add auto_scan_all_environment

uav_langchain_tools.py

@@ -520,6 +520,8 @@ def create_uav_tools(client: UAVAPIClient) -> list:
                 return "Error: drone_id is required"
 
             result = client.return_home(drone_id)
+            if result["status"] == "error":
+                result["info"] = "If the drone is IDLE, first take off, then return home."
             return json.dumps(result, indent=2)
         except json.JSONDecodeError as e:
             return f"Error parsing JSON input: {str(e)}. Expected format: {{\"drone_id\": \"drone-001\"}}"
@@ -1438,6 +1440,113 @@ def create_uav_tools(client: UAVAPIClient) -> list:
         else:
             return f"Finished path. Final coverage: {current_coverage:.2%}. Wait for a while and continue calling this function! return [TASK DONE] this time"
     
+    @tool
+    def auto_scan_all_environment() -> str:
+        """
+        Command all available drones to collaboratively scan the area (0,0) to (1024,1024) using obstacle avoidance.
+        Drones will plan safe paths to grid points, avoiding known obstacles, and detecting new ones along the way.
+        
+        No input required.
+        """
+        global targets_expolred, obstacles_detected
+        import math
+        import json
+
+        try:
+            # 1. 初始化：获取无人机并起飞
+            drones = client.list_drones()
+            if not drones:
+                return "Error: No drones available for scanning."
+            
+            drone_ids = [d['id'] for d in drones]
+            active_drones = []
+            
+            # 起飞高度设置为 20m，既能避开低矮障碍，也能获得较好视野
+            scan_altitude = 20.0
+            
+            for did in drone_ids:
+                status = client.get_drone_status(did)
+                if status.get('state') == 'ground' or status['position']['z'] < 1.0:
+                    client.take_off(did, altitude=scan_altitude)
+                active_drones.append(did)
+            
+            # 初始感知：起飞后先看一眼，建立初步地图
+            env_perception()
+            
+            # 2. 生成扫描网格 (Grid Generation)
+            # 区域 1024x1024。步长设为 250m，平衡覆盖率与时间成本
+            scan_points = []
+            area_size = 1024.0
+            step_size = 250.0 
+            
+            x = step_size / 2
+            while x < area_size:
+                y = step_size / 2
+                while y < area_size:
+                    scan_points.append((x, y))
+                    y += step_size
+                x += step_size
+            
+            # 3. 任务分配 (Round-Robin)
+            tasks = {did: [] for did in active_drones}
+            num_drones = len(active_drones)
+            for i, point in enumerate(scan_points):
+                assigned_drone = active_drones[i % num_drones]
+                tasks[assigned_drone].append(point)
+                
+            scan_log = []
+            max_points = max([len(t) for t in tasks.values()]) if tasks else 0
+            
+            # 4. 执行循环：规划 -> 移动 -> 感知
+            for i in range(max_points):
+                for did in active_drones:
+                    if i < len(tasks[did]):
+                        tx, ty = tasks[did][i]
+                        
+                        # 构造导航参数
+                        nav_payload = json.dumps({
+                            "drone_id": did,
+                            "x": tx,
+                            "y": ty,
+                            "z": scan_altitude
+                        })
+                        
+                        # === 核心修改：调用 auto_navigate_to_non_tool 进行路径规划 ===
+                        # 该函数会利用当前 obstacles_detected 里的数据计算避障路径
+                        # 如果目标点在障碍物内，它会返回错误信息，我们只需记录并跳过
+                        try:
+                            # 1. 尝试导航
+                            nav_result_str = auto_navigate_to_non_tool(nav_payload)
+                            nav_result = json.loads(nav_result_str)
+                            
+                            status = nav_result.get("status", "error")
+                            
+                            # 2. 无论导航成功与否（可能半路停下），都进行感知
+                            # 这对于"一边撞墙一边开图"的探索过程至关重要
+                            new_entities_found = env_perception()
+                            
+                            log_msg = f"Drone {did} -> ({tx:.1f}, {ty:.1f}): {status}"
+                            if new_entities_found:
+                                log_msg += " [New Entities Detected]"
+                            scan_log.append(log_msg)
+                            
+                        except Exception as nav_err:
+                            scan_log.append(f"Drone {did} nav error: {str(nav_err)}")
+                            # 即使出错，也要尝试感知当前位置
+                            env_perception()
+
+            # 5. 汇总结果
+            return json.dumps({
+                "status": "success",
+                "message": f"Global smart scan completed with {num_drones} drones.",
+                "total_targets_detected": len(targets_expolred),
+                "total_obstacles_detected": len(obstacles_detected),
+                "scan_details": scan_log
+            }, indent=2)
+
+        except Exception as e:
+            return f"Error during smart scanning: {str(e)}"
+    
 
     # Return all tools
     return [
@@ -1469,5 +1578,6 @@ def create_uav_tools(client: UAVAPIClient) -> list:
         charge,
         get_nearest_waypoint,
         get_all_waypoints,
-        get_targets
+        get_targets,
+        auto_scan_all_environment
     ]