multi-AUVs. Through detailed analysis and comparison with five existing multi-objective optimization algorithms, it is found that the IMMRFO algorithm proposed in this paper can significantly reduce the average planned path length by 3.1~9.18 km in the path length target and reduce the average cost by 18.34~321.872 in the cost target. In an actual off-shore measurement process, IMMRFO enables AUVs to effectively bypass obstacles and threat sources, reduce risk costs, and improve mobile surveillance safety.

Enhanced ant colony algorithm with obstacle avoidance strategy for multi-objective path planning of mobile robots

Path planning with multiple evaluation metrics makes the motion of a robot realistic, but the contradiction between the metrics and the lack of global search and obstacle avoidance capabilities increases the difficulty of obtaining the optimization solution. To solve these problems, an enhanced ant colony algorithm (EACA) with an obstacle avoidance strategy is proposed in this article. First, the path planning model is constructed, and strict movement rules are designed. Secondly, the EACA with global search, balancing the contradiction between metrics, is designed. The dynamic regulation of pheromone concentration and the mechanism of fluctuating pheromone distribution are explored, heuristic information is optimized and the path planning effect is enhanced. Finally, a new mechanism of away-from-obstacles is proposed as the obstacle avoidance strategy, which ensures a reasonable safe distance. Comparative simulations on several different maps validate the performance of EACA with the obstacle avoidance strategy for planning robot movement paths.

Comprehensive Ocean Information-Enabled AUV Motion Planning Based on Reinforcement Learning

Motion planning based on the reinforcement learning algorithms of the autonomous underwater vehicle (AUV) has shown great potential. Motion planning algorithms are primarily utilized for path planning and trajectory-tracking. However, prior studies have been confronted with some limitations. The time-varying ocean current affects algorithmic sampling and AUV motion and then leads to an overestimation error during path planning. In addition, the ocean current makes it easy to fall into local optima during trajectory planning. To address these problems, this paper presents a reinforcement learning-based motion planning algorithm with comprehensive ocean information (RLBMPA-COI). First, we introduce real ocean data to construct a time-varying ocean current motion model. Then, comprehensive ocean information and AUV motion position are introduced, and the objective function is optimized in the state-action value network to reduce overestimation errors. Finally, state transfer and reward functions are designed based on real ocean current data to achieve multi-objective path planning and adaptive event triggering in trajectorytracking to improve robustness and adaptability. The numerical simulation results show that the proposed algorithm has a better path planning ability and a more robust trajectory-tracking effect than those of traditional reinforcement learning algorithms.

Hybrid IACO-A*-PSO optimization algorithm for solving multiobjective path planning problem of mobile robot in radioactive environment

Mobile robots are receiving significant interest in the nuclear energy industry because of their potential intelligence features and efficient operation. Mobile robot path planning (PP) in a radioactive environment can be considered as finding a collision-free path constrained by the path length, cumulative radiation dose rate and energy consumption. To solve this multiobjective path planning (MOPP) problem, we propose a hybrid algorithm based on an improved ant colony optimization (IACO), the A* algorithm and particle swarm optimization (IACO-A*-PSO). First, a modified A* algorithm is presented to find a suboptimal path, which is used to improve the initial ACO pheromone. Next, an improvement of the adaptive heuristic function and pheromone update rule based on an elitist system is proposed to balance the global search ability and convergence speed of the ant colony algorithm. Finally, PSO is used to obtain the optimal ACO control parameters and multiobjective weight coefficients. To evaluate the efficiency of the proposed algorithm, a comparative study has been performed between the proposed IACO-A*-PSO, the A*, ACO, and IACO algorithms and a genetic algorithm (GA) in four radioactive indoor environments with different sizes, radioactive sources and obstacles. The simulation results show that the proposed algorithm has the best comprehensive performance and stronger adaptability to different environments than the A*, ACO, IACO and GA algorithms in terms of the path length, cumulative radiation dose, energy consumption, runtime and success rate simultaneously. This demonstrates that the proposed algorithm is an effective method for solving the MOPP problem of mobile robot in radioactive environment, which is beneficial for improving the safety and reliability of robots in nuclear energy.

Multi-objective path planning for mobile robot in nuclear accident environment based on improved ant colony optimization with modified A∗

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A∗ (IACO-A∗) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A∗ algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A∗ has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A∗ algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A∗ algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.

