Mobile Robot Path Planning Research Articles

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.

A Robot Path Planning Method Based on Improved Genetic Algorithm and Improved Dynamic Window Approach

Intelligent mobile robots play an important role in the green and efficient operation of warehouses and have a significant impact on the natural environment and the economy. Path planning technology is one of the key technologies to achieve intelligent mobile robots. In order to improve the pickup efficiency and to reduce the resource waste and carbon emissions in logistics, we investigate the robot path optimization problem. Under the guidance of the sustainable development theory, we aim to achieve the goal of environmental social governance by shortening and smoothing robot paths. To improve the robot’s ability to avoid dynamic obstacles and to quickly solve shorter and smoother robot paths, we propose a fusion algorithm based on the improved genetic algorithm and the dynamic window approach. By doing so, we can improve the efficiency of warehouse operations and reduce logistics costs, whilst also contributing to the realization of a green supply chain. In this paper, we implement an improved fusion algorithm for mobile robot path planning and illustrate the superiority of our algorithm through comparative experiments. The authors’ findings and conclusions emphasize the importance of using advanced algorithms to optimize robot paths and suggest potential avenues for future research.

A new approach for mobile robot path planning based on RRT algorithm

A novel route planning method based on the RRT algorithm is proposed in this study. To enhance the structure of the state tree, a general-purpose pseudorandom number generator is inserted into RRT. In addition, the proposed method includes a distance restriction that helps reduce the number of possible candidate nodes. MATLAB has been utilized to examine the efficacy of the proposed technique using three different types of maps. The simulation results demonstrate that the revised method is advantageous for route planning due to its superior convergence and efficacy over the original.

Hierarchical Topology Map with Explicit Corridor for global path planning of mobile robots

Hierarchical Topology Map with Explicit Corridor for global path planning of mobile robots

Enhanced Teaching–Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem

This research proposes an enhanced teaching–learning based optimization (ETLBO) algorithm to realize an efficient path planning for a mobile robot. Four strategies are introduced to accelerate the teaching–learning based optimization (TLBO) algorithm and optimize the final path. Firstly, a divide-and-conquer design, coupled with the Dijkstra method, is developed to realize the problem transformation so as to pave the way for algorithm deployment. Secondly, the interpolation method is utilized to smooth the traveling route as well as to reduce the problem dimensionality. Thirdly, an opposition-based learning strategy is embedded into the algorithm initialization to create initial solutions with high qualities. Finally, a novel, individual update method is established by hybridizing the TLBO algorithm with differential evolution (DE). Simulations on benchmark functions and MRPP problems are conducted, and the proposed ELTBO is compared with some state-of-the-art algorithms. The results show that, in most cases, the ELTBO algorithm performs better than other algorithms in both optimality and efficiency.

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.

Dynamic path planning of mobile robot based on improved simulated annealing algorithm

Dynamic path planning for mobile robots is an urgent issue that needs to be solved because of the growing use of mobile robots in daily life and industrial operations. This work focuses on avoiding moving obstacles in dynamic situations. The computational effort required by some current algorithms makes them difficult to utilize for path planning in dynamic situations whilst the computational effort required by other methods makes them simple yet prone to local minima. In this paper, an improved simulated annealing (SA) algorithm for dynamic path planning is proposed. To reduce its computational effort, the initial path selection method and deletion operation are introduced. Simulation results show the improved SA algorithm outperforms other algorithms and provides optimal solutions in static and dynamic environments.

Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm.

With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (GWO)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the GWO algorithm is modified by introducing a two-dimensional Tent-Sine coupled chaotic mapping in this paper. This guarantees that the GWO algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization Algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the GWO algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-GWO algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-GWO algorithm based on the visual-field line detection technique of Bresenham's line algorithm, reduces the number of iterations of the EWB-GWO algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-GWO algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.

A review: On bio-inspired optimization methods for path planning of mobile robot

In recent years, researchers have paid attention to algorithms inspired by nature where these algorithms have proven their efficiency in solving many optimization problems, especially in complex situations, due to their high precision, speed of optimization, simplicity of the techniques, and efficiency in agent cooperation. The primary issue in the field of autonomous mobile robots is navigation. An autonomous robot's navigation ability is one of its most crucial and distinctive features. There are four components of the autonomous robot navigation issue: vision, localization, cognition, and path planning. Many academics have used bio-inspired methods to solve navigation difficulties in mobile robots in recent years, including path planning where they considered the path planning problem as an optimization problem. Many novel path-planning methods have been created, and those using bio-inspired algorithms have received much attention. These algorithms have been shown to be useful in solving complex problems where the solution space isn't always adequately characterized and the problem necessitates solving complex mathematical models of live processes. More intricate optimization methods that transcend the constraints of classical procedures must be applied as the complexities of the optimization problem increase. This work contributes to presenting a group of algorithms inspired by nature that has been used to solve the problem of planning the path of mobile robots, and then making a comparison between these algorithms based on three factors (cost, time, and path length). Choosing an appropriate path-planning method contributes to ensuring safe and efficacious navigation from one point to another.

