Mobile Robot Path Research Articles

Bidirectional artificial potential field-based ant colony optimization for robot path planning

Ant colony optimization (ACO) is a common approach for addressing mobile robot path planning problems. However, it still encounters some challenges including slow convergence speed, susceptibility to local optima, and a tendency to falling into traps. We propose a bidirectional artificial potential field-based ant colony optimization (BAPFACO) algorithm to solve these issues. First, the bidirectional artificial potential field is introduced to initialize the grid environment model and restrict direction selection to jump out of the trap. Second, an adaptive heuristic function is presented to strengthen directionality of the algorithm and reduce the turning times. Third, a pseudo-random state transition rule based on potential difference between starting and ending nodes is developed to accelerate convergence speed. Finally, an improved pheromone update strategy incorporating pheromone diffusion mechanism and elite ants update strategy is proposed to help getting out of local optima. To demonstrate the advantages of BAPFACO, the validation of the performance in six different complexity environments and comparative experiments with other conventional search algorithms and ACO variants are conducted. The results of experiment show that compared to various ACO variants, BAPFACO have advantages in terms of reducing the turning times, shortening path length, improving convergence speed and avoiding ant loss. In complex environments, compared to IHMACO, the average path length enhancement percentage (PLE) of BAPFACO is 20.98%, the average iterations enhancement percentage (IE) of BAPFACO is 20.00% and the average turning times enhancement percentage (TE) of BAPFACO is 49.43%. These results firmly demonstrate the efficiency and practicality of the BAPFACO algorithm for mobile robot in path planning.

Path planning of mobile robot based on improved PRM and APF

In the field of mobile robot path planning, the artificial potential field (APF) method has been widely researched and applied due to its intuitiveness and efficiency. However, the APF algorithm often encounters challenges such as local minima and unreachable goals in complex environments. To address these issues, this paper proposes innovative path planning algorithm that integrates the advantages of the probabilistic roadmaps method (PRM), by introducing Sobol sampling and elliptical constraints to enhance PRM. The improved PRM not only reduces redundant nodes but also enhances the quality of sampling points. Furthermore, this paper uses the path nodes from the improved PRM as virtual target points for the APF algorithm, and effectively solves the inherent flaws of the APF algorithm through the segmented processing of the attractive force function and the introduction of a relative distance factor in the repulsive force function. Simulation results show that the algorithm reduces planning time, node count, and path length, demonstrate significant improvements in efficiency and performance. In addition, experiments with omnidirectional mobile robots further confirm the effectiveness and reliability of the algorithm in practical applications.

Improved ant colony algorithm for path planning based on pheromone difference distribution strategy

In view of the problems of blind search in the initial stage, slow convergence speed and easy to fall into local optimum when the traditional ant colony algorithm is used for mobile robot path planning, an improved ant colony algorithm is proposed. Firstly, according to the distance between each node and the line connecting the starting point and the target point, the initial pheromone is unevenly distributed to make it normally distributed, which reduces the blindness of the algorithm search in the initial stage and speeds up the search for the optimal solution; secondly, the volatility factor is improved, and the principle of double volatility factor is adopted to control the volatility of pheromone, which not only reduces the possibility of local optimum but also speeds up the convergence speed; the redundant path is further optimized to make the path better. Simulation results show that the improved ant colony algorithm in this paper converges faster and more stably than the traditional ant colony algorithm and other improved ant colony algorithms.

Hybrid Mobile Robot Path Planning Using Safe JBS-A*B Algorithm and Improved DWA Based on Monocular Camera

This paper addresses the formidable challenge of enabling autonomous navigation in Mobile Robots (MRs), focusing on the development of advanced path planning strategies. Despite their pivotal role in diverse applications, they face challenges in dynamic settings due to limitation in existing Global Path Planning (GPP) and Local Path Planning (LPP) techniques. In response to this, we propose an innovative hybrid path planning approach that enhances the A* algorithm with a risk-aware heuristic function and integrates the Jump Point Search (JPS) technique for route optimization. Additionally, B-spline smoothing is employed for perceptually global trajectory refinement. Our approach also includes an innovative improvement to the Dynamic Window Approach (DWA) to align with the proposed enhanced A* algorithm for effective local navigation. Acknowledging the importance of high-quality input in path planning, we present substantial improvements to the IRDC-Net, a monocular-image semantic-segmentation model that we studied previously. Novel improvements include the integration of quantization and the Adam optimizer, along with the implementation of the Balanced Cross-Entropy loss function. These enhancements not only elevate the output quality of IRDC-Net but also reduce the model’s training parameters. The experimental results demonstrate the performance and viability of the proposed algorithm. Ultimately, the hybrid MR’s path planning algorithm exhibits proficiency across various tasks, particularly in addressing the challenge of evading moving obstacles to ensure the robot’s safety while adhering to the global path.

