Stairs Sensor Driver
Stairs are often a challenging environment for a robotic cleaner. This is because the stairs may contain static and dynamic obstacles (humans, flower pots) that may obstruct the robot’s path.
The environmental perception system in this study is able to detect and localize these objects through a vision sensor. Additionally, the depth based clustering of these detected obstacles and debris allows the platform to plan a cleaning path efficiently.
Detection of stairs
Stairs are very often a challenge for robots to detect as they may be located at any angle from the axis of rotation. To overcome this issue, a stairways sensor driver is developed that uses 2D lidar and depth information to detect stairs.
This algorithm is tested for staircase recognition and shows that it is reliable. Furthermore, it is also tested for detecting misalignment in the front riser of the staircase using an IMU sensor and shows that it can detect and correct the alignment.
The EPS system comprises of a RGB-D vision sensor, SSD MobileNet based object detection module, and depth based error correction unit. This system is designed to stairways sensor driver recognize objects in the environment and helps the sTetro robot to make decisions.
After the EPS system is deployed, the sTetro robot can approach and climb staircases autonomously. It can also recognize static and dynamic obstacles, like a human, that might interfere with the operation of the stairs.
For recognizing the stairs, the sTetro robot is fixed with the 2D lidar in a vertical position and a reference scan is taken. The sTetro then moves into an arbitrary orientation and starts scanning its environment by turning the counterclockwise direction. After this, the scan matching algorithm is activated and range data are obtained to distinguish a staircase from an object in the surrounding space.
Figure 10(a) shows the experimental setup used to test the stair recognition. This demonstrates that when the robot is facing a staircase, the 2D lidar is clearly detected and its range data is consistent with a staircase. The sTetro then moves towards the stairs and the range scan data are again consistent with the staircase.
However, when the robot is not facing a staircase but instead is facing a wall, the measured scan gives a one big step in range data as shown in Figure 14(b). The sTetro then moves into an undefined angular orientation and tries to scan the environment to distinguish the stairs from the walls.
In addition, the EPS system is empowered with an adaptive control and autonomous stair climbing framework to support the sTetro in its cleaning operation. It combines the RGB-D vision sensor with the SSD MobileNet based object detection module and a depth based error correction unit to provide accurate staircase recognition, static and dynamic obstacle detection and debris detection.
Detection of people
The detection of people at the top and bottom of your stairs is a key component in any automated obstacle crossing system. The best way to achieve this is to use a stairways sensor driver. The sensor will allow you to automatically turn on your LED lights when someone enters or leaves your stairs. The stairways sensor is available from many manufacturers and can be easily installed in a matter of minutes.
The sensor is accompanied by an easy to read manual and a power supply. To get started, you will need to connect two PIR sensors with the power supply and a length of wire that is the right gauge for your application. The most important part is to ensure that the sensors are correctly positioned for optimal performance. You can also opt for a power supply with integrated sensors and an automatic switch on/off function.
The stairways sensor can be used to perform a variety of functions depending on your needs. Some of the more obvious stairways sensor driver uses include automatic lighting control, securing your home from intruders and keeping your belongings safe from thieves. There are many stairways sensor models to choose from, and the most important thing is to select one that is suited to your application.
Detection of carriage
The carriage is a crucial part of a stairway as it allows the passenger to move from one level to the next. It comprises a power bogie (PB) and a levelling bogie (LB) mounted on a common frame. The power bogie provides the drive to the bogies which, in turn, rotate a pinion on a rack to propel the carriage along the rails. Side rollers (S) on both bogies mechanically keep the carriage square on the rails and an anti-tilt mechanism prevents the carriage from tipping excessively when moving around vertical bends.
The stairways sensor driver has a few tricks up its sleeve in the way of detection. For example, it will light up when a person enters or leaves the stairs. It also has a timer that lights up the steps when a person turns left or right at the top of the stairs and switches off once a person arrives at the bottom. It can also set a range of lighting effects, including a blinking LED which illuminates the steps as it approaches and fades away after a predetermined length of time.
It can also detect a carriage when it enters or leaves the stairs. This is a useful function that can be used to warn a user of any obstructions on the stairs before they start climbing or descending.
Detection of obstacles
The detection of obstacles by a stairways sensor driver can be a challenge, especially when the robot is working in dynamic environments such as warehouses or hospitals. In these situations, it is vital that the robot has the ability to detect and avoid pitfalls as well as keep on solid ground at all times. In addition, this system should be able to warn the user of potential dangers by using a range of feedback devices such as vibration, sound, and tactile sensation.
In order to do this, a stairways sensor driver must be able to identify the floor plane and the stairs in the space around the robot. This can be accomplished by integrating a laser scanner into the robot’s navigational system. The scanner can then be programmed to recognize the floor plane as a reference and plan routes around it accordingly.
The algorithm used for the staircase detection uses Point Cloud data and the RANSAC method to filter points based on the local surface normals of the points. The resulting set of points is then used to determine the floor plane and the stairs. The algorithm can also detect loose obstacles in the space, such as a wall.