Figure 1.
Raster map model
Figure 2.
A-Star algorithm smoothing processing diagram
Figure 3.
A-Star algorithm path smoothing results in 30×30 environment
Table I.
Comparison of the effects of A-Star algorithm improvement
| Algorithm | Path length/m | Time for path finding/s | Number of expansion nodes | Is there a turning point |
|---|---|---|---|---|
| A-Star | 22.42 | 5.9 | 166 | Yes |
| Improved A-Star | 21.56 | 5.5 | 59 | No |
Figure 4.
Force analysis diagram of mobile robot
Figure 5.
Modified repulsion field parameters force analysis
Figure 6.
Path planning comparison
Figure 7.
Complex obstacle test comparison
Table II.
Comparison results of improved algorithm
| Experiment Name | Algorithm | Path length/m | Run time/s | Number of cycles |
|---|---|---|---|---|
| Path planning testing | APF | 49.970710 | 6.186677 | 447 |
| IAPF | 48.003037 | 5.430491 | 440 | |
| Complex obstacle testing | APF | ∞ | ∞ | ∞ |
| IAPF | 51.519690 | 6.801836 | 451 |
Figure 8.
Hybrid algorithm model diagram
Figure 9.
Static path comparison diagram
Table III.
Algorithm comparison in static environment
| Algorithm | Path length/m | Search time/s | Does the algorithm have the ability to handle dynamic obstacles |
|---|---|---|---|
| A-Star | 45.36 | 6.72 | No |
| IAPF | 48.00 | 10.43 | Yes |
| DWA | 48.86 | 28.21 | Yes |
| Hybrid algorithm | 46.54 | 8.14 | Yes |
Figure 10.
Dynamic path planning diagram
Figure 11.
Hands-free robot platform
Figure 12.
Actual test scenario
Figure 13.
Actual scene construction effect
Figure 14.
A-Star Hybrid DWA algorithm path planning
Figure 15.
Improved A-Star hybrid improved artificial potential field algorithm