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Page 18 of 27 Wang et al. Intell Robot 2023;3(4):538-64 I http://dx.doi.org/10.20517/ir.2023.30

Figure 8. Updating process of the probability map in different scenarios.

a Gauss-Markov motion model. The result shows that the existence probability of the target spreads towards
adjacent grid cells, and the uncertainty of the target position increases with the simulation steps. In Scenario II,
thetarget will be subject totheevasivevirtual force A generated by the UAVs in additionto random movement,
showing a trend of evading the UAVs. The result shows that there is a significant reduction in the existence
probability of the target around the UAVs. In Scenario III, the target is affected by the evasive virtual forces A
and B, and the result shows that the target in the UAVs’ forward direction will additionally evade both sides
of the forward direction, while the target behind the UAVs may return to the previous search area by random
movement after the UAVs have moved away.

Comparedwithpreviousmethods [10,11] , thechangesintheprobabilitymapinthesimulationresultsareconsis-
tent with the actual experimental scenario rather than purely numerical assumptions, reflecting the reasonable
evasive maneuvers of moving targets and improving the efficiency of the search process.

6.2. Analysis on the influence of different factors

In addition to the configuration in the first experiment, set = 3 with the initial position of [850 850] ,

[1850 3550] and [1250 2250] . To avoid a lengthy coefficient tuning process, a set of coefficients that have

performed well in multiple experiments is directly presented here, and the factor tunning of the objective