Obstacle Avoidance Path Planning of Rotor UAV
With the increasingly wide application of UAV in military, civil and other fields, autonomous navigation and obstacle avoidance of UAV has become a hot issue of study, and path planning is one of core technologies to realize it. Based on traditional path
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Abstract With the increasingly wide application of UAV in military, civil and other fields, autonomous navigation and obstacle avoidance of UAV has become a hot issue of study, and path planning is one of core technologies to realize it. Based on traditional path planning method cannot juggle global optimal and real-time obstacle avoidance effectively, this paper proposes a new path planning method fusing the global and the local,it uses genetic algorithm in global and artificial potential field method in local, then gets an optimal path to reach the destination safely. Through simulation experiment, this method has good real-time and reliability, meeting the requirements of autonomous navigation and obstacle avoidance. Keywords Rotor UAV algorithm Optimal path
Path planning
Artificial potential field
Genetic
1 Introduction As UAV is finding wider and wider application in the field of military and civilian, UAV autonomous navigation and obstacle avoidance technology has become a hot research topic, and path planning in flight is one of the core technology of realizing autonomous navigation and obstacle avoidance. According to the target range of UAV path planning, which can be divided into global path planning and local path planning [1]. From the state of the planning environment it is divided into static path planning and dynamic path planning [3].
X. Zhang (&) X. Hao G. Sun Y. Xu Information Engineering University, Zhengzhou 450000, China e-mail: [email protected] X. Zhang X. Hao G. Sun Y. Xu Beidou Navigation Technology Collaborative Innovation Center of Henan, Zhengzhou 450000, China © Springer Nature Singapore Pte Ltd. 2017 J. Sun et al. (eds.), China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume I, Lecture Notes in Electrical Engineering 437, DOI 10.1007/978-981-10-4588-2_41
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Common four UAV path planning methods are template matching method, artificial potential field method respectively, map building method and artificial intelligence method [4]. The principle of template matching method is simple and matching effect is good, but it can’t find a matching path without enough template library [5]; The principle of artificial potential field method is simple and easy to control, but it often has local minimum points and the difficulty is the design of the gravitational field and the repulsive force field in a dynamic environment [6]; Map building method is straightforward and obstacle avoidance effect is good, but excessive amount of data leads to its bad real-time [7]; Artificial intelligence method improves the intelligence features of UAV and makes up for the shortcomings of other methods, but it can’t complete the task alone in unknown environments [8]. In existing known methods, global planning method has a good effect on the known environment, but poor real-time performance in unknown environment; Local planning method can timely deal with random encounter obstacles, but the path planning is difficult to achieve the best in the whole environment,
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