# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 4 Open_Planner Algorithm  
The optimal path not only needs global planning, but also needs local dynamic programming to avoid obstacles and ensure safety. In this paper, Open_Planner algorithm was used for local path planning.  
The Storage Structure of Nodes Is Optimized  
The Open_Planner algorithm uses roll-outs generator to obtain multiple local sampling trajectories according to the path planned by the whole route, and calculates the normalized  
Figure 4: Schematic diagram of the continuity constraint.  
cost function of each sampling trajectory through obstacle detection and cost function toll-outs evaluator. The trajectory line with the lowest cost and the best is used as the local path.  
The Open_Planner partial plan is divided into two parts: roll-outs generator and roll-outs evaluator. The roll-outs generator is used to explore local paths and optimize trajectories, and the roll-outs evaluator is used to select the optimal path for the cost function.  
The input information of the roll-outs generator is the current location of the vehicle, the global planning path, and rollouts. Rollouts are divided into three parts: car tip, roll-in, and roll-out, as shown in Figure 5.  
Car tip represents the starting point from the center of the vehicle to the horizontal sampling, 'Roll-in' represents the starting point from the horizontal sampling to the parallel sampling, and 'Roll-out' represents the maximum planning distance from the starting point of the parallel sampling. By intercepting the global path, point sampling is carried out for the captured global path, and the final trajectory is generated by smoothing the obtained sampling points.  
The roll-outs generator first extracts car tip, roll-in and roll-out from the global path according to the current position of the vehicle, then conducts transverse sampling for the extracted global path, and, finally, makes the path smooth using conjugate gradient.  
Roll-outs evaluator evaluates different paths using three cost functions, namely, priority cost, collision cost, and transition cost. Priority cost indicates that the middle path is preferred when there is no obstacle. There are two types of collision cost, lateral cost denotes the horizontal distance of local trajectory from the obstacle, longitudinal cost denotes the vertical distance of local trajectory from the nearest obstacle. The transition cost limits the ability of vehicles to switch frequently between candidate paths, ensuring smooth progress.  
Then, cost normalization is carried out. Each trajectory is evaluated with additional cost function, and three different normalized cost measures are calculated to avoid one cost in the evaluation function occupying too much. Finally, the cost function is used to calculate the cost, and a local path with the lowest cost is selected. The cost function is shown in Equation (15):  
\[H(x)=l\cdot\alpha+d\cdot\beta+v\cdot\gamma+k\cdot\delta \tag{15}\]  
where \(l\) represents the distance between the vehicle and the lane centerline, \(d\) represents the distance between the vehicle and the obstacle, \(v\) represents the rate of change in speed, and \(k\) represents the path curvature.