Mobile Robot Motion Behavior Based on Hopfield Neural Network Dynamics Under Electromagnetic Radiation

The nonlinear characteristic of activation function is a critical aspect that affects the performance of artificial neural networks (ANNs). Despite its importance, the impact of neuronal nonlinearity variations on the dynamics of ANNs has not been thoroughly studied in previous research. This paper aims to fill this gap by exploring the influence of nonlinearity changes in activation function due to memristive electromagnetic induction on the dynamical characteristics of ANNs. We first propose a memristor model with sinusoidal conductance function. Then, the effects on the biological neuron under the electromagnetic induction are simulated by replacing the gradient of hyperbolic tangent activation function with the conductance of the proposed memristor. Furthermore, a Hopfield neural network (HNN) is constructed by taking the nonlinearity changes of activation function into consideration and its dynamical behaviors are analyzed through Lyapunov exponent spectra, bifurcation diagram, biparameter-based dynamic maps and phase portraits. The numerical simulation results show complex dynamical behaviors such as single-scroll chaotic spiral attractor, double-scroll chaotic attractors and initial-value-dependent offset boosted attractors, demonstrating that nonlinearity changes of activation function induced by the electromagnetic induction have a significant impact on the dynamics of HNN. To further verify our findings, we implement the proposed HNN in hardware and find that the results are in line with our numerical simulations. Finally, we apply the chaotic HNN in the mobile robot path planning task, which achieves higher coverage rate than the random number-based random walk method.

Path planning algorithm design using particle swarms optimization and artificial potential fields

AbstractOne of the most important challenges in an autonomous and robotics system is the path planning in which the system finds the optimal path from start point to goal point. The traditional path planning algorithms may have large memory requirements which scale with the size and resolution of the configuration space. To address these challenges, this paper introduces a novel path planning algorithm that combines Particle Swarm Optimization and Artificial Potential Field in the form of a path planning algorithm for mobile robots. The biological and physical concepts from Particle Swarm Optimization and Artificial Potential Field algorithms are combined to yield an algorithm which minimizes instances of getting stuck in local minima and generates a smooth but feasible path. The developed method requires memory which scales only with the number of particles and the time taken to reach the goal. This results in a memory‐efficient solution that generates smooth and feasible paths for mobile robots navigating in a 2D space.

Improved ant colony optimization algorithm based on islands type for mobile robot path planning

According to the characteristics of ant colony optimization (ACO) algorithm in mobile robot path planning, such as local optimal solution, slow convergence speed, low search efficiency, and propensity to produce numerous deadlock ants, an improved ACO algorithm based on island type (insular ACO (INACO)) is introduced. In this algorithm, firstly, several islands are established between the starting and the ending position of environment, serving as intermediate nodes for the paths searched by the ants. This greatly reduces the number of deadlock ants. Additionally, rectangular areas are initialized with non-uniform pheromone levels between adjacent islands, while other areas are set to a constant minimum pheromone value. This prevents blind searches during the initial stages of path planning. Furthermore, an adaptive volatilization coefficient is introduced into the global pheromone update rules to balance the convergence and global search ability. Finally, optimal parameter combinations of INACO are determined by simulation. INACO algorithm is simulated in various grid maps and compared with other improved ACO algorithms. Results demonstrate its superior global optimal search capability and rapid convergence speed. The average iterative times is 2.7 in a 20 × 20 grid environment and 4.7 in a 30 × 30 grid environment. Notably, INACO produces very few lost ants, with mean values of 50.1 and 95.2 in 20 × 20 and 30 × 30 grid environments, respectively.

