Path Planning Techniques for Mobile Robots A Review
Sean Campbell
Mobile robots have become increasingly popular in recent years, offering a wide range of applications in areas such as industry, agriculture, search and rescue and much more. This has been achieved mainly as a result of extremely active research and development work on robotic and autonomous technology. We are still faced with many challenges however in order for a robot to navigate efficiently and reliably in an environment without any human assistance. The robot should be capable of extracting the necessary information from the environment and taking the necessary action required to plan a feasible path for collision free motion to reach its goal. In this paper, we review the most commonly used path planning methodologies that have been applied for mobile robot navigation in both static and dynamic environments. We look at both global and local path planning approaches as well as classical and heuristic based techniques.
Review of Path Planning Algorithms for Unmanned Vehicles
Yunyi Shen
The knowledge of unmanned vehicles path planning algorithm is introduced. Firstly, the basic concept and steps of path planning are described. Secondly, it introduces Dijkstra algorithm, A-Star algorithm, RRT algorithm, PRM algorithm and other typical path planning algorithms from the classification ideas of the path planning algorithm based on search and sampling. The advantages and disadvantages of each algorithm are analyzed, and the applicable environment and conditions of the algorithm are summarized. Finally, several new algorithms and their development directions are introduced.
Path Planning of intelligent artificial autonomous mobile robots is a critical technology to achieve the task of exploration, cruise, moving etc. A large set of methods and algorithms have been introduced to solve the path planning problem in complex environment, due to huge number of research has been done in the procedure of developing large practical used autonomous mobile robots, rovers, vehicles, airplane, ship, submarine in variety of application filed in recent years such as agricultural, industry, military, space exploring. This paper collects and presents the different represents, models and planning algorithms in the application of path planning of robots. The aims of this paper are to draw a whole procedure of robot path planning, to present a detailed analysis of the available technology in each designing phase, to make a useful comparison in critical performance between different algorithms. All the methods described are based on their characteristic and the performing environment. The analysis shows that despite the large number of methods developed for path planning in different environment and variety usage, considering the complexity, completeness, effectiveness and optimality, random sample based methods are the trend which can be efficiently used to solve practical complex path planning problem. There are also some suggestions of methods' choice in each designing phase such as environment construct, representation, and algorithm.
In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented. View Full-Text
A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms
The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.
Collision Free Path Planning
Levent Guvenc
Path planning is a challenging task for autonomous driving in a dynamically changing environment. The planned path should be collision‐free with surrounding obstacles in the environment, while also smooth enough for the benefits of smooth path following and passenger comfort. This chapter begins with a detailed review of the literature on collision‐free path planning methods available in the literature. It explores the different approaches ranging from the elastic band method, the quintic spline with minimum curvature variation method, to the model‐based trajectory planning method. The chapter demonstrates the performance of model‐based trajectory planning method on a low friction road, combined with the road friction coefficient estimation module, and evaluates different complex driving scenarios with multiple objects. It ends with a discussion and comparison of the different methods introduced, followed by conclusions.
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