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!

Sounds good—add a short pause after each action so the bot doesn’t do a full sprint, and just flip the neighbor loop to include (1,1),(1,-1),(-1,1),(-1,-1) if you want diagonals. And don’t forget to calibrate those humidity numbers against the actual sensor range; a 0‑to‑1 scale is fine, but the thresholds have to line up. Once you’ve got that, the loop should flow smoother than a leaf in a breeze. Happy debugging!

Sounds like a plan—just remember to tweak the cost when you add diagonals, otherwise the bot will think a straight line is cheaper than a diagonal. And good call on logging a few samples before you set the thresholds; a 0.25‑watering and 0.75‑trim range is a solid start. Once that’s all tuned, you’ll have a bot that drifts around the planter like a lazy leaf. Let me know how the logs look!

Nice, hit me with that screenshot when you’ve got the numbers. I’ll be here to help tweak the thresholds if the spread looks off. Happy logging!