Optimization of the mobile robot's path searching based on the A-star algorithm

Notable has been the rapid development of artificial intelligence in recent years, with mobile robots being widely applied in various fields. Greatly facilitating people's daily lives, research on robots has received widespread attention. The obstacle avoidance capability of mobile robots is an important indicator of their intelligence. This article utilizes the A-star algorithm. It optimizes the robot's movement path. The A-star algorithm is characterized by balancing the actual cost from the starting point to the current node with the estimated cost from the endpoint to the current node. This algorithm incorporates elements of both Dijkstra's algorithm and greedy search. By being guided by a heuristic function, A-star efficiently approaches the target point, ensuring optimal path planning. This paper focuses on optimizing the A-star algorithm, enhancing both node expansion during path exploration and search time for paths as the robot navigates through a maze.

Mobile robot path planning based on multi-experience pool deep deterministic policy gradient in unknown environment

Mobile robot path planning based on multi-experience pool deep deterministic policy gradient in unknown environment

A multi-strategy improved sparrow search algorithm for mobile robots path planning

Abstract Path planning for mobile robots plays a vital role in task execution, given the constraints imposed by environments and energy resources. It poses a significant challenge for mobile robots, requiring them to find a feasible path between the start point and target point that is obstacle-free and as short as possible. To address the challenge of path planning, a multi-strategy improved sparrow search algorithm with crazy operator (CMISSA) is proposed. Firstly, Tent chaos mapping and reverse learning are introduced into the population initialization of sparrow search algorithm (SSA) to enhance the uniformity and effectiveness of the initial population distribution. Secondly, adaptive parameters are applied in SSA to maintain a balance between exploitation and exploration. Thirdly, to prevent SSA from getting trapped in local optima, the crazy operator is used to perturb the population position. Finally, a novel adaptive boundary control strategy is introduced to handle the location of individuals that have crossed the boundary. In addition, the experimental results on 15 classical benchmark functions show that CMISSA has better optimization performance than other 10 comparison algorithms. Furthermore, in the path planning experimental results, the results of comparing CMISSA with 5 comparison algorithms on 5 different environments reveal CMISSA's average path shortening rates were 34.90%, 20.11%, 29.01%, 51.97%, 37.42%, respectively. It is further demonstrated that CMISSA has superior availability for solving mobile robots path planning.

A-star algorithm vs. Q-learning algorithm for path planning: A comparative analysis

In robotics research, the core focus is on path planning, as it's essential for robots to navigate according to human objectives to be valuable. This paper provides an in-depth analysis of the commonly used A-Star algorithm and Q-learning algorithm in mobile robot path planning. It specifically examines the advantages and disadvantages of both the A-Star algorithm and the Q-learning algorithm, highlighting their roles in the advancement of robotic intelligence. The A-star algorithm is an algorithm with a heuristic function to guide path selection direction. Also, the A-star algorithm is guaranteed to identify the most efficient path in terms of traffic cost when facing static obstacles. However, its performance degrades when dealing with trap nodes. Specifically, the algorithm occupies a large amount of memory and takes a long computational time. In contrast, the Q-learning algorithm is effective for learning continuous actions with the same network data structure for local path planning tasks.

Symmetric Pseudo-Multi-Scroll Attractor and Its Application in Mobile Robot Path Planning

The symmetric multi-scroll strange attractor has shown great potential in chaos-based applications due to its high complexity in phase space. Here, the approach of symmetrization is employed for attractor doubling to generate pseudo-multi-scroll attractors in a discrete map, where a carefully selected offset constant is the key to organizing coexisting attractors. By choosing the Hénon map to generate the pseudo-multi-scroll attractor and implementing the digital circuit on a microcontroller, this study fills a significant gap in the research on discrete chaotic systems. The complexity performance is further validated using a pseudo-random number generator, demonstrating substantial academic contributions to the field of chaos theory. Additionally, a pseudo-multi-scroll attractor-based squirrel search algorithm is first developed, showcasing its practical application in mobile robot path planning. This work not only advances the theoretical understanding of chaotic systems but also provides practical methods for implementation in digital systems, offering valuable insights for policy-making in advanced robotic systems and intelligent manufacturing.

Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm

The paper proposes an innovative adaptation of the estimation of distribution algorithm (EDA), intended for multi-objective optimisation of a ship’s route in a non-stationary environment (tidal waters). The key elements of the proposed approach—the adaptive Markov chain-based path generator and the dynamic programming-based local search algorithm—are presented in detail. The experimental results presented indicate the high effectiveness of the proposed algorithm in finding very good quality approximations of optimal solutions in the Pareto sense. Critical for this was the proposed local search algorithm, whose application improved the final result significantly (the Pareto set size increased from five up to nine times, and the Pareto front quality just about doubled). The proposed algorithm can also be applied to other domains (e.g., mobile robot path planning). It can be considered a framework for (simulation-based) multi-objective optimal path planning in non-stationary environments.

