In this second deliverable, the rover must find water on the planet. Remember that:$1.$ The rover cannot move in the middle when the difference between regions is greater than $1\mathrm{.}2.$ He also cannot climb over obstacles$\mathrm{.}3.$ The rover moves like any car.In addition, the rover has a greater fuel consumption $($cost$)$ the further it is from the initial level or level $0($sea level$).$We have the following code.Every search algorithm says that it cant find and answer. Please help us to solve it$.$import matplotlib.pyplot as pltimport numpy as npimport randomfrom matplotlib.patches import Rectangleimport matplotlib.colors as mcolorsrows $=15$columns $=30$# Generate mapmap $=[[$random$.$choice$([\text{'}0\text{'},\text{'}1\text{'},\text{'}2\text{'},\text{'}3\text{'},\text{'}4\text{'},\text{'}5\text{'},\text{'}*\text{'},\text{'}$~$\text{'}])$ for $\_$ in range$($columns$)]$ for $\_$ in range$($rows$)]$# Roverrover$\_$row $=$ random.randint$(0,$ rows $-1)$rover$\_$column $=$ random.randint$(0,$ columns $-1)$map$[$rover$\_$row$][$rover$\_$column$]=\text{'}$R$\text{'}$# Colorscolor$\_$dict $=\{\text{'}0\text{'}$: 'green', $\text{'}1\text{'}$: 'lightgreen', $\text{'}2\text{'}$: 'green', $\text{'}3\text{'}$: 'green', $\text{'}4\text{'}$: 'green', $\text{'}5\text{'}$: 'darkgreen', $\text{'}*\text{'}$: 'red', $\text{'}$~$\text{'}$: 'blue', $\text{'}$R$\text{'}$: 'black'$\}$# Rover $=$ R# Water $=$ ~# Obstacule $=*$fig$,$ ax $=$ plt$.$subplots$()$# Map with colorsfor i in range$($rows$)$: for j in range$($columns$)$: value $=$ map$[$i$][$j$]$ ax$.$text$($j$,$ i$,$ value, ha$=$'center', va$=$'center', color$=$'black'$)$ if value in color$\_$dict: color $=$ color$\_$dict$[$value$]$ if value.isdigit$()$: green$\_$tone $=$ int$($value$)/5\mathrm{.}0$ color $=$ mcolors.hsv$\_$to$\_$rgb$((120/360,$ green$\_$tone, $0\mathrm{.}8))$ rect $=$ Rectangle$(($j $-0\mathrm{.}5,$ i $-0\mathrm{.}5),1,1,$ linewidth$=1,$ edgecolor$=$'black', facecolor$=$color$)$ ax$.$add$\_$patch$($rect$)$# Limitsax.set$\_$xlim$(-0\mathrm{.}5,$ columns $-0\mathrm{.}5)$ax$.$set$\_$ylim$($rows $-0\mathrm{.}5,-0\mathrm{.}5)$ax$.$set$\_$xticks$([])$ax$.$set$\_$yticks$([])$plt$.$show$()$from simpleai.search import $($ SearchProblem, depth$\_$first, breadth$\_$first, astar, greedy, uniform$\_$cost$,)$class RoverSearchProblem$($SearchProblem$)$: def $\_\_$init$\_\_($self$,$ initial$\_$state, map$)$: super$($RoverSearchProblem$,$ self$).\_\_$init$\_\_($initial$\_$state$)$ self.map $=$ map def actions$($self$,$ state$)$: row, col $=$ state possible$\_$actions $=[]$ for dr$,$ dc in $[(1,0),(-1,0),(0,1),(0,-1)]$: new$\_$row, new$\_$col $=$ row $+$ dr$,$ col $+$ dc if $(0<=$ new$\_$row $<$ len$($self$.$map$)$ and $0<=$ new$\_$col $<$ len$($self$.$map$[0])$ and self.map$[$new$\_$row$][$new$\_$col$]$ not in $[\text{'}1\text{'},\text{'}2\text{'},\text{'}3\text{'},\text{'}4\text{'},\text{'}5\text{'}]$ and abs$($int$($self$.$map$[$row$][$col$])-$ int$($self$.$map$[$new$\_$row$][$new$\_$col$]))<=1)$: possible$\_$actions.append$(($new$\_$row, new$\_$col$))$ return possible$\_$actions def result$($self$,$ state, action$)$: return action def is$\_$goal$($self$,$ state$)$: row, col $=$ state return self.map$[$row$][$col$]==\text{'}*\text{'}$ def cost$($self$,$ state, action, state$2)$: row, col $=$ state row$2,$ col$2=$ state$2$ return abs$($int$($self$.$map$[$row$][$col$])-$ int$($self$.$map$[$row$2][$col$2]))$# Define initial state and mapinitial$\_$state $=($rover$\_$row, rover$\_$column$)$problem $=$ RoverSearchProblem$($initial$\_$state, map$)$# Solve using different algorithmsdfs$\_$solution $=$ depth$\_$first$($problem$)$bfs$\_$solution $=$ breadth$\_$first$($problem$)$astar$\_$solution $=$ astar$($problem$,$ graph$\_$search$=$True$)$greedy$\_$solution $=$ greedy$($problem$,$ graph$\_$search$=$True$)$uniform$\_$cost$\_$solution $=$ uniform$\_$cost$($problem$)$# Print solutions or analyze them furtherprint$("$DFS Solution:", dfs$\_$solution.state$)$print$("$BFS Solution:", bfs$\_$solution.state$)$print$("$A$*$ Solution:", astar$\_$solution.state$)$print$("$Greedy Solution:", greedy$\_$solution.state$)$print$("$Uniform Cost Solution:", uniform$\_$cost$\_$solution.state$)$