The research on robot path planning with multi-constrains

Recently, genetic algorithms (GAs) have attracted great interest owing to efficiency and flexibility against complex robot path planning problems. To accelerate the convergence speed, preceding researches adapted conventional GAs by using problem-specific techniques. However, such approaches increase computational burden and algorithmic complexity, resulting in subsequent additional problems. In this paper, we used Population-Based Incremental Learning (PBIL) algorithm for robot path planning as a probabilistic evolutionary approach. In addition to PBIL, we also proposed the probabilistic model of nodes and the edge bank to generate promising paths. The experimental results demonstrate that the proposed method gave markedly better performance than its conventional counter-parts(GA,kGA,fGA) in terms of success rates and the quality of obtained paths.

Aiming at the problem of robot path planning in complex maps, an algorithm of robot path planning based on triangular grid graph is proposed. Firstly, a weighted undirected loop graph and a feasible domain of nodes are obtained by discretizing the triangular mesh map. Next, the Dijkstra search algorithm is applied to find the feasible shortest path from an initial to a final configuration. Finally, The Douglas-Peucker algorithm is applied to remove duplicate and redundant nodes in the feasible path, and the waypoint are extracted. The final path is a curve that is obtained by connecting the several extracted waypoint. The proposed algorithm is tested for various maps. Compared with probabilistic roadmap method, the experimental results show that the proposed method can overcome the shortcomings of the random sampling method. Furthermore, the experimental result of triangular mesh map method used in two labyrinth maps show that the triangular mesh map method can solve the robot path planning problem in complex map very well, and it is an excellent algorithm for robot path planning.

The authors previously (1994) showed that the potential field method combined with the navigating fuzzy logic controller (NFLC) can produce a safe and smooth paths for a robot. When the robot is trapped in undesired local minimum, the fuzzy tracking controller (FTC) can be employed to escape the trapping. Since the rules of the NFLC and the FTC is developed by expert's experiences, the learning of the rules is necessary to improve the performance. In this paper, an auto tuning technique called the cache genetic algorithm (CGA) is proposed to adjust the rules. The proposed CGA performs the fast operation of selection, crossover and mutation in a cache pool to obtain best fuzzy parameters. Computer simulations showed that the proposed fuzzy potential approach (FP) with the proposed cache genetic learning algorithm can improve the overall performance with fast tuning speed.

A new ant algorithm for robot path planning is presented according to the latest achievements of research on actual ants. In this algorithm, m scout ants collaborate with each other to search for an optimal or near-optimal path. Of m scout ants, n ants adopt nearest-neighbor search strategy and the left q=m-n ants adopt random search strategy. A section of optimal path will be found after all ants search L steps. Then m ants regard the end point of as the new starting point and search for L steps to find the next section of optimal path . Repeat this process until one of m ants reaches the goal. In this way, the path is consisted of h sections of path with step length L. Carry out the next generation search after step length L is modified, then repeat the searching course mentioned above. An optimal or near-optimal path will be found through comparison after multi-generation of searching. The simulation experiment results show that an optimal or near-optimal path can be planned even in environment with very complex obstacles, which can meet the requirements of real-time planning or navigation. The effect is quite satisfying.

This paper studies the obstacle avoidance and navigation of mobile robots in unstructured environments. Because of the shortcomings of one single sensor, the system integrates the stereo vision sensor blumblebee2 with the lidar sensor to detect the information surrounding the mobile robot’s surroundings. And then through data fusion to get a more complete, more accurate scene distribution. Then use the improved system of ant colony optimization to increase the convergence speed and precision of the algorithm in robot path planning. Finally, the simulation experiment is carried out in the environment of Matlab and Visual Studio, and the physical experiment is carried out under the ROS platform. The experimental results show the feasibility and effectiveness of the proposed method.

In view of high dimension, the difficulty of training, the problem of slow learning speed in the application of BP neural network in mobile robot path planning, an algorithm of reinforcement Q learning based on extreme learning machine (Q-ELM algorithm) is proposed in this paper. Firstly, the characteristic of reinforcement learning is combining the dynamic network with supervised learning, and the algorithm obtains the state information of the environment and the robot by the characteristic. After that, it is used to analyze the state to get the rewards and punishments of the current state by extreme learning machine. Secondly, it is used to solve the problem of slow training speed by the characteristic of less parameter settings and better generalization performance; Finally the autonomic learning performance of the learning algorithm is verified. The experimental results show that the Q-ELM learning algorithm not only improves the initiative of machine learning, but also improves the learning rate of 4 times than TD reinforcement learning methods, and verifies the stability and convergence of the algorithm.

