93 lines
4.5 KiB
TeX
93 lines
4.5 KiB
TeX
% Overall three-stage framework: offline field prior -> parallel coarse search
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% -> joint numerical optimization. Laid out to fit one IEEE column (252 pt)
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% at full size, so no scaling is applied when included. Section numbers are
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% plain text so the standalone figure compiles independently of main.tex.
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\documentclass[border=1pt]{standalone}
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\input{figstyle}
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\begin{document}
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\begin{tikzpicture}[
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blk/.style={draw=sitec, line width=0.5pt, rounded corners=1.2pt,
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align=center, inner sep=2.2pt, font=\tiny,
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text width=23mm, minimum height=5.4mm},
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io/.style={blk, fill=fillc, text width=20mm},
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offb/.style={blk, fill=centc!10, draw=centc!70},
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parkb/.style={blk, fill=parkc!10, draw=parkc!70, text width=21mm},
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exitb/.style={blk, fill=exitc!10, draw=exitc!70, text width=21mm},
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optb/.style={blk, fill=corrc!10, draw=corrc!70, text width=52mm},
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stage/.style={draw=sitec!45, line width=0.4pt,
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dash pattern=on 1.4pt off 1.2pt, rounded corners=2pt},
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lbl/.style={font=\tiny\itshape, sitec},
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ar/.style={->, line width=0.45pt, sitec,
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>={Latex[length=1.3mm,width=1.0mm]}},
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note/.style={font=\tiny, sitec, align=left},
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]
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% =====================================================================
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% Stage 1 -- offline construction of the guidance-field prior
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% =====================================================================
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\node[io] (bnd) at (0,0) {site boundary point cloud};
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\node[offb] (skel) at (0,-0.98) {EVG skeleton $+$ QP smoothing
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$\rightarrow \varLambda_\text{center}$};
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\node[offb] (guid) at (0,-1.96) {normal offset $\pm W/2$
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$\rightarrow \varLambda_\text{park},
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\varLambda_\text{exit}$};
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\node[offb] (fld) at (0,-3.05) {kernel superposition
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$\rightarrow U_\text{park}, U_\text{exit}$
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(mirrored about $\varLambda_\text{center}$)};
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\foreach \a/\b in {bnd/skel, skel/guid, guid/fld}{ \draw[ar] (\a) -- (\b); }
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\node[stage, fit=(bnd)(fld), inner sep=3pt] (s1) {};
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\node[lbl, above=0.8pt of s1.north] {Stage 1: offline (Sec.~III)};
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% =====================================================================
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% Stage 2 -- two coarse paths searched independently and in parallel
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% =====================================================================
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\node[parkb] (hap) at (3.72,-0.82) {field-adaptive Hybrid A*
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on $U_\text{park}$};
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\node[exitb] (hax) at (3.72,-2.24) {field-adaptive Hybrid A*
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on $U_\text{exit}$};
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\node[stage, fit=(hap)(hax), inner sep=3pt] (s2) {};
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\node[lbl, above=0.8pt of s2.north, align=center]
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{Stage 2: online, parallel (Sec.~IV)};
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\draw[ar] (fld.east) -- ++(0.22,0) |- (hap.west);
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\draw[ar] (fld.east) -- ++(0.22,0) |- (hax.west);
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% =====================================================================
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% Stage 3 -- joint optimization of the two coarse paths
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% =====================================================================
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\node[optb] (corr) at (1.86,-4.35)
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{convex corridors $\mathcal{P}_k$ from the perception snapshot
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(ellipsoid proxy, inset by $\Delta_\text{cut}$)};
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\node[optb] (nlp) at (1.86,-5.45)
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{joint NLP:
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$\mathbf{z}=[\mathbf{z}_\text{park};\mathbf{z}_\text{exit}]$,
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interaction cost $J_\varPsi$, solved by Ipopt};
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\node[io, text width=28mm] (out) at (1.86,-6.45)
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{$\varGamma_\text{park}^\star,\ \varGamma_\text{exit}^\star$};
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\node[stage, fit=(corr)(out), inner sep=3pt] (s3) {};
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\node[lbl, below=0.8pt of s3.south]
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{Stage 3: joint optimization (Sec.~V)};
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\draw[ar] (hap.east) -- ++(0.20,0) |- ([yshift=1.1mm]corr.east);
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\draw[ar] (hax.south) -- ++(0,-0.30) -| ([xshift=8mm]corr.north);
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\draw[ar] (corr) -- (nlp);
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\draw[ar] (nlp) -- (out);
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% ---------- what each stage fixes ------------------------------------
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\node[note] at (0.30,-3.72) {\textbf{fixes} topology};
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\node[note] at (3.30,-3.00) {\textbf{fixes} $N,\{\delta_j\}$};
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\node[note] at (-0.62,-5.45) {\textbf{fixes}\\shape,\\$\kappa$ cont.};
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% ---------- rebuild trigger -------------------------------------------
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\draw[ar, densely dotted, centc, >={Latex[length=1.3mm,width=1.0mm]}]
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(out.west) -- ++(-0.42,0) |- (skel.west);
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\node[font=\tiny, centc, align=center] at (-1.32,-2.55)
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{rebuild only\\on boundary\\change};
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\end{tikzpicture}
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\end{document}
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