Abstract
This study develops a generalized wavefront algorithm for conducting mobile robot path planning. The algorithm combines multiple target point sets, multilevel grid costs, logarithmic expansion around obstacles, and subsequent path optimization. The planning performances obtained with the proposed algorithm, the A∗ algorithm, and the rapidly exploring random tree (RRT) algorithm optimized using a Bézier curve are compared using simulations with different grid map environments comprising different numbers of obstacles with varying shapes. The results demonstrate that the generalized wavefront algorithm generates smooth and safe paths around obstacles that meet the required kinematic conditions associated with the actual maneuverability of mobile robots and significantly reduces the planned path length compared with the results obtained with the A∗ algorithm and the optimized RRT algorithm with a computation time acceptable for real-time applications. Therefore, the generated path is not only smooth and effective but also conforms to actual robot maneuverability in practical applications.
Highlights
Path planning is one of the key technologies for facilitating the autonomous maneuverability of mobile robots within environments that include obstacles. e path planning process seeks to obtain an optimum collision-free path from a starting point to a target point around obstacles according to the particular criteria such as distance traveled, travel time, and incurred cost [1,2,3]
In terms of searchbased path planning algorithms, the A∗ algorithm is a classic heuristic optimal search algorithm [13] that has had a significant impact on motion planning research. e A∗ algorithm usually searches for the best path by creating a discrete state space in the form of a state lattice [14, 15]. e state lattice consists of a node, which represents a state, and a motion primitive that arrives at a neighboring node from the original node
A state node is transformed by its motion primitive to another state node. us, the state lattice transforms the original continuous state space into a search map, and the motion planning problem becomes a search employing various search algorithms [16] for a series of motion primitives that transform the initial state to the target state
Summary
Path planning is one of the key technologies for facilitating the autonomous maneuverability of mobile robots within environments that include obstacles. e path planning process seeks to obtain an optimum collision-free path from a starting point to a target point around obstacles according to the particular criteria such as distance traveled, travel time, and incurred cost [1,2,3]. En, a logarithmic function is employed to expand the number of reserved grid points around an obstacle to avoid robot collision, and the obtained path is smoothed based on a Bezier curve. The yellow and red circles represent the respective start and end grid points, and the black circles represent the selected key points along the planned path In this case, the key point selection process is conducted according to the following steps: Step 1: detect new generated path points. Simulation Results e feasibility and effectiveness of the proposed generalized wavefront algorithm were validated by comparing its path
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