A Comparative Study Between Hopfield Neural Network and A* Path Planning Algorithms for Mobile Robot

PurposeThe purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).Design/methodology/approachBased on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.FindingsThe assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.Originality/valueThe work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

A mobile robot's main objective is to reach its intended destination by traversing an optimal path determined by factors such as time, distance, and safety from potential impediments. Therefore, smooth pathfinding and obstacle avoidance are core functionalities of autonomous mobile robots (AMRs). Numerous academics have proposed a wide range of path planning and obstacle avoidance algorithms. However, these methods often face challenges in complex, dynamic environments requiring both local and global optimization. This work presents a novel hybrid path-finding method that integrates the strengths of the D* algorithm with a Modified Pelican Optimization Algorithm (MPOA) and quadratic parametric equations (QPE) for mobile robots. The proposed approach leverages the global optimization capabilities of MPOA and QPE to enhance path-finding efficiency, along with the adaptive and dynamic nature of the D* algorithm for real-time replanning. The comparison of simulation results to conventional approaches shows substantial progress in path-planning computation, especially adaptivity and functional scenarios. The validity and ability of the proposed method are also illustrated from the comparison of its performance with other path planning algorithms based on the optimization of the functions of cost and achieving the shortest smooth path length. the methods are tested in a static obstacles environment using the Python package.

A novel collaborative path planning algorithm for 3-wheel omnidirectional Autonomous Mobile Robot

In this study we present our idea for using immune-genetic algorithm with the elitism and an improved roulette wheel selection strategy (IGAE-R) to help a controllable mobile robot to find an optimal path between a starting and ending point in a static environment. Grid theory is utilized to establish the free space model of the monile robot in the environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. Some characteristics of IGAE-R include: immune-genetic with elitist reservation strategy, deletion and insertion operators during the searching process, and roulette wheel selection strategy. IGAE-R allows eight-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.

<p align="left">Path planning is an essential task for the navigation of Autonomous Mobile Robot. This is one of the basic problems in robotics. Path planning algorithms are classified as global or local, depending on the knowledge of surrounding environment. In local path planning, the environment is unknown to the robot, and sensors are used to detect the obstacles and to avoid collision. Bug algorithms are one of the frequently used path planning algorithms where a mobile robot moves to the target by detecting the nearest obstacle and avoiding it with limited information about the environment. This proposed Critical-PointBug algorithm, is a new Bug algorithm for path planning of mobile robots. This algorithm tries to minimize traversal of obstacle border by searching few important points on the boundary of obstacle area as a rotation point to goal and end with a complete path from source to goal.</p>

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

We evaluate a technique that uses an embedded network deployed pervasively throughout an environment to aid robots in navigation. The embedded nodes do not know their absolute or relative positions and the mobile robots do not perform localization or mapping. Yet, the mobile robot is able to navigate through complex environments effectively. First, we present an algorithm for physical path planning and its implementation on the Gnats, a novel embedded network platform. Next, we investigate the quality of the computed paths. We present quantitative results collected from a real-world embedded network of 60 nodes. Experimentally, we find that, on average, the path computed by the network is only 24% longer than the optimal path. Finally, we show that the paths computed by the network are useful for a simple mobile robot. Results from a network of 156 nodes in a static environment and a network of 60 nodes in a dynamic environment are presented.

This paper presents a novel ACO algorithm to solve the global path planning problem, called Heterogeneous ACO (HACO) algorithm. We proposed HACO algorithm to improve the performance of ACO for global path planning in the previous paper. The HACO algorithm differs from the Conventional ACO (CACO) algorithm for the path planning in three respects. First, we proposed modified Transition Probability Function (TPF) and Pheromone Update Rule (PUR). Second, we newly introduced the Path Crossover (PC) in the PUR. Finally, we also proposed the first introduction of the heterogeneous ants in the ACO algorithm. We apply the proposed HACO algorithm to general path planning problem and we verify the effect of the each scheme through the simulation.

Mobile robots are intended to operate in a variety of environments, and they need to be able to navigate and travel around obstacles, such as objects and barriers. In order to guarantee that the robot will not come into contact with any obstacles or other objects during its movement, algorithms for path planning have been demonstrated. The basic goal while constructing a route is to find the fastest and smoothest route between the starting point and the destination. This article describes route planning using the improvised genetic algorithm with the Bezier Curve (GA-BZ). This study carried out two main experiments, each using a 20x20 random grid map model with varying percentages of obstacles (5%, 15%, and 30% in the first experiment, and 25% and 50% in the second). In the initial experiments, the population (PN), generation (GN), and mutation rate (MR) of genetic algorithms (GA) will be altered to the following values: (PN = 100, 125, 150, or 200; GN = 100, 125, 150; and MR = 0.1, 0.3, 0.5, 0.7) respectively. The goal is to evaluate the effectiveness of AMR in terms of travel distance (m), total time (s), and total cost (RM) in comparison to traditional GA and GA-BZ. The second experiment examined robot performance utilising GA, GA-BZ, Simulated Annealing (SA), A-Star (A*), and Dijkstra's Algorithms (DA) for path distance (m), time travel (s), and fare trip (RM). The simulation results are analysed, compared, and explained. In conclusion, the project is summarised.

