Path Planning Methods of Mobile Robot Based on Soft Computing Technique

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.

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.

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.

<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>

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.

This paper proposes a novel Genetic Algorithms (GAs) approach for a near-optimal path planning of a mobile robot in a greenhouse. The chromosome encoding features in inverse proportion between research spaces of GAs and complexity of obstacles. The fitness evaluation is designed for both incomplete and complete paths to guide the evolutional direction. The crossover and mutation operators are trimmed to he path planning problem. Two operators were presented to promote the effectiveness of evolution of problem-specific GAs. The simulation results obtained is satisfactory.Key wordsGenetic Algorithmsmobile robotpath planning

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.

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.

This research aims to create a simulator for solving the global path planning of mobile robots. Various sampling-based methods such as Rapidly-exploring Random Tree (RRT), RRT*, and Fast-RRT, along with other derivative algorithms, have been widely used to solve path-planning problems in mobile robots. The level of computational efficiency, path optimality, and the ability to adapt to variant environments are some of the issues that still arise, although these techniques have shown good results in many cases. Although the existing solutions are innovative, comparison between the existing methods is still difficult due to significant differences in convergence speed, implementation complexity, and quality of the resulting paths. This makes choosing the most suitable method for a particular application difficult. The simulator uses sampling-based path planning algorithms such as RRT*, Fast RRT*, RRT*-Smart, informed-RRT*, and Honey Bee Mating Optimization-based Fast-RRT*. With this simulator, users can easily compare the performance of each algorithm and see the characteristics and efficiency of each algorithm in various situations. By running all methods through this simulator, the user can easily compare the methods based on convergence speed and optimality. Therefore, it will effectively help users understand robot navigation, improve the quality of learning, and promote the development of path-planning technology for mobile robots.

This paper proposes an algorithm for trajectory planning based on the motion of Brownian particles. One of the most popular approaches in path planning is to use the artificial potential fields method which, due to its easiness in implementation, might attract the robot towards a local minimum configuration, thus preventing it from reaching the desired final destination. Although there are different approaches to deal with this drawback, their modeling lacks the simplicity of the potential fields, adding thus an extra complexity to the problem. The solution proposed here combines the strengths of both approaches: it is easy to analyze and to implement, just like in the potentials method, while it preserves the robustness against local minima of more complex particle swarm models. An approximate analysis for the deterministic version of the selected model was performed and it was observed, via simulations, that the results obtained after this simplification were consistent with the behavior of the stochastic system.

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.

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.

A Machine Learning approach for collision avoidance and path planning of mobile robot under dense and cluttered environments

An improved TD3 algorithm is studied for the low success rate and slow learning speed of TD3(Twin Delayed Deep Deterministic Policy Gradients) algorithm in mobile robot path planning. Prioritized experience replay is added and dynamic delay update strategy is designed which can reduce the impact of value estimation errors to improve the success rate of path planning, and reduce training time. Based on the ROS melodic operating system and the Gazebo simulation software, the Turtlebot3 robot model and simulation experimental environment are established. The effectiveness of the improved TD3 algorithm is verified by comparative experiments. For the path planning task of the mobile robot, the simulation results show that compared with TD3 algorithm, the success rate of the improved TD3 algorithm is increased by 15.6%, and the training navigation time is shortened by 20%. It indicates that the improved TD3 algorithm achieves better performance in the path planning of mobile robot with continuous action space.

Based on the artificial potential field algorithm and bacterial foraging algorithm, research method for obstacle avoidance and path planning of the mobile robot. Firstly, analysis method of bacterial foraging three operations: trends, replication, migration, establishment of bacterial foraging algorithm process; secondly establish the bacterial foraging algorithm model; and then analyze the artificial potential field algorithm and bacterial foraging the advantage and disadvantage of the algorithm, proposed a new hybrid algorithm has the global search ability and fast convergence. Using artificial potential field and bacterial foraging method on mobile robot global path planning and then compared with the mixture algorithm, based on the simulation results, the global path planning of mobile robot Simulation results show the feasibility and effectiveness of the hybrid algorithm.