2017
DOI: 10.1108/ir-11-2016-0301
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A path planning method of anti-jamming ability improvement for autonomous vehicle navigating in off-road environments

Abstract: Purpose Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments. Design/methodology/approach The authors decompose the path planning problem into… Show more

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Cited by 11 publications
(3 citation statements)
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“…Applying algorithm, A* to the planned path can effectively find the best path. The traditional A* algorithm sometimes produces too many expansion nodes in the search process, thereby reducing the search efficiency [21]. In order to improve the search efficiency of algorithm A, a two-way search A algorithm is introduced in the literature.…”
Section: Common Network Path Planning Algorithmsmentioning
confidence: 99%
“…Applying algorithm, A* to the planned path can effectively find the best path. The traditional A* algorithm sometimes produces too many expansion nodes in the search process, thereby reducing the search efficiency [21]. In order to improve the search efficiency of algorithm A, a two-way search A algorithm is introduced in the literature.…”
Section: Common Network Path Planning Algorithmsmentioning
confidence: 99%
“…In 42 , the authors modified the A* algorithm to get the smoothed path. In 43 , Adaptive Window Approach function for path smoothness and safety navigation, and in 44 , fuzzy support vector machine (FSM) and general regression neural network (GRNN) functions incorporated with A* algorithm. Still, the heuristic function in the A* algorithm itself has high computation time and integrating additional functions leads to an increase in run time and more memory requirements.…”
Section: Comparison With Recent Mopp Algorithmsmentioning
confidence: 99%
“…To find the optimal path for off-road autonomous driving with static obstacle avoidance, Chu et al [22] determined the priority of each path by considering the path safety cost, path smoothness, and path consistency. Chen et al [23] proposed a path-planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) to provide a solution path for autonomous vehicles navigating in off-road environments and verified through experiments that their method could navigate vehicles on smooth and obstacle-free routes when satisfying the constraint of vehicle kinematics. Wang et al [24] considered the influence of terrain slope and soil strength on a vehicle's off-road trafficability.…”
Section: Introductionmentioning
confidence: 99%