ignoring file ***missing required architecture armv7in file **** ignoring file ***missing required

本文解决了iOS开发中真机调试出现的警告错误:“ignoring file... missing required architecture armv7”。介绍了错误产生的原因及解决办法,并提供了制作适用于模拟器和真机的静态库的方法。

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今天调试时发现用模拟器运行成功,但是真机报错:


iOS警告: ignoring file ***missing required architecture armv7in file ****

Undefined symbols for architecture armv7:

 "_OBJC_CLASS_$_SafeInput", referenced from:

     objc-class-ref in AuthenLoginView.o


折腾了半天原来是库文件导致(IOS的库文件分模拟器版和真机版)


总结:以上这种报错的可能性:

1、没有引入静态库,重新引入

2、静态库版本是模拟器版,不是真机版,重新制作一个模拟器+真机版并引入即可

 

如何制作静态库可以参考:http://www.jianshu.com/p/3439598ea61f?utm_campaign=hugo&utm_medium=reader_share&utm_content=note

### Hybrid A* Algorithm vs Traditional A* Algorithm Differences Traditional A* algorithm employs an efficient heuristic function that typically uses the Euclidean distance from the current node to the goal as part of its cost estimation mechanism[^1]. This approach works well in idealized environments where movement can be assumed to occur freely between any two points without constraints. In contrast, Hybrid A* extends this concept by incorporating kinematic restrictions into the heuristic evaluation process. Specifically designed for robotic navigation tasks, particularly those involving wheeled vehicles or similar entities with specific turning radii and motion limitations, Hybrid A* takes these factors into account when calculating potential paths. #### Pathfinding Characteristics For traditional A*, pathfinding operations assume direct line-of-sight travel possibilities between nodes within a grid system. The simplicity allows rapid computation but may result in impractical routes unsuitable for real-world robots due to ignoring physical capabilities such as minimum turn radius requirements. On the other hand, Hybrid A* addresses these shortcomings through more sophisticated modeling techniques which simulate how actual machines would navigate around obstacles while adhering to their inherent mechanical properties. By doing so, generated trajectories better reflect feasible motions achievable by autonomous systems like cars or drones operating under dynamic conditions. ```python def calculate_heuristic_traditional_a_star(current_position, target_position): # Calculate straight-line distance using Pythagorean theorem dx = abs(target_position[0] - current_position[0]) dy = abs(target_position[1] - current_position[1]) return math.sqrt(dx * dx + dy * dy) def calculate_heuristic_hybrid_a_star(vehicle_state, target_position): # Incorporate vehicle dynamics including orientation angles and curvature limits during calculation heading_angle_difference = normalize_angle(vehicle_state.heading - atan2( target_position[1]-vehicle_state.position.y, target_position[0]-vehicle_state.position.x)) steering_penalty = max(0, min(abs(heading_angle_difference)/max_steering_angle, 1)) ** 2 lateral_offset = perpendicular_distance_to_line_segment(vehicle_state.position, start_point, end_point) longitudinal_progression_cost = estimate_longitudinal_movement_costs(start_point, end_point) total_cost = (steering_penalty + lateral_offset + longitudinal_progression_cost) / normalization_factor return total_cost ``` The inclusion of additional parameters ensures that solutions provided are not only optimal concerning shortest distances but also realistic regarding execution feasibility given hardware specifications.
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