Given two points, starting and ending, the path planning is the robotic field computes the robot path.
The most accessible path planning is the straight line. In that case, there is no need for a complicated computational cost, assuming no obstacle on the path.
The last case is too easy. Often, there is an obstacle in the environment where the robot has to move. In any case, there could be other constraints like a maximum cruise velocity.
In the majority, motion planning is an offline computation since motion planning has many constraints, and the computation cost increases significantly.
The challenge during my activity research has been integrating some inverse kinematics concepts into motion planning, intending to reduce the computational time and make motion planning applicable to a real system with dynamic objects around.
An example can be a situation where the robot has to go from point A to point B, but there is an obstacle that can move during the time. Imagine that during the robot movement, the block starts moving, and it goes on the robot path. Motion planning can be capable of computing a new approach in a short time.
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