Abstract Owing to the great application potential of Intelligent Autonomous Robots (IARs) [105, 84], they will play an important role in the future of human beings. This tendency has attracted attention of many researchers in the area of engineering, computer science, locomotion, and so on. Among the broad set of research areas in IAR navigation, collision avoidance is indispensable for autonomous navigation. The research in this field can be divided into two major categories: global trajectory planning methods [1, 109, 111] and local motion planning methods [29, 52, 54, 130, 133]. The former is to design a collision free trajectory for a robot moving from a starting point towards a target among obstacles in a known environment. These methods, in general, can find an optimal path, but they are unsuitable in real-world applications. The latter is to move a robot in an unknown environment and to avoid collision dynamically. Usually, only a feasible path, instead of an optimal path, is found in these methods. However, they are more flexible for practical time-varying environments. Now it is perceived that both of the methods have relative advantages and disadvantages if they are used separately. The advantages of these methods can be utilized, if they are combined for an IAR navigation.
Evolutionary Trajectory Planning of Autonomous Robots
2004-01-01
25 pages
Article/Chapter (Book)
Electronic Resource
English
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