Mobile Robot Path Planning using Multi-Objective Adaptive Ant Colony Optimization

To determine the optimal trajectory of mobile robots in a complex environment and to enhance the effectiveness of search efficiency and path optimization, an improved ant colony (ACO) algorithm is proposed. A fusion algorithm of improved particle swarm optimization (PSO) and ACO algorithm is proposed to solve for optimal paths. Firstly, we improve the PSO and adaptively adjust the inertia weight to facilitate the particle search. Besides, we disturb the particles with the chaotic variables to increase the convergence speed. Secondly, the pheromone distribution on the path is adjusted based on the optimal solution of PSO to solve the initial pheromone insufficiency issue in the ACO algorithm. Then, to optimize the update strategy, we adaptively adjust the pheromone intensity values. We also provide a wide range of control schemes for updating the parameters for balancing the search capability and convergence. The results in the simulation environment show that our algorithm is more effective than other improved algorithms. The improved fusion algorithm converges faster and finds shorter optimal path. The method can improve the comprehensive performance and achieve the rapid path planning for mobile robots.

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning.

The path of the mobile robot is a multi-objective optimization problem. The traditional ant colony algorithm is introduced into the path planning of the mobile robot. Due to the complexity of the robot path, the large search space and the low efficiency, it is easy to fall into the local optimum and even deadlock and other defeats. In order to enhance the efficiency and accuracy of path planning, a path planning algorithm of mobile robot based on ant colony algorithm is proposed. The path planning of mobile robot is simulated under grid environment. The experimental results show that the improved algorithm can improve the efficiency of path planning for mobile robot, safely avoid obstacles and find all the optimal solutions in the shortest time.

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.

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a “high-intensity training” mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem.

In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels.

Path planning aims to enable autonomous robots to navigate safely and efficiently from a starting point to a target point in challenging and dynamic environments. Path planning in robotics is highly significant and still an ongoing subject of research. The increasing use of robots in various applications such as industrial automation, service robotics, and autonomous vehicles has brought forth the need for reliable and efficient path planning algorithms. The inherent capability of Voronoi diagrams to partition space based on proximity makes them an effective framework for research in path planning. Ant colony optimization, a bio-inspired optimization technique, is based on the foraging behavior of ants and is commonly employed to address the traveling salesman problem and various other combinatorial optimization problems. A hybrid method was adopted in this study by combining a Voronoi diagram and an ant colony algorithm. To create paths for the robot where it can stay as far away from obstacles as possible, a Voronoi diagram was utilized. Additionally, to find the shortest path from the starting point to the destination among these paths, ant colony optimization was employed. The main contribution of the study lies in the combination of the Voronoi diagram for obstacle avoidance and ant colony optimization for finding the optimal path. The combination of these techniques makes an effective contribution to robotic path planning by focusing on ensuring safety by avoiding obstacles while optimizing the shortest path. Experimental studies show that the hybrid method produces successful results for the desired purpose.

With the continuous progress and development of robotics, mobile robots have been widely used in many different fields. In the power grid, mobile robots are used to inspect electrical equipment, which greatly reduce the investment of manpower and material resources. However, in many cases, mobile robots need to work in a constantly changing and complex environment. Because they cannot obtain environmental information in time, it is often difficult to make path planning. In response to this problem, this paper proposes a path planning method for mobile robots based on improved reinforcement learning. This method establishes a grid environment model, defines the return value through the number of steps of the robot, and then proposes a changing action selection strategy for the balance between the robot’s exploration and utilization of the environment in reinforcement learning, so that the exploration factor dynamically change with the increase of the robot’s exploration degree of the environment, thus speeding up the convergence speed of the learning algorithm. Simulation results show that this method can realize autonomous navigation and path planning of mobile robots in complex environments. Compared with traditional algorithms, it greatly reduces the number of iterations.

Path planning of mobile robots is an important research content in mobile robot design. In view of the characteristics of classic ant colony algorithm, such as long distance, low efficiency and easy to fail into dead zone, a new pheromone updating algorithm and global/local pheromone distribution models are proposed. The grid method is used to construct two-dimensional plane space with obstacles and the simulation experiment is carried out. The simulation results show that under the improved algorithm, the robot path planning efficiency is high and the path distance is short, which verifies the validity of the model.

In this paper, warehouse logistics robot as the research object, starting from the reality of modern e-commerce logistics industry, proposed a warehousing logistics mobile robot path planning method. Ant colony algorithm is used to plan the forward path of mobile robot in static and dynamic environment of warehouse logistics. The results show that the elite-based strategy improves the global search capability of ACA and eliminates redundant nodes in the path, and the path length of the obtained plan is significantly better than that of traditional ACA, which is reduced by 3.46% and 5.90%, respectively, indicating that the elite-based strategy and the central-point-based smoothing method play their roles. The path length of the robot’s final operation is larger than that of the global path planning, which increases by 1, accounting for 6.67% of the original path length. Therefore, the storage and logistics mobile robot based on ACA-E algorithm has short driving distance and superior obstacle avoidance ability.