ACO+PSO+A*: A bi-layer hybrid algorithm for multi-task path planning of an AUV

Autonomous underwater vehicle (AUV) plays a great role in the ocean engineering, and path planning is one of its key technologies. For such scenarios as oil spill detection, AUV should execute multiple tasks, which become more challenging due to the 3D ocean environment with obstacles. To solve the multi-task path planning problem of AUV, this paper first proposes a bi-level multi-objective path planning model aimed at minimizing the path length and dangerous distance. Then, a bi-layer hybrid algorithm is developed to solve the above model. In this algorithm, ant colony optimization algorithm (ACO) is adopted to generate a task sequence of the upper level model in the outer layer; particle swarm optimization algorithm (PSO) is employed to produce some waypoints between two adjacent tasks, and A* algorithm is used to generate a collision-free path of the lower level model based on waypoints in the inner layer. After that, the collision-free path in the inner layer is feedback to the ACO in the out layer to update its pheromone, and ACO yield a better task sequence in the next iteration, thus obtaining the shortest collision-free path traversing all tasks. Finally, PSO+A* and A* algorithms, together with the proposed bi-layer hybrid algorithm and some two-stage optimization algorithms are compared, respectively. Empirical results show that the proposed algorithm can produce an optimal collision-free path with shorter length and higher security.

Hybrid Optimization Based Multi-Objective Path Planning Framework for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been touted as a cost-effective way to follow sensitive resources across wide areas. The extensive wireless sensor networks that UAVs travel through have allowed them to acquire exceptional amounts of data. The path planning of UAV operations in low-altitude urban environments is the main emphasis of this research. Regarding two objectives, namely journey distance and safety level, a Multi-Objective Path Planning (MOPP) framework is introduced. A unique hybrid optimization approach is used to optimally find the best flight path between adjacent acquisition stations based on these MOPPs. The conceptual fusion of traditional DHOA with WOA results in the suggested hybrid model known as Deer Hunter Updated Whale Optimization (DHUWO). Ultimately, the projected collision-free optimal path framework referred to Multi-Objective Path Planning with DHUWO (MOPP-DHUWO) model is compared over the existing models in terms of safety level and distance as well.

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

AbstractPath planning is a key research issue in the field of unmanned aerial vehicle (UAV) applications. In practical applications, multi-objective path planning is usually required for multi-UAVs, so this paper proposes the improved balanced artificial bee colony (IB-ABC) algorithm to optimize multi-objective path planning. The algorithm adopts the ABC algorithm that emphasizes the global search capability, which is based on iterative feedback information. It uses single-element points, multi-element points, and iteration constraints to optimize the strategies of employed bees, follower bees, and scout bees, respectively. In terms of time and priority, simulation experiments prove that the IB-ABC algorithm can balance local and global search capabilities, accelerate the speed of convergence, and realize multi-UAV path planning in complex mountain environments.

Multi-source and multi-objective path planning based on genetic optimized long short-term memory neural network model

Multi-source and multi-objective path planning based on genetic optimized long short-term memory neural network model

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

With the development of artificial intelligence, path planning of Autonomous Mobile Robot (AMR) has been a research hotspot in recent years. This paper proposes the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. Firstly, the evaluation function is improved to make the convergence of A* algorithm faster. Secondly, the unnecessary nodes of the A* algorithm are removed, meanwhile only the necessary inflection points are retained for path planning. Thirdly, the improved A* algorithm combined with the greedy algorithm is applied to multi-objective point planning. Finally, path planning is performed for five target nodes in a warehouse environment to compare path lengths, turn angles and other parameters. The simulation results show that the proposed algorithm is smoother and the path length is reduced by about 5%. The results show that the proposed method can reduce a certain path length.

Design of Travel Route Identification and Scheduling System Based on Artificial Intelligence-Aided Image Segmentation.

