A dynamically hybrid path planning and obstacle avoidance algorithm for mobile robots based on improved A-star and dynamic windows approach

This paper presents an efficient parallel elite genetic algorithm (PEGA) for global path planning of an omnidirectional mobile robot moving in a static environment expressed by a grid-based map. This efficient PEGA, consisting of two parallel EGAs along with a migration operator, is proposed for global path planning of the mobile robots. The PEGA takes advantages of maintaining better population diversity, inhibiting premature convergence and keeping parallelism than conventional GAs do. The generated collision-free path is optimal in the sense of the shortest distance. The pipelined hardware implementation of IP (intellectual property) core library on a field-programmable gate array (FPGA) chip is employed to significantly speedup the processing time. Furthermore, a soft-core processor and a real-time operating system (RTOS) are embedded into the same chip to perform the global path planning using hardware/software co-design technique and SoPC (system-on-a-programmable-chip) concept. The merit and performance of the proposed SoPC-based PEGA are illustrated by conducting several simulations and experiments.

It is an essential issue for mobile robots to reach the target points with optimum cost which can be minimum duration or minimum fuel, depending on the problem. In this paper, it was aimed to develop a software for the optimal path planning of mobile robots in user-defined two-dimensional environments with static obstacles and to analyze the performance of some optimization algorithms for this problem using this software. The developed software is designed to create obstacles of different shapes and sizes in the work area and to find the shortest path for the robot using the selected optimization algorithm. Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Genetic Algorithm (GA) were implemented in the software. These algorithms have been tested for optimum path planning in four models with different problem sizes and different difficulty levels. When the results are evaluated, it is observed that the ABC algorithm gives better results than other algorithms in terms of the shortest distance. With this study, the use of optimization algorithms in real-time path planning of land mobile robots or unmanned aerial vehicles can be simulated.

This paper presents an algorithm for optimal path planning for mobile robots using Genetic Algorithms coupled with morphological image preprocessing of the terrain. Path Planning in a given environment, being a NP-Hard problem, is computationally demanding especially if exact or deterministic techniques are employed. This paves the way for the use of evolutionary computing techniques, such as Genetic Algorithms (GAs), for the solution of this problem. However as GAs tend to find optimal paths without considering the location and distribution of obstacles in the given map, therefore a large amount of time may be spent before even a feasible path is found. The technique conferred in this paper overcomes this shortcoming by using specialized morphological preprocessing techniques applied on the landscape representative image. The results obtained on a variety of structured, unstructured and clustered terrains indicate the efficacy of the algorithm presented and the potential for the use of this algorithm in real-time operation of mobile robots.

An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve

Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.

In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.

We proposed a novel ACO algorithm to solve the global path planning problems in the previous paper, called Heterogeneous ACO (HACO) algorithm. In this paper, we compare the performance of HACO algorithm with the modified Genetic Algorithm (GA) for global path planning. The HACO algorithm differs from the Conventional ACO (CACO) algorithm for the path planning in three respects. First, we proposed modified Transition Probability Function (TPF) and Pheromone Update Rule (PUR). Second, we newly introduced the Path Crossover (PC) in the PUR. Finally, we also proposed the first introduction of the heterogeneous ants in the ACO algorithm. We apply the proposed HACO algorithm and modified GA to the general global path planning problems and compare the performance of these through the computer simulation.

The path planned by the traditional A∗ algorithm is not the shortest and there are too many steps because of the one grid step length. To overcome the drawbacks, an improved A∗ algorithm based on variable-step-length is presented for path planning of mobile robots. The environment of the mobile robot is modeled by the grid method. Considering that the reachable region of the mobile robot may be larger than one grid in each searching step, a variable-step-length scheme is proposed. The path planned in each cycle can be selected from the lines pointing to every grid in the reachable range surrounding the current grid. For the sake that the line distance between two points is the shortest, the planning indices such as the path distance and the step number are all better than those of the polygonal line planned by the traditional A∗ algorithm. Base on the variable-step-length, the searching direction and the improved cost function are designed for the optimal step. The corresponding obstacle avoidance algorithm is also discussed. The proposed variable-step-length A∗ algorithm make the mobile robot to choose an appropriate step according to the current environment and find a shorter and less steps path. The simulation results show the improved algorithm is more efficient for the path planning of the mobile robot.

