A survey on path planning techniques for autonomous mobilerobots

2020

Robots are currently replacing humans in different tasks in various sectors. Among the vital features desirable in autonomous robots is the capability of navigating safely through a given environment. Robot navigation is a process designed with the ability of avoiding any hitches or obstacles while aiming at a specific predefined position. Many studies have been proposed to find solutions to robot path-planning problems. This paper presents a survey of the heuristic and classical path-planning approaches. Focal strengths, together with the weaknesses of these approaches, were also identified to provide deep insight for future studies. As several literature studies have recommended, classical methods might not be effective in real-time applications as a result of their failure to confront the unpredictable nature of the real-world. They require a considerable amount of computation and space, while heuristic-based methods can overcome real-world problems with some modifications. To su...

Computers in Human Behavior, 2015

An autonomous mobile robot operating in an unstructured environment must be able to deal with dynamic changes of the environment. Navigation and control of a mobile robot in an unstructured environment are one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, fuzzy logic controller is optimized by integrating fuzzy logic with other soft computing techniques like genetic algorithm, neural networks, and Particle Swarm Optimization (PSO). Soft computing techniques are used in this work to tune the membership function parameters of fuzzy logic controller to improve the navigation performance. Four methods have been designed and implemented: manually constructed fuzzy logic (M-Fuzzy), fuzzy logic with genetic algorithm (GA-Fuzzy), fuzzy logic with neural network (Neuro-Fuzzy), and fuzzy logic with PSO (PSO-Fuzzy). The performances of these approaches are compared through computer simulations and experiment number of scenarios using Khepera III mobile robot platform. Hybrid fuzzy logic controls with soft computing techniques are found to be most efficient for mobile robot navigation. The GA-Fuzzy technique is found to perform better than the other techniques in most of the test scenarios in terms of travelling time and average speed. The performances of both PSO-Fuzzy and Neuro-Fuzzy are found to be better than the other methods in terms of distance travelled. In terms of bending energy, the PSO-Fuzzy and Neuro-Fuzzy are found to be better in simulation results. Although, the M-Fuzzy is found to be better using real experimental results. Hence, the most important system parameter will dictate which of the four methods to use.

Journal of Robotics, 2022

Mobile robots have been widely used in various sectors in the last decade. A mobile robot could autonomously navigate in any environment, both static and dynamic. As a result, researchers in the robotics feld have ofered a variety of techniques. Tis paper reviews the mobile robot navigation approaches and obstacle avoidance used so far in various environmental conditions to recognize the improvement of path planning strategists. Taking into consideration commonly used classical approaches such as Dijkstra algorithm (DA), artifcial potential feld (APF), probabilistic road map (PRM), cell decomposition (CD), and meta-heuristic techniques such as fuzzy logic (FL), neutral network (NN), particle swarm optimization (PSO), genetic algorithm (GA), cuckoo search algorithm (CSO), and artifcial bee colony (ABC). Classical approaches have limitations of trapping in local minima, failure to handle uncertainty, and many more. On the other hand, it is observed that heuristic approaches can solve most real-world problems and perform well after some modifcation and hybridization with classical techniques. As a result, many methods have been established worldwide for the path planning strategy for mobile robots. Te most often utilized approaches, on the other hand, are ofered below for further study.

Neural Computing and Applications, 2014

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work.

Robotics and Computer-integrated Manufacturing, 2009

A comparative study of various robot motion planning schemes has been made in the present study. Two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and a conventional potential field method (PFM) have been developed for this purpose. Training to the SC-based approaches is given off-line and the performance of the optimal motion planner has been tested on a real robot. Results of the SC-based motion planners have been compared between themselves and with those of the conventional PFM. Both the SC-based approaches are found to perform better than the PFM in terms of traveling time taken by the robot. Moreover, the performance of fuzzy logic-based motion planner is seen to be comparable with that of neural network-based motion planner. Comparisons among all these three motion planning schemes have been made in terms of robustness, adaptability, goal reaching capability and repeatability. Both the SC-based approaches are found to be more adaptive and robust compared to the PFM. It may be due to the fact that there is no in-built learning module in the PFM and consequently, it is unable to plan the velocity of the robot properly.

International Journal of Automation and Control, 2009

Present research and development in the area of mobile robots mainly aims at study of various techniques, methods and sensors being used for navigation of mobile robots. Different techniques have been discussed for the navigation of mobile robots in the first part. These techniques can be subdivided as (1) fuzzy logic technique, (2) neural network technique and (3) genetic algorithm technique. In the second part, five methods are being discussed for navigation of mobile robots. These methods are (1) potential field method, (2) grid-type method, (3) heuristic method, (4) adaptive navigation method and (v) Virtual Impedance method. The last segment focuses on different sensors being used for navigation of mobile robots. The sensors discussed are (1) ultrasonic sensor, (2) laser sensor, (3) magnetic compass disk sensor, (4) infrared sensor and (5) vision (camera) sensor. Keeping the above strategies in forefront, a comprehensive discussion has been made and is described methodologically in the current paper.

Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007

This paper introduces a genetic algorithm (GA) planner that is able to rapidly determine optimal or nearoptimal solutions for mobile robot path planning problems in environments containing moving obstacles. The method restricts the search space to the vertices of the obstacles, obviating the need to search the entire environment as in earlier GA-based approaches. The new approach is able to produce an off-line plan through an environment containing dynamic obstacles, and can also recalculate the plan on-line to deal with any motion changes encountered. A particularly novel aspect of the work is the incorporation of the selection of robot speed into the GA genes. The results from a number of realistic environments demonstrate that planning changes in robot speed significantly improves the efficiency of movement through the static and moving obstacles.

2003

Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the capabilities of the robot itself. This is the problem of planning a path through a continuous domain, for which several approaches have been developed. A method for autonomous mobile robot path planning is presented. Initially, the environment model, given as a closed chain of polygonal obstacles, is transformed into a visibility graph of obstacle vertices with a minimum number of links. An additional visibility graph of obstacle is formed simultaneously. The given initial point and destination point are presented as obstacles with a single vertex and are added to the determined graphs as vertices and as obstacles correspondingly. The extended graphs are updated and in the first step a shortest path from initial point to destination point through the obstacles is searching for. By this method a subset of obst...

2016

Navigation is one of the most challenging competences required of a mobile robot. Success in navigation requires success in perception, localization, cognition, and motion control. Path planning for mobile robot is not only guarantees a collision free path with minimum travelling distance but also requires smoothness and clearances. In this project, mathematical modeling for the robot was done; path planning was done using an algorithm, than using this mathematical model it was simulated in the MOBOTSIM software in the dynamic environment. Experimental results and real environment working model show that the proposed algorithm has effective and efficient in achieving the goal position in dynamic environment.

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology. An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization.