roverslip op• Data kinds: matrix × image2d × scalar × table → table
• Call: import fullseye as fs; fs.ledger.path_slip_risk(path, dtm, cell: 'float', model, alpha: 'float' = 0.9, speed: 'float' = 0.05, s_max: 'float' = 0.9) -> 'dict' (to call the implementation directly, import roverslip; roverslip.path_slip_risk(path, dtm, cell: 'float', model, alpha: 'float' = 0.9, speed: 'float' = 0.05, s_max: 'float' = 0.9) -> 'dict'; from the registry, opsdrive.get("path_slip_risk"))
Evaluates a path edge by edge: slope, mean slip, CVaR_α slip, probability of exceeding s_max (normal distribution for the Gaussian process, interpolation of the quantile curves for quantile regression), and the travel times at the mean slip and at the CVaR.
> The detailed description below is the original text — the summary and the headings are translated.
返り値 {"length" [m], "time_mean" [s], "time_cvar" [s], "max_pitch" [deg], "max_mean_slip", "max_cvar_slip", "p_exceed_max": 辺ごとの越える確率の最大, "p_exceed_any": 辺が独立と置いた 1 − Π(1 − p), "pitch", "mean_slip", "cvar_slip", "p_exceed"}(後ろ 4 つは辺ごと)。下りの辺は −s で測る。
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_rover_slip_risk_path — py -3.11 examples/poc_rover_slip_risk_path.py
table as input)course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same
roverslip)bekker_pressure · bekker_wheel_sinkage · wheel_forces · wheel_sinkage · wheel_traction_curve · slope_slip_curve · ground_shift_track · odometry_slip
*Provenance: roverslip.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.