Sure thing, here’s a quick sketch of what you’re looking for. Think of it as a loop that keeps an eye on two main data streams: the plant’s state (humidity, growth stage, root pressure) and the garden’s layout (paths, obstacles, watering stations). The bot will: 1. Poll the sensors 2. Run a simple decision tree (or tiny neural net if you’re into that) to decide whether to water, trim, or wait 3. Use A* (or Dijkstra) on a grid that represents the planter’s interior to pick the shortest route to the next watering point or trimming spot 4. Act via your actuators, then sleep for a bit Here’s a bare‑bones Python prototype to get you started: ``` import time import heapq class Plant: def __init__(self, id, pos): self.id = id self.pos = pos self.humidity = 0.0 self.growth = 0.0 self.root_pressure = 0.0 class GardenGrid: def __init__(self, width, height, obstacles): self.width = width self.height = height self.obstacles = set(obstacles) def neighbors(self, node): dirs = [(1,0),(-1,0),(0,1),(0,-1)] result = [] for dx,dy in dirs: nx, ny = node[0]+dx, node[1]+dy if 0 <= nx < self.width and 0 <= ny < self.height and (nx,ny) not in self.obstacles: result.append((nx,ny)) return result def heuristic(self, a, b): return abs(a[0]-b[0]) + abs(a[1]-b[1]) def astar(self, start, goal): frontier = [(0, start)] came_from = {start: None} cost_so_far = {start: 0} while frontier: _, current = heapq.heappop(frontier) if current == goal: break for next in self.neighbors(current): new_cost = cost_so_far[current] + 1 if next not in cost_so_far or new_cost < cost_so_far[next]: cost_so_far[next] = new_cost priority = new_cost + self.heuristic(next, goal) heapq.heappush(frontier, (priority, next)) came_from[next] = current path = [] node = goal while node != start: path.append(node) node = came_from[node] path.reverse() return path def decide_action(plant): if plant.humidity < 0.3: return "water" if plant.growth > 0.8 and plant.root_pressure > 0.7: return "trim" return "wait" def main_loop(): grid = GardenGrid(10,10,obstacles=[(3,3),(4,3)]) plants = [Plant(1,(2,2)), Plant(2,(7,7))] bot_pos = (0,0) while True: for plant in plants: # Update sensor readings here plant.humidity += 0.01 plant.growth += 0.02 plant.root_pressure += 0.01 action = decide_action(plant) if action == "water": path = grid.astar(bot_pos, plant.pos) # Move along path, trigger watering bot_pos = plant.pos print(f"Watered plant {plant.id}") elif action == "trim": path = grid.astar(bot_pos, plant.pos) bot_pos = plant.pos print(f"Trimmed plant {plant.id}") time.sleep(5) if __name__ == "__main__": main_loop() ``` Feel free to swap out the decision logic for something more sophisticated—maybe a tiny Bayesian model that takes light levels into account. The A* bit is just a skeleton; tweak the heuristic if your planter has diagonal movement or weighted costs. That should give you a decent starting point. Happy automating!