Research on robot path planning with safe distance optimization

PurposeWith the development of technology, the application scenarios of mobile robots are becoming more and more extensive, accompanied by a variety of application scenarios suitable and safe path planning algorithms are indispensable for mobile robots.Design/methodology/approachThe purpose of this paper to improve the safety performance of your bot during the execution of tasks. The methods are synthesized in three main areas: setting appropriate safety distances based on the actual radius of the robot, turn penalty reduces the number of turns by applying an additional penalty to the number of turns in a heuristic function and path smoothing is used to improve path reliability by reducing the number of right-angle turns.FindingsA suitable safety distance greatly improves the safety of mobile robots and facilitates their development. Optimization of turns in the path of mobile robots improves the travel efficiency of robots. Enhancing the safety of mobile robots has become a research hotspot for path-planning algorithms.Originality/valueThis paper proposes a path planning scheme for mobile robots with safe distances, which provides readers with a comprehensive and systematic progress of path planning research. It helps readers to get inspiration from enhancing the safety of mobile robots.

Research on Autonomous Mobile Robot Path Planning Based on M-RRT Algorithm

The Rapidly-Exploring Random Tree (RRT) algorithm has demonstrated proficiency in adapting to path search challenges within high-dimensional dynamic environments. However, a notable limitation of the RRT algorithm lies in its inability to fulfill the criteria for achieving the shortest and smoothest path for mobile sensing nodes. To address the limitations of the conventional RRT algorithm and enhance the path planning for mobile robots, this paper proposed an innovative approach named M-RRT, designed to overcome the aforementioned shortcomings and optimize the path planning process for mobile sensing nodes. First, the search area is constructed according to the defined coverage density. After searching the path in the search area, the RRT algorithm uses the greedy method to delete the intermediate nodes in the path, and obtains the uniquly optimal path. Finally, the Bezier curve is used to optimize the path, which makes the path shortest and meets the dynamic requirements of the mobile node. Simulation results show that M-RRT has better path and faster convergence speed than traditional RRT, which can better meet the planning requirements of mobile nodes.

Simulation of Dynamic Path Planning of Symmetrical Trajectory of Mobile Robots Based on Improved A* and Artificial Potential Field Fusion for Natural Resource Exploration

With the rapid development of new-generation artificial intelligence and Internet of Things technology, mobile robot technology has been widely used in various fields. Among them, the autonomous path-planning technology of mobile robots is one of the cores for realizing their autonomous driving and obstacle avoidance. This study conducts an in-depth discussion on the real-time and dynamic obstacle avoidance capabilities of mobile robot path planning. First, we proposed a preprocessing method for obstacles in the grid map, focusing on the closed processing of the internal space of concave obstacles to ensure the feasibility of the path while effectively reducing the number of grid nodes searched by the A* algorithm, thereby improving path search efficiency. Secondly, in order to achieve static global path planning, this study adopts the A algorithm. However, in practice, algorithm A has problems such as a large number of node traversals, low search efficiency, redundant path nodes, and uneven turning angles. To solve these problems, we optimized the A* algorithm, focusing on optimizing the heuristic function and weight coefficient to reduce the number of node traversals and improve search efficiency. In addition, we use the Bezier curve method to smooth the path and remove redundant nodes, thereby reducing the turning angle. Then, in order to achieve dynamic local path planning, this study adopts the artificial potential field method. However, the artificial potential field method has the problems of unreachable target points and local minima. In order to solve these problems, we optimized the repulsion field so that the target point is at the lowest point of the global energy of the gravitational field and the repulsive field and eliminated the local optimal point. Finally, for the path-planning problem of mobile robots in dynamic environments, this study proposes a hybrid path-planning method based on a combination of the improved A* algorithm and the artificial potential field method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. By introducing symmetry, we can more intuitively judge whether the path is close to the optimal state. Symmetry is an important criterion for us to evaluate the performance of the final path.