This study designs a travel recognition and scheduling system using artificial intelligence and image segmentation techniques. To address the problem of low division quality of current point division algorithms, this study proposes a streaming graph division model based on a sliding window (GraphWin), which dynamically adjusts the amount of information (vertex degree information and adjacency information) referenced at each division according to the current division quality and division time by introducing a sliding window mechanism, to achieve the highest possible division while allowing loss of certain division efficiency. The goal is to improve the division quality as much as possible while allowing a certain loss of division efficiency. To meet the user's need to travel through multiple destinations with the shortest route, this thesis proposes a deep reinforcement learning actor-critic (AC)-based multiobjective point path planning algorithm. The algorithm builds a strategy network and an evaluation network based on actor-critic's multiobjective point path planning, updates the strategy network and evaluation network parameters using AC optimization training, reduces the reliance of the algorithm model on a large amount of high-quality label data, and speeds up the convergence speed of the deep reinforcement learning algorithm by pretraining, finally completing the multiobjective point access sequential path planning task. Finally, the personalized travel recommendation system is designed and implemented, and the system performance analysis is conducted to clarify the system requirements in terms of functional and nonfunctional aspects: the system architecture, system functional modules, and database tables are designed to conduct use case testing of the main functional modules of the system, and the usability of the attraction recommendation algorithm is verified through the concrete implementation of the functional modules such as attraction recommendation in the system.

A comparative study on Ant Colony Optimization algorithm approaches for solving multi-objective path planning problems in case of unmanned surface vehicles

The increased use of unmanned surface vehicles (USVs) to various applications in complex environments has led to the need of novel path planning approaches that can effectively address multi-objective path planning problems. Ant Colony Optimization (ACO) is a well-known and popular path search algorithm for graph-based problems that finds the shortest path. It has been widely used for solving path planning problems for robotic vehicles, and unmanned aircraft and surface vehicles, among others. To this end, this study focuses on extending the use of ACO to cope with multiple objectives by using fuzzy inference systems, such as Mamdani and Takagi–Sugeno–Kang (TSK), or the root mean square error (RMSE) criterion adopted to the state-of-the-art SIGPA algorithm. A comparative evaluation of these approaches is conducted in the context of solving different scenarios of multi-objective USV path planning problems. The results showed that ACO with Mamdani reached better performance in terms of solution quality compared to the other approaches under examination, while ACO with RMSE presented higher convergence speed and ACO with TSK balanced better among convergence speed and solution quality. Thus, each of the proposed approaches can be considered for multi-objective path planning of USVs depending the application needs.

Multimodal multi-objective evolutionary algorithm for multiple path planning

The multi-objective path planning problem has received much attention recently. Traditional solving methods try to find a single optimal path without considering the multiformity of the paths. In this study, we first analyze the situation that several different paths may have the same objective values, termed as multi-modal minimum path problems. To address these problems, we propose a novel solution-encoding method, which decreases the size of decision-space greatly. Then, to maintain the population diversity in the decision space, we propose an environmental selection strategy, in which the duplicate solutions are deleted first and then a second-selection method is adopted. Finally, an effective multi-objective evolutionary algorithm based on the special environmental selection is proposed, termed MMEA-SES. Through the experiments, the proposed method is proved effective and efficient compared to other state-of-the-art algorithms for multimodal multi-objective path planning.

An evolutionary multi-objective path planning of a fleet of ASVs for patrolling water resources

The rapid increase of human activities with direct influence on the environment has motivated the global awareness of the need to efficiently monitor the natural resources. Among the wide range of problems addressed, such as overuse of agrochemicals, uncontrolled waste, etc., the contamination of water resources plays a protagonist role, given its close links with biodiversity and the food chain. Water monitoring is considered one of the most efficient ways to deal with these problems, especially through the use of autonomous vehicles, which can boost the capabilities and efficiency of the monitoring routines with appropriate strategies. In this work, the monitoring problem is addressed by means of the Non-Homogeneous Patrolling Problem with closed circuits. This problem has a great computational complexity, especially when multiple targets are included in a monitoring mission. A formulation based on closed metric graphs and the application of a multi-objective genetic algorithm is proposed to provide Pareto-efficient monitoring solutions for a variable number of Autonomous Surface Vehicles. To address the multi-agent, multi-objective and constrained paradigm, efficient genetic operators have been designed for the generation of valid solutions in an affordable time. The method results in Pareto-efficient solutions for scenarios with disjoint and uncorrelated objectives, which outperform the fitness of other solutions by a factor of 2, on average. The results provide decision makers a method to find different non-dominated strategies depending on the monitoring needs, depending on fleet and vehicle size.

