## Comparative study

In this section a comparative study on a set of deterministic-sampling based dynamic trajectory planners is carried out to analyze the performance of the DFM algorithm. A*, FM, FM*, D* Lite and DFM algorithms are tested using the simulation testbed described in the previous section. The graph of figure 6 depicts the performance of the five trajectory planning algorithms over a range of replanning computations (runs).

Runs

Fig. 6. Performance of A*, FM, FM*, D* Lite and DFM algorithms as a function of the number of runs. Each graph represents the evolution of the cumulated computation time of each algorithm over the runs.

Runs

Fig. 6. Performance of A*, FM, FM*, D* Lite and DFM algorithms as a function of the number of runs. Each graph represents the evolution of the cumulated computation time of each algorithm over the runs.

One can see in figure 6 that FM* is the fastest static algorithm. However, from run 11, dynamic replanning algorithms (D* Lite and DFM) give better performance than static planning algorithms (A*, FM and FM*). This is explained by the greater efficiency of dynamic replanning algorithms when changes in the cost function happen close to the goal configuration.

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