# A Vision-Based Approach for Autonomous Motion in Cluttered Environments
## 5 Discussion  
Accurate map building and safety real-time motion planning are two core abilities needed for robots to realize autonomous movement. In Section 4, we discussed the simulations and experiments that were used to test our methods. The results show that our method can build an accurate map and generate a safe, smooth path in real time.  
In the perception experiment, the binoculars system that consists of two binoculars is used to achieve reliable robot positioning and map building. The map built by our methods is accurate, as the obstacles are accurately represented; combined with our motion-planning algorithm, it can be used to realize the robust movement of robots. Our system is lightweight; the total weight of two camera is 150 g, which can be reduced to 50 g by removing the case. In contrast, the commonly used multi-line lidar Velodyne VLP-16 weighs 850 g. Compared with method [57; 58; 59] which use a single binocular camera d435i  
Figure 11: The paths generated by our proposed path-generation algorithm (green), the Path Smooth algorithm (blue), and the A* algorithm (red) in the experiment.  
for position and mapping, our system does not need to run on platforms with strong computing power. Moreover, our method is more reliable, due to the larger viewing angle of the fisheye camera. Therefore, our method can be widely used for robot's perception task in an unfamiliar environment.  
Our motion-planning algorithm outperforms the A* algorithm and the Path Smooth algorithm in simulations and experiments; the A* algorithm and the Path Smooth algorithm are used as benchmark algorithms because they are widely used for robot motion planning. We can conclude from the results that the smoothness of our proposed algorithm is obviously better than that of the A* algorithm and comparable to the Path Smooth algorithm. The experiment results also show that the path generated by our method is safer. The Path Smooth algorithm failed to find a feasible path in experiments, due to its path colliding with obstacles. In contrast, both our algorithm and the A* can generate a collision-free path, but the path generated by A* is close to obstacles. In Simulation 1, the minimum distance to obstacles was 0.1 m. By contrast, our algorithm can make the path keep a safe distance to obstacles; in Simulation 1, the path's minimum and maximum distance to obstacles were 0.67 m and 2.69 m respectively.  
However, compared with A* and Path Smooth, our method has two shortcomings: the first is that the path generated by our proposed algorithm is about 10% longer than that of the A* and the Path Smooth algorithms; the second is that the average time of our algorithm to generate a path is much longer. In order to get a safe path, we need to push the path away from obstacles, and this results in the path being lengthened. Thus, the path generated by our method is slightly longer but much safer. Moreover, calculating the ESDF of the path point makes our algorithm take a longer time to plan, but the planning time is still less than 10 ms, which can meet the real-time requirements of the robot system. Therefore, our motion-planning algorithm can generate a path that outperforms the A* and the Path Smooth in real time.