Risk-Aware Look-Ahead Planning for Independent Multi-Goal UAV Paths: A Bounded Two-Dimensional Geometric Study

We examine independent start-to-goal path generation in bounded, static two-dimensional geometry, motivated by UAV inspection. RAL-MultiRRT combines artificial-potential-field-guided tree expansion, normalized finite-horizon scoring, and feasibility-checked shortcut refinement. A reconstructed implementation and benchmark provide 700 main path-set attempts and 96 exploratory ablation and sensitivity attempts. An additional 140 budget-validation attempts evaluate increasing iteration budgets and a common nominal search setting, giving 936 recorded attempts in the revised study. The main benchmark comprises four fixed maps with 20 seeds per method, 45 independently generated maps with one seed per method, and one narrow-passage geometry with 15 seeds per method. Five executable variants are reported with explicit implementation identities and budgets. On the fixed maps, APF-MultiRRT and RAL-MultiRRT returned valid sets in 74/80 and 78/80 attempts, respectively. Both succeeded on all 45 independent maps; RAL reduced mean paired path length by 11.38 m but added 0.394 s of recorded planner time. The length difference was not significant after Holm correction across the 20 reported quality/cost comparisons. On S3, however, jointly valid RAL paths were significantly longer and had a larger turning-angle proxy than APF paths after Holm correction. On the single narrow-passage map, valid-set counts were 1/15 and 9/15. The new iteration-budget sweep shows empirical reachability saturation by approximately 200–400 iterations in S1/S2 for the APF-guided variants, while the common-setting diagnostic confirms that matching nominal iteration and step limits does not equalize computational work. These observations support a scenario-dependent quality–computation trade-off, without establishing a general look-ahead advantage, flight-dynamic feasibility, or remote-sensing mission performance.