Multi-strategy Improved Pelican Optimization Algorithm for Mobile Robot Path Planning

In response to the problems of easily falling into local optima, low path planning accuracy, and slow convergence speed when applying the traditional pelican optimization algorithm to the mobile robot path planning problem, a multi-strategy improved pelican optimization algorithm (MPOA) is proposed. In the initialization stage, chaotic mapping is used to increase the diversity of the pelican population individuals. In the exploration stage, an adaptive feedback adjustment factor is proposed to adjust the local optima of pelican individuals’ positions and balance the algorithm’s local development capability. In the development stage, the Lévy flight strategy is introduced to adjust the domain radius of the pelican population individuals, and the Gaussian mutation mechanism is used to disturb individuals that have fallen into local optima. Simulation experimental results show that the improved algorithm has significantly improved and effectively shortened the length of the planned path.

Cooperative path planning study of distributed multi-mobile robots based on optimised ACO algorithm

The rapid development of robotics technology has driven the growth of robot types and the development of related technologies. As an important aspect of robot research, path planning technology plays an irreplaceable role in practical production and application. Ant colony algorithm has a wide range of applications in robot path planning, but there is also a problem of performance overly relying on initial parameter selection. In order to solve this problem and improve the performance of mobile robot path planning, an improved ant colony algorithm based on firefly algorithm was studied and designed in a two-dimensional environment. In order to further explore the performance of ant colony algorithm in solving robot coordinated path planning problems, an improved ant colony algorithm based on heuristic function was also designed. In a three-dimensional environment, an improved ant colony algorithm based on the improved artificial potential field method was designed. The research results show that the maximum running time of the improved ant colony algorithm based on the firefly algorithm in different grid environments is 819.36 s, 847.01 s, and 811.54 s, respectively. The average running time of the improved ant colony algorithm based on heuristic function in different grid environments is 5.19 s, 5.97 s, and 9.09 s, with average path lengths of 29.90 cm, 31.08 cm, and 37.01 cm, and path length variances of 0.35, 0.87, and 2.21, respectively. The ant colony algorithm based on the improved artificial potential field method has a running time of 1.930 s, 3.182 s, and 4.662 s in different grid environments, and a path length of 29.275 cm, 49.447 cm, and 67.057 cm, respectively. The ant colony algorithm for research and design optimization has good performance. The contribution of the research lies in the design of three path planning methods for mobile robots, including two-dimensional path planning and three-dimensional path planning, which improves the time of path planning and shortens the average path length. The novelty of the research is reflected in the design of a path planning method for mobile robots in two-dimensional and three-dimensional environments, which improves the ant colony algorithm through firefly algorithm and heuristic function, and combines the ant colony algorithm with the improved artificial potential field method. The method designed by the research institute can provide technical support for path planning of mobile robots.

MOBILE ROBOT TRACKING SYSTEM BASED ON MACHINE VISION AND LASER RADAR

The proposed solution addresses the issue of insufficient real-time performance and accuracy in mobile robot path tracking by introducing a system that combines machine vision and laser radar. In this study, the Broadcom BCM2711 microcontroller chip is connected to the RS232 communication interface for transmitting information to the ARM embedded processor. Users can access position distance, direction, and other robot-related data through the man-machine interface's LCD display in a Windows operating system environment. By initiating an adaptive position tracking algorithm program identified by the robot within the position tracking unit, mobile position tracking of the robot is achieved. Experimental results demonstrate significant improvements in both real-time performance and accuracy of this mobile robot tracking system.

Entertainment robots based on swarm intelligence algorithm applied in remote dance performances

With the continuous development of technology, the application of entertainment robots in the field of dance performances is receiving increasing attention. This article focuses on the application of entertainment robots based on swarm intelligence algorithms in remote dance performances, aiming to design an intelligent dance performance control system to achieve precise and smooth dance performances of robots. The study introduces the basic artificial fish swarm algorithm and combines it with mobile robot path planning, proposing a mobile robot path planning method based on swarm intelligence algorithm. By simulating the behavior of fish in schools, the optimization of path planning is achieved. The control system process of the entertainment robot intelligent dance performance control system includes preparation work before the dance performance, data transmission and dance posture control during the performance process, and post performance organization work. The experiment verified the effectiveness and reliability of the intelligent dance performance control system for entertainment robots. The experimental results show that the system can achieve precise, smooth, and diverse dance performances, providing a novel and interesting form of dance performance for the audience.