A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning.

A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimization (MPSO) and artificial bee colony algorithm (ABC), is conducted with the multimodal routing strategy to enhance the safety and the cost issue for the mobile robot path planning problem. The evolution is divided into three stages, where the first is the independent evolutionary stage, with the same evolution strategies for each swarm. The second is the fusion stage, in which individuals are evolved hierarchically in the parasitism structure. Finally, in the interaction stage, a multi-swarm-elite strategy is used to filter the information through a predefined cross function among swarms. Meanwhile, the segment obstacle-avoiding strategy is proposed to accelerate the searching speed with two fitness functions. The best path is selected according to the performance on the safety and consumption issues. The introduced algorithm is examined with different obstacle allocations and simulated in the real routing environment compared with some typical algorithms. The results verify the productiveness of the parasitism-relation-based structure and the stage-based evolution strategy in path planning.

Online path planning of cooperative mobile robots in unknown environments using improved Q‐Learning and adaptive artificial potential field

Online path planning in unknown environments with obstacles is a challenging issue in mobile robot systems. Cooperative mobile robots have received much attention in recent years due to their robustness and ability to perform more complex tasks and operations faster. In this article, by unifying the Dyna Q-learning and a novel concept (introduced as a feature matrix) an improved Q-learning is proposed that accelerates the learning process even with poor choice of parameters. In addition, to overcoming the local minima, an adaptive artificial potential field method is developed, so that two parameters of the new concept named the virtual obstacle are controlled using the massachusetts institute of technology (MIT) method to navigate the mobile robot in the proper direction. Then, with proper utilizing of the improved Q-learning and adaptive artificial potential field method, the path planning is performed online effectively while target tracking and collision avoidance are guaranteed. Finally, the performance of the proposed method is tested on decentralized cooperative mobile robots. The simulation results showed optimal path planning in terms of the distance in an unknown environment and collision avoidance with the obstacles. In addition, getting out of the local minima (i.e. immobility) is guaranteed in all situations.

Improved ant colony algorithm for path planning of mobile robots based on compound prediction mechanism

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots

In terms of mobile robot path planning, the traditional A* algorithm has the following problems: a long searching time, an excessive number of redundant nodes, and too many path-turning points. As a result, the shortest path obtained from planning may not be the optimal movement route of actual robots, and it will accelerate the hardware loss of robots. To address the aforementioned problems, a fusion algorithm for path planning, combining the improved A* algorithm with segmented second-order Bézier curves, is proposed in this paper. On the one hand, the improved A* algorithm is presented to reduce unnecessary expansion nodes and shorten the search time, which was achieved from three aspects: (1) the traditional 8-neighborhood search strategy was adjusted to 5-neighborhood according to the orientation of the target point relative to the current node; (2) the dynamic weighting factor of the heuristic function was introduced into the evaluation function of the traditional A* algorithm; and (3) the key node extraction strategy was designed to reduce the redundant nodes of the optimal path. On the other hand, the optimal path planned by the improved A* algorithm was smoothed using segmented second-order Bézier curves. The simulation results show that the improved A* algorithm can effectively reduce the search time and redundant nodes and the fusion algorithm can reduce the path curvature and path length to a certain extent, improving path safety.

Improved Informed-RRT* Based Path Planning and Trajectory Optimization for Mobile Robots

Improved Informed-RRT* Based Path Planning and Trajectory Optimization for Mobile Robots