In the current research field of mobile robots, optimal path planning is a hot topic. Ant colony algorithm is diffusely used in mobile robot path planning because of its its positive feedback, distributed and so on. However, traditional basic ant colony algorithm has some disadvantages, such as easy local convergence, slow search convergence rate and low efficiency. To solve these problems, this paper proposes an improved ant colony algorithm. Based on the algorithm principle, a new method for global pheromone initialization is put forward by improving and optimizing the pheromone mechanism, and there is a certain probability to carry out “variation” operation in each search iteration process. Meanwhile, the calculation method of heuristic function is improved to reduce the possibility of premature convergence of the algorithm falling into local optimum. The two-dimensional space model of mobile robot is created by grid method. Then they are simulated and tested respectively, and the test results are compared and analyzed. The experimental results show that the improved algorithm can obviously optimize the defects of the original algorithm, which not only can plan the path faster, but also can plan the better path, and the search efficiency has been significantly improved.

Mobile robots are widely used in daily life, and ant colony algorithm is often adopted for its path planning. However, the classic ant colony algorithm has poor search ability and search speed due to the monotony of individuals within the population. In order to solve the problems of local optimal solution, slow convergence speed and low search efficiency of classic ant colony algorithm, a genetic-ant colony algorithm is proposed in this paper. The algorithm parameters are optimized by simulation experiment to determine the best parameter combination. The method is to give a certain range of random pheromone values to the primary ant individuals to enrich the population diversity. In each cycle, m ants are sent for foraging. After each foraging cycle, aiming at the growth of individual pheromone value, the fitness function of genetic algorithm is established to evaluate individual fitness value, and the parental individuals carry out genetic operations such as selection, crossover and variation to produce offspring individuals with the same number and higher pheromone level, the offspring individuals carry out the next cycle of foraging and keep cycling. The population diversity is maintained at a high level, while the increase in the level of individual pheromone speeds up the convergence and operation of the algorithm. The simulation results show that the running time of the improved algorithm is significantly shorter than that of the traditional ant colony algorithm in the 40 * 40 grid map, and the running time of the algorithm is reduced by 21.34%; in the complex grid environment, the frequency of obtaining the optimal path of the algorithm is significantly higher than that of ordinary ant colonies, and the frequency is increased; and the number of iterations when the optimal path is reached for the first time is significantly reduced,–which has verified the effectiveness, stability and superiority of genetic-ant colony algorithm.

This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments. As a computational approach to learning through interaction with the environment, reinforcement learning algorithms have been widely used for intelligent robot control, especially in the field of autonomous mobile robots. However, the learning process is slow and cumbersome. For practical applications, rapid rates of convergence are required. Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning, a state-chain sequential feedback Q-learning algorithm is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments. The state chain is built during the searching process. After one action is chosen and the reward is received, the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning. With the increasing number of Q-values updated after one action, the number of actual steps for convergence decreases and thus, the learning time decreases, where a step is a state transition. Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments. The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time, compared with the one-step Q-learning algorithm and the Q(λ)-learning algorithm.

Dynamic Window Approach (DWA) is widely used in the local path planning of mobile robot. DWA algorithm has the advantages of simple planning algorithm and good realtime performance. However, the DWA exists unreachable target point, unreasonable path planning in the dense obstacles and it cannot ensure both speed and security. Therefore, an online parameter self-adaptive DWA algorithm is proposed to automatically adjust the weight of the objective function on the basis of the size of alternative velocity space and the distance between the mobile robot to the target point. By adapting to the dynamic changes of the environment, the optimal running speed and reasonable path of mobile robot can be obtained. The simulation verified that the mobile robot can pass through the dense obstacles area and reach the target point safely and reliably. And the problems that the robot might move around the dense obstacles and result in an unsmooth path are avoided. and the operational efficiency of the mobile robot is ensured simultaneously.

Purpose The purpose of this paper is to consider the smooth path planning problem for a mobile robot based on the genetic algorithm (GA) and the Bezier curve. Design/methodology/approach The workspace of a mobile robot is described by a new grid-based representation that facilitates the operations of the adopted GA. The chromosome of the GA is composed of a sequence of binary numbered grids (i.e. control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points. Findings This paper has proposed a new smooth path planning for a mobile robot by resorting to the GA and the Bezier curve. A new grid-based representation of the workspace has been presented, which makes it convenient to perform operations in the GA. The GA has been used to search the optimum control points that determine the Bezier curve-based smooth path. The effectiveness of the proposed approach has been verified by a numerical experiment, and some performances of the obtained method have also been analyzed. Research limitations/implications There still remain many interesting topics, for example, how to solve the specific smooth path planning problem by using the GA and how to promote the computational efficiency in the more grids case. These issues deserve further research. Originality/value The purpose of this paper is to improve the existing results by making the following three distinctive contributions: a rigorous mathematical formulation of the path planning optimization problem is formulated; a general grid-based representation (2n × 2n) is proposed to describe the workspace of the mobile robots to facilitate the implementation of the GA where n is chosen according to the trade-off between the accuracy and the computational burden; and the control points of the Bezier curve are directly linked to the optimization criteria so that the generated paths are guaranteed to be optimal without any need for smoothing afterwards.