In open indoor spaces, robots generally depends on global path-planning algorithms like A* or Dijkstra’s for smooth navigation, on the other hand in GPS-unreachable areas or rough terrain, they use local strategies like SLAM or reactive obstacle avoidance to handle dynamic conditions. Outdoor environments may also demand real-time adjustments for unpredictable terrain or obstacles, prioritizing robustness over route optimization.

Dear Farkad Adnan

Different environmental conditions, such as indoors and corridors, significantly affect the pathfinding strategies for robot movement. Here are some key aspects to consider. One of them can be Space structure and topology. In closed spaces such as rooms, robots often encounter limited space and many obstacles. This requires the use of algorithms that can efficiently plan a route taking into account bottlenecks and obstacles (e.g., A, RRT: Rapidly-exploring Random Tree algorithms).

In corridors, where the space is more linear, pathfinding strategies can focus on moving in a straight line and avoiding collisions with objects that may be within the corridor. Algorithms that optimize travel time and minimize the risk of collisions can be used here.

Environmental conditions influence the choice of sensors and robot strategies for location and adaptation. Infrared or ultrasonic sensors are needed in dark places, while cameras are needed in bright places. Dynamic environments, such as rooms with moving objects, require real-time adaptation, for example using machine learning to predict movements. Safety becomes more important in crowded places, which complicates pathfinding. In general, different environmental conditions require a flexible approach to robot routes, sensors, and algorithms.