Improved genetic algorithms incorporate other techniques, methods or algorithms to optimize the performance of genetic algorithm. In this paper, improved genetic algorithms of optimum path planning for mobile robot navigation are proposed. An Obstacle Avoidance Algorithm (OAA) and a Distinguish Algorithm (DA) are introduced to generate the initial population in order to improve the path planning efficiency to select only the feasible paths during the evolution of genetic algorithm. Domain heuristic knowledge based crossover, mutation, refinement and deletion operators are specifically designed to fit path planning for mobile robots. Proposed genetic algorithms feature unique, simple path representations, and simple but effective evaluation methods. Simulation studies and real time implementations are carried out to verify and validate the effectiveness of the proposed algorithms.

Evolutionary algorithms (EA) have been used to solve path planning of mobile robots successfully. However, accuracy and convergence are always topics. The cloud model which transforms qualitative concept into quantitative description, and vice versa, is adept in uncertainty modeling. It can be used in EA for evolving in a uniform and natural way. An EA embedded with cloud model (EACM) for path planning of mobile robots was proposed in this paper. The algorithm adopted floating-point coding and realized fast converging. Simulation results verified the efficiency of our algorithm.

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

This paper presents a novel method to solve the problem of path planning for mobile robots based on bee colony algorithm. The method is inspired by collective behavior of honeybees to find food sources around the hive. The proposed method includes two steps. The first step is to use a simple rule to establish an initial collision-free path from the starting point to the target and the second step is utilizing bee colony algorithm to optimize the initial path. The results of computer simulations show that the proposed method is effective and can be used in the real-time path planning of mobile robots.

A lot of methods have been proposed for 2D path planning of mobile robot, which could be a mobile platform or a wheelchair, in planar maps. This paper addresses a concept of the shortest path planning for a mobile robot to traverse a 3D surface, which is a parametrized regular surface that models the non-flat terrain on which the mobile robot traverses. Geodesic curve linking a given start to a given target that is locally shortest on non-flat terrain is used as path. Nonlinear geodesic equations are computed by a gradient descent method with energy function of geodesic, which is shown to converge to the geodesic path in a neighborhood of target position in which a certain Lipschitz condition holds. We present numerical simulations to illustrate the geodesic path planning on non-flat terrains. I. INTRODUCTION The path planning problem is generally stated as: given a start and a goal and a description or representation of an environment, plan a path linking the start and target locations subject to some criteria of safety, mobility and optimality. The path planning for robots is a complex problem in robotics that has been studied for decades, and remain challenging in real-time robot motion in dynamic environment consisting of static or moving obstacles,such as UAV (8)-(11) and underwater robots (11,12). Researchers and engineers have been interested in two-dimensional path planning for mobile robot. Many methods have been proposed for path planning, such as graph search, randomization methods, potential fields, soft computing (e.g., fuzzy logic, neural networks, evolutionary computations) based methods. Depending on whether the environment model is completely known a priori or not, path planning is mainly classified into two categories: The first is called path planning based on the environment model or global path planning as the mobile robot knows all the information about the environment. The path could be planned offline without considering the resources of planning. The second is called path planning based on sensors or local path planning where the information of environment is provided by sensors of the robot or the environment.The robot is required to real-time plan or replan a path to account for the new information of environment gathered by the sensors and planning resources such as computing power and allowed planning time. For the first kind of path planning, we mention harmonic function (10), artifical potential field

Path planning is one of the basic problems of Autonomous Mobile Robot. The mobile robot supposed to be able to work in an unfamiliar situation using an automatic plan determined by locally sensed information. In the case of real-time environments speed calculation and rescheduling of path is essential to bypass the moving obstacle and make collision free path of robot. Robot movement planning in dynamic condition requires the actions to be selected under real-time control. To avoid run-time obstacles a new approach is presented in this paper based on Bug Algorithm. This proposed ModifiedCriticalPointBug(MCPB) algorithm, is a new Bug algorithm for path planning of mobile robots. This algorithm is carried out by the robot after regular interval, thus permitting the robot to correct its path if a new obstacle comes into the path or the old one move in a new route. As a result, the robot not only bypasses collision but also makes almost optimal path by making a sequence of run time modification in its path.

In recent years, the development of mobile robot is very fast, which has gradually penetrated into all fields of human life. Path planning is an important part of the intelligence of the mobile robot. Aiming at the problem of path planning of mobile robot in complex environment, a fuzzy control algorithm is designed in the paper. We use sensors to obtain obstacle information and target information, and control the speed of the left and right drive wheels of the mobile robot by fuzzy controller, so that the robot can go straight or turn intelligently. A reasonable path is planned by the fuzzy algorithm in the paper. Simulation results show the feasibility and effectiveness of the proposed algorithm for the path planning of mobile robot.