Research on path planning of mobile robot based on improved genetic algorithm

The basic genetic algorithm usually has the following problems when solving robot path planning problems. First, the chromosome (path) of the first generation is randomly generated, so its fitness is often too high, resulting in too slow convergence speed and even unable to obtain the global optimal solution. Second, in the crossover operator, the selection of crossover points is often random, which cannot ensure that the fitness of cross generated offspring is better than that of their parents. Finally, in the mutation stage, because the traditional mutation strategy is to replace the selected mutation node in the individual chromosome with a random node, the offspring path generated by the mutation operator is likely to be discontinuous, so it cannot reflect the effectiveness of the mutation operator. Based on the mentioned contents, some improved strategies are proposed for the discussed three shortcomings of the basic genetic algorithm. First, a new generation mechanism of the first generation is constructed by simulating the path finding rules of ants in ant colony optimization, which greatly improves the quality of the first generation. Second, during crossover operator, the chromosomes of the two parents are disconnected at the same node, and the optimal chromosome fragments in each segment are combined based on greed, so as to gather all the excellent gene fragments of the parents in an individual as much as possible, improve the quality of the offspring after crossover operator, and speed up the convergence speed. Finally, a new mutation strategy is proposed to eliminate redundant sections and the number of corners, so that the individuals always can be mutated in a good direction. A large number of simulation results show that the improved genetic algorithm is effective in solving the robot path planning problem, and the overall performance is better than the basic genetic algorithm and some other improved genetic algorithms. Finally, by modeling and simulating the real enterprise factory environment based on the ROS platform, the experimental results also verify the effectiveness and feasibility of the improved genetic algorithm.

A review of methodologies for path planning and optimization of mobile robots

This research paper provides a comprehensive review of methodologies for path planning and optimization of mobile robots. With the rapid development of robotics technology, path planning and optimization have become fundamental areas of research for achieving efficient and safe autonomous robot navigation. In this paper, we review the classic and state-of-the-art techniques of path planning and optimization, including artificial potential fields, A* algorithm, Dijkstra's algorithm, genetic algorithm, swarm intelligence, and machine learning-based methods. We analyze the strengths and weaknesses of each approach and discuss their application scenarios. Moreover, we identify the challenges and open problems in this field, such as dealing with dynamic environments and real-time constraints. This paper serves as a comprehensive reference for researchers and practitioners in the robotics community, providing insights into the latest trends and developments in path planning and optimization for mobile robots.

Path planning of mobile robot in complex environment based on improved Q-learning algorithm

Path planning of mobile robot in complex environment based on improved Q-learning algorithm

Global Path Planning of Mobile Robot Based on Adaptive Sampling Area RRT

Global Path Planning of Mobile Robot Based on Adaptive Sampling Area RRT

Path planning for mobile robots in complex environments based on improved ant colony algorithm.

Aiming at the problems of the basic ant colony algorithm in path planning, such as long convergence time, poor global path quality and not being suitable for dynamic environments and unknown environments, this paper proposes a path planning method for mobile robots in complex environments based on an improved ant colony (CBIACO) algorithm. First, a new probability transfer function is designed for an ant colony algorithm, the weights of each component in the function are adaptively adjusted to optimize the convergence speed of the algorithm, and the global path is re-optimized by using the detection and optimization mechanism of diagonal obstacles. Second, a new unknown environment path exploration strategy (UPES) is designed to solve the problem of poor path exploration ability of the ant colony algorithm in unknown environment. Finally, a collision classification model is proposed for a dynamic environment, and the corresponding dynamic obstacle avoidance strategy is given. The experimental results show that CBIACO algorithm can not only rapidly generate high-quality global paths in known environments but also enable mobile robots to reach the specified target points safely and quickly in a variety of unknown environments. The new dynamic obstacle avoidance strategy enables the mobile robot to avoid dynamic obstacles in different directions at a lower cost.

Optimal Path Planning for Mobile Robots in Complex Environments Based on the Grey Wolf Algorithm and Self-powered Sensors

The term "mobile robot" or "intelligent mobile robot" refers to an autonomous system with specific sensory skills that can move independently in an environment that contains obstacles and can perform certain activities, and belongs to the category of intelligent robots. The use of mobile robots combines expertise from many different academic fields, including strongly integrated techniques in biology, anthropology, computer science, control theory, power transmission, measurement, and sensing. At present, there are already cases of using mobile robots to perform various simple tasks, but due to the low efficiency of optimal path planning, the scope of application cannot be further expanded. Therefore, based on the improved gray wolf algorithm and self-powered sensor technology, this paper optimized the optimal path planning method and energy driving method of the mobile robot, so that the mobile robot can be applied in complex environments. The experiment compared the improved path planning algorithm in this paper with the traditional path planning algorithm. The results show that the optimal path planning using the improved gray wolf algorithm can search the random tree algorithm, the improved bee colony algorithm and the multi-objective based on edge distribution estimation faster. Compared with the optimization algorithm, the optimization effect was increased by 14.84%. It is beneficial to the optimal path planning of mobile robots in complex environments.