Robot path planning in 3D space is a challenging problem for its complex configuration. Sampling-based algorithms have gained great success in solving path planning problems in 3D space, but the quality of the initial path is not guaranteed and the convergence to the optimal solution is slow. To address these problems, in this article, we present a novel sampling-based path planning framework enhanced by the deep neural network (DNN) with applications to 3D space. In the proposed framework, we first train the DNN with a number of successful path planning cases in 3D space. Then the DNN is utilized to predict the promising region where the feasible path probably exists for a given path planning problem. This predicted promising region serves as a nonuniform sampling heuristic to bias the sampling process of the path planner. In this way, the path planner can focus on the promising region in the exploration and exploitation process so that the path planning speed gets accelerated. We conduct numerical simulations to evaluate the performance of the proposed algorithm and the results show that it can perform much better than conventional path planning algorithms. Furthermore, we also investigate the performance of different DNN architectures for path planning in 3D space. Note to Practitioners—In this work, we aim to provide an efficient learning-based method to accelerate the robot path planning process in 3D space. Conventional path planning algorithms need to perceive the environment first, and then implement a series of calculations such as collision checking and data storing to generate a feasible path. When facing complex and high-dimensional environments, they do not perform well. But the proposed neural network method in this article can predict the promising region where the feasible path exists for any given environment. This prediction result is used to guide the path planning process so that the algorithm performance can get significantly improved. Apart from sampling-based algorithms, the proposed neural network model can also be extended to other types of path planning algorithms.

We consider the problem of warehouse multi-robot automation system in discrete-time and discrete-space configuration with focus on the task allocation and conflict-free path planning. We present a system design where a centralized server handles the task allocation and each robot performs local path planning distributively. A genetic-based task allocation algorithm is firstly presented, with modification to enable heuristic learning. A semi-complete potential field based local path planning algorithm is then proposed, named the recursive excitation/relaxation artificial potential field (RERAPF). A mathematical proof is also presented to show the semi-completeness of the RERAPF algorithm. The main contribution of this paper is the modification of conventional artificial potential field (APF) to be semi-complete while computationally efficient, resolving the traditional issue of incompleteness. Simulation results are also presented for performance evaluation of the proposed path planning algorithm and the overall system.

In order to solve the problem that mobile robot is trapped in local convergence and cannot achieve path optimization in ant colony algorithm path planning, an improved variable step size ant colony algorithm is proposed to achieve path optimization with less convergence iterations. According to the relevant characteristics of the application of ant colony algorithm in path planning, the pheromone allocation is optimized, the influence of local pheromone content on the algorithm is reduced, the ant colony is prevented from falling into local optimization when searching the path, and the weight factor is added to the transfer probability formula. improve the probability of the mobile robot moving towards the end point, effectively reduce the number of convergence iterations of the ant colony, and change the mobile robot’s mobile step size. So that it can move freely without collision within 360°, and the path length can be effectively shortened. The simulation results show that in a simple environment, the convergence iteration times and optimal path length of the improved variable step size ant colony algorithm are 2 and 28.042 m, respectively, and the convergence iteration times and optimal path length of the traditional ant colony algorithm are 25 and 29.213 m, respectively. In the complex environment, the convergence iteration times and optimal path length of the improved variable step size ant colony algorithm are 2 and 43.9602m respectively, and the convergence iteration times and optimal path length of the improved potential field ant colony algorithm are 16 and 45.1127m, respectively. Simulation results verify the effectiveness and superiority of the improved variable step size ant colony algorithm.

Electromagnetically actuated microswimmers have been widely used in various biomedical applications due to their minor invasive traits and their easy access to confined environments. In order to guide the microswimmers autonomously towards a target, an obstacle-free path must be computed using path planning algorithms, meanwhile a motion controller must be formulated. However, automatic manipulations of magnetically actuated microswimmers are underdeveloped and still are challenging topics. In this paper, we develop an automatic manipulation system for magnetically actuated helical microswimmers in static environments, which mainly consists of a mapper, a path planner, and a motion controller. First, the mapper processes the captured image by morphological transformations and then labels the free space and the obstacle space. Second, the path planner explores the obstacle-free space to find a feasible path from the start to the goal by a global planning algorithm. Last, the motion controller guides the helical microswimmers along the desired path by a closed-loop algorithm. Experiments are conducted to verify the effectiveness of the proposed automatic manipulation. Furthermore, our proposed approach presents the first step towards applications of microswimmers for targeted medical treatments, such as micromanipulation, targeted therapy, and targeted drug delivery.

Mobile robots, as a new type of automated equipment, are widely utilized in intelligent manufacturing production lines. This study focuses on addressing issues such as slow convergence speed, low solution accuracy, and susceptibility to local optima in the traditional whale optimization algorithm (WOA) for mobile robot path planning in intelligent manufacturing production lines. This paper presents a multi-population WOA method that integrates orthogonal learning. This algorithm effectively avoids the impact of local optima through orthogonal learning-based information exchange, enhances convergence speed through reverse learning strategies, and improves solution accuracy via nonlinear convergence factors and adaptive weighting strategies. Simulation results demonstrate significant advantages in search performance for path planning with the improved algorithm, validating its effectiveness and superiority in enhancing intelligent manufacturing efficiency. This research offers a more efficient and accurate solution to the path planning problem, which holds great significance in enhancing the efficiency and accuracy of path planning for mobile robots.

With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research. This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out. The experimental results showed that on grid maps with scales of 20 $ \times $ 20, 30 $ \times $ 30, and 40 $ \times $ 40 compared to nonparallel ACO algorithms, the proposed PACO algorithm had less loss of solution accuracy but reduced the average total time by 50.71, 46.83 and 46.03%, respectively, demonstrating good solution performance.