Robotic Path Planning Based on a Triangular Mesh Map

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.

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.

According to the realistic duty of robot, it's necessary to find a short path to avoid obstacles and carry limited goods, and then the added constrains let the problem is more complex and more practical. The paper proposes a tabu-genetic algorithm for robot path planning. By utilizing the main frame of parallel search supplied by genetic algorithm and embed the individual serial search mode of tabu search algorithm, this method can enlarge search space. And in order to enhance optimized speed and quality, also improve the algorithm performance, fitness function and amendatory policy are designed. Besides, algorithm convergence is analyzed based on the model of Markov chains. The results of simulations show that the method proposed in this paper, which can find an optimal path quickly and improve convergence speed and solution quality greatly.

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.

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.

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

To ensure the safe production of mines, the intelligent trend of underground mining operations is gradually advancing. However, the operational environment of subterranean mining is intricate, making the conventional path-planning algorithm used by mining inspection robots frequently inadequate for real requirements. To safeguard the mining inspection robot, targeting the problem of low search efficiency and path redundancy in the path planning of the existing rapidly exploring random tree (RRT) algorithm in the narrow and complex unstructured environment, a path-planning algorithm combining improved RRT and a probabilistic road map (PRM) is proposed. Initially, the target area is efficiently searched according to the fan-shaped goal orientation strategy and the adaptive step size expansion strategy. Subsequently, the PRM algorithm and the improved RRT algorithm are combined to reduce the redundant points of the planning path. Ultimately, considering the kinematics of the vehicle, the path is optimized by the third-order Bessel curve. The experimental simulation results show that the proposed path-planning algorithm has a higher success rate, smoother path, and shorter path length than other algorithms in complex underground mining environments, which proves the effectiveness of the proposed algorithm.

In the actual path planning of the mobile robot, the algorithm planning of the robot is almost always to find the shortest path, but if the robot is in a narrow working environment with many corners, it will make the robot too close to the obstacle, and the robot’s edge will contact with the obstacle, resulting in the path planning failure. In order to solve the security problem of mobile robot path planning, we put forward a kind of inflation obstacles, refinement algorithm first determine skeleton and area of the mobile robot on the map, and then the refinement algorithm can get skeleton to improve traffic area, connected to the path of starting point and end point, define the passage area. Then by searching the first and second nodes, we can find the optimal path that accords with the safety of the robot. The new path planning algorithm can realize non-collision planning, improve the safety and stability of the robot, and verify its effectiveness and authenticity through simulation results.

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.

In view of biogeography-based optimization algorithm has the disadvantages of application limitations and slow convergence speed when in solving the problem of mobile robot path planning. This paper proposes an improved biogeography-based optimization algorithm, which is used to solve the global path planning of mobile robot in static environment. In the proposed algorithm, the navigation point model is selected as the working area model of mobile robot, and the nonlinear migration model and mutation mechanism with the elite retention mechanism are introduced to the biogeography-based optimization algorithm to improve its performance. Simulation proves the feasibility and the effectiveness of the proposed path planning algorithm.

With the development of robotics and artificial intelligence field unceasingly thorough, path planning as an important field of robot calculation has been widespread concern.This paper analyzes the current development of robot and path planning algorithm and focuses on the advantages and disadvantages of the traditional intelligent path planning as well as the path planning.The problem of mobile robot path planning is studied by using ant colony algorithm, and it also provides some solving methods.

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.

Aiming at the problem of large amount of calculation and difficulty in convergence of robot path planning in complex underwater 3D environments, the paper proposes a 3D path planning algorithm based on the sparrow search algorithm with potential field heuristic. First, establish an underwater 3D grid model, and use artificial potential field forces to generate an initial feasible path. Secondly, use the infinite folding chaotic map to expand the sparrow population and increase the diversity of the population.Then use the dynamic adjustment strategy to improve the location update formula of the sparrow search algorithm to adjust the search area range. Finally, the improved algorithm proposed is applied to solve the 3D path planning problem of underwater robots. The simulation results show that the optimization performance of the potential field sparrow search algorithm (PFSSA) proposed in this paper is better than the particle swarm optimization algorithm (PSO) and the sparrow search algorithm (SSA), and it can quickly obtain an optimal and safe feasible path, which proves the effectiveness of the method, and it has application value in the field of path planning of underwater robot.

Path planning is one of the classical problem in the study of network technology in computer science, communications and operations research, geographic information system and robotics. As cloud is very popular in recent years and many organizations are switching to the cloud. This paper proposes the modified genetic algorithm to solve the problem of Robot path planning using EC2. The main purpose of path optimization is to determine the shortest path between the source node and the destination node. The proposed approach determines the shortest path using the fitness function proposed for the Genetic algorithm to perform collision free traversing of robots from its source to destination. The over all problem is solved using cloud computing platform. Here, this paper presents the concept of EC2. To validate the proposed work path length is taken as one of the important parameter. The results are comparatively better than the other relevant work and the same is validated and presented in this paper.