Multi-Objective Path-Based D* Lite

Incremental graph search algorithms such as D* Lite reuse previous, and perhaps partial, searches to expedite subsequent path planning tasks. In this article, we are interested in developing incremental graph search algorithms for path finding problems to simultaneously optimize multiple objectives such as travel risk, arrival time, etc. This is challenging because in a multi-objective setting, the number of "Pareto-optimal" solutions can grow exponentially with respect to the size of the graph. This article presents a new multi-objective incremental search algorithm called Multi-Objective Path-Based D* Lite (MOPBD*) which leverages a path-based expansion strategy to prune dominated solutions. Additionally, we introduce a sub-optimal variant of MOPBD* to improve search efficiency while approximating the Pareto-optimal front. We numerically evaluate the performance of MOPBD* and its variants in various maps with two and three objectives. Results show that our approach is more efficient than search from scratch, and runs up to an order of magnitude faster than the existing incremental method for multi-objective path planning.

Multi-objective path planning for mobile robot with an improved artificial bee colony algorithm.

Effective path planning (PP) is the basis of autonomous navigation for mobile robots. Since the PP is an NP-hard problem, intelligent optimization algorithms have become a popular option to solve this problem. As a classic evolutionary algorithm, the artificial bee colony (ABC) algorithm has been applied to solve numerous realistic optimization problems. In this study, we propose an improved artificial bee colony algorithm (IMO-ABC) to deal with the multi-objective PP problem for a mobile robot. Path length and path safety were optimized as two objectives. Considering the complexity of the multi-objective PP problem, a well-environment model and a path encoding method are designed to make solutions feasible. In addition, a hybrid initialization strategy is applied to generate efficient feasible solutions. Subsequently, path-shortening and path-crossing operators are developed and embedded in the IMO-ABC algorithm. Meanwhile, a variable neighborhood local search strategy and a global search strategy, which could enhance exploitation and exploration, respectively, are proposed. Finally, representative maps including a real environment map are employed for simulation tests. The effectiveness of the proposed strategies is verified through numerous comparisons and statistical analyses. Simulation results show that the proposed IMO-ABC yields better solutions with respect to hypervolume and set coverage metrics for the later decision-maker.

Patrol robot path planning in nuclear power plant using an interval multi-objective particle swarm optimization algorithm

This paper presents an interval multi-objective path planning (PP) scheme for patrol robot in nuclear power plant. The purpose of this PP scheme is to find collision-free paths with the shortest length and smallest risk degree. Firstly, a novel workspace modeling method is proposed to describe the static PP environment of patrol robot in nuclear power plant. Then considering the conflicts of the shortest length and smallest risk degree, an interval multi-objective particle swarm optimization (IMOPSO) method is used. In the IMOPSO, an ingenious interval update law for the particle’s global best position and local best position based on the crowding distance of each risk degree interval is used to increase the diversity of population, and an iterative procedure is adopted to update the particle’s position when the found paths are collided with some existing obstacles. Finally, three representative simulation tests are used to verify the validity of proposed IMOPSO method. Results show that comparing with other three well-known multi-objective evolutionary algorithms, our proposed method has the advantages of finding a better Pareto optimal paths.

Improved Wolf Pack Algorithm for Solving a Class of Multi-Objective Route Optimization Problem

This paper considers a class of Multi-objective path planning and solving algorithms. The choice of step size is a very important factor that affects the iterative algorithm. This paper improves the classical Wolf Pack Algorithm (WPA) by designing an adaptive step size. This new improved wolf pack algorithm (IWPA) improves the classical wolf colony algorithm by designing an adaptive step size. It effectively solves the problem that the WPA may fall into a endless loop. The simulation results by MATLAB show that the IWPA has a good effectiveness than the WPA to solve multi-objective route optimization.

Production Logistics Management of Industrial Enterprises Based on Wavelet Neural Network

With an efficient production logistics system, intelligent manufacturers can reduce the investment in production, improve the stability and self-repair ability of production logistics, and strike a perfect balance between production scheduling and production logistics. This paper probes deep into the production logistics management (PLM) of industrial enterprises, and proposes a PLM model for such enterprises based on wavelet neural network (WNN). Firstly, the PLM system architecture of industrial enterprises was established, and the scheduling and task allocation principles were proposed for the collaboration of various subjects in the system. Based on curved time window, a multi-objective path planning and optimization model was established, under influencing factors like the dynamics of station demand and the maximum driving range of handling equipment. Simulation results show that the proposed model is effective in optimizing the path for industrial production logistics. The research results provide theoretical supports to the real-time optimization of PLM and rationalization of production scheduling in industrial enterprises.