A modified dueling DQN algorithm for robot path planning incorporating priority experience replay and artificial potential fields

A hybrid method of rough set and genetic algorithms is presented to raise the speed and accuracy of path planning of robot. Firstly, gain the decision rule by rough set theory. And then, come to a series of available paths by training the gained minimal decision rule. Finally, optimize the population of the paths above using genetic algorithms, and obtain the most excellent path. The results show that the hybrid method is good at raising the speed of path planning of robot.

By analyzing the research status of artificial bee colony algorithm and robot path planning, an improved artificial bee colony algorithm is proposed in this paper. Firstly, this paper elaborates the robot movement map environment modeling. Secondly, the robot path planning problem is transformed into a mathematical modeling problem, and the robot path planning is mathematically modeled. Thirdly, through the analysis of the robot planning of the traditional artificial bee colony algorithm, the concept of physical strength of bees was introduced. Fourthly, an improved artificial bee colony algorithm based on bee physical strength was proposed to accelerate the algorithm convergence and find the global optimal path. Finally, the artificial bee colony algorithm based on bee physical strength was simulated in the robot path planning method. By comparing the results of the two algorithms in path planning, the feasibility and effectiveness of algorithm proposed by the paper are verified.

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.

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

Aiming at the existent problem of global path planning for mobile robot, a path planning for mobile robot based on chaos genetic algorithm is proposed in this paper. Reasonable coding way and fitness function are used in the chaos genetic algorithm, and the chaos operation is added to the genetic algorithm (GA), the convergence rate of genetic algorithm is improved, and the local optimization can be avoided by using the chaos genetic algorithm. So the solution which obtained by chaos genetic algorithm can not only satisfy the path shortest but also effective avoid the collision with obstacle. The simulation result shows that the method is correct and feasible.

In order to solve the problem of premature convergence of the basic genetic algorithm when planning the robot running path, the basic genetic algorithm is improved and optimized. Different population initialization methods are used to initialize multiple populations randomly, so as to improve the diversity of populations; Improve the adaptive strategy and elite strategy of crossover and mutation operators to improve the convergence speed of the algorithm; Add the path tortuosity as the planning index in the fitness function to make the planned path smoother, and add constraints to the model to avoid obstacles; Finally, through the transformation of the coding paradigm of the above improved genetic algorithm, it can run on Flink distributed cluster to obtain faster solution speed, so as to meet the efficiency requirements of path planning in large-scale robot cluster system. The optimized algorithm is compared with the basic genetic algorithm. The simulation results show that the improved algorithm is efficient in robot path planning.

In this paper, a dynamic path planning scheme based on genetic algorithm (GA) is presented for navigation and obstacle avoidance of mobile robot under unknown environment. The real coding, fitness function and specific genetic operators are devised in the algorithm. The unique coding technique decreases the conventional computational complexity of genetic algorithm. It also speeds up the execution of searching by projecting two dimensional data to one dimensional data, which reduce the size of search space. The fitness function of genetic algorithm takes full consideration of three factors: the collision avoidance path, the shortest distance and smoothness of the path. The specific genetic operators are also selected to make the genetic algorithm more effective. The simulation experiments are made under the VC++ 6.0 environment. The simulation results verify that the genetic algorithm is high effective under various complex dynamic environments.

At present, the walking routes of service robots are mostly formulated and fixed. Therefore, there are many places that the robots cannot reach and appear inflexible. When obstacles are on the walking route of the robot, collisions may occur. Thus, an area-optional regeneration Rapidly-Exploring Random Tree (RRT) algorithm for dynamic path planning is proposed to plan the walking route of the service robot in real-time in an environment with moving obstacles in this paper. First, sub-regions that need path planning can be determined according to the target points, and each sub-region corresponds to a root node, so that a feasible path that does not touch obstacles through a narrow area can be found faster, and the speed of path planning can be improved. Secondly, in real-time path planning, it is analyzed that whether tree nodes near moving obstacles need to be removed. Besides, how to remove, add, and reconnect are also analyzed, avoiding global planning and improving the efficiency of path planning in frequently changing environments. It is verified by simulation that the efficiency of path planning can be improved after dividing the area, and the effectiveness and superiority of the algorithm are verified in a real-time changing environment.KeywordsArea-optionalRegeneration RRT algorithmObstacle avoidanceReal-time path planning