The platforms are improved from our middle size robots, the hardware and software are redesigned according to the demands of RoboCup@Home league. Our platform consists of a four wheel mobile platform for moving, a pair of manipulators for object grasping and a lift platform to localize the position of manipulators. A Microsoft Kinect cameras and a UTM-30LX laser scanner are selected as the sensors. The mobile platform size is 0.5m x 0.5m in length and width. The distance between two Shoulders is 0.6m. The minimum height of our robot is 1.2m and its maximum height can reach 1.6m by driving the lift platform. So, the minimum size of our robot is 0.5m x 1.20m in width and height, and its maximum size is 0.6m x 1.60m in width and height.
The mobile platform is shown in the bottom of figure 1. The mobile base has four driven omni-wheels which are uniformly distributed on the base. Four maxon RE40 motors are selected to drive the omni-wheels. A synchronous belt transmission is inserted to absorb the shock noise between the wheel and the ground. The designed entity relationship diagrams are shown in figure 2. Our design idea is that the service robot mobile platform should be conveniently moving in a domestic envi-ronment, and should be accurate, robust to the input commands. The reason is that reliable motion of the service robot is the backbone of almost all the robot’s behaviors.
Object manipulation is a basic function of the domestic service robot, so the manipulator is necessary equipment for the robot. The 6D industry manipulator need complex inverse kinematics programming, and it is too heavy to apply for the domestic service robot. We design a new 2D manipulator which has a shoulder joint and an elbow joint. The joints are driven by two RX-64 servos. Obviously, only using a 2D manipulator, the endeffector cannot reach the entire workspace. We design a drive system which can lift the shoulder joint to appropriate position in the vertical direction and the mobile base can locate the position and direction of the shoulder joint in horizontal plane. The designed entity relationship diagrams of the manipulator and the ballscrew lifting platform are shown in figure 3.
We have used two sensors on the robot platform to perceive its environment: A Hokuyo UTM-30LX Laser Range finder has been placed on the mobile base for mapping, location, navigation and obstacle avoidance. A 3D Ranging Camera: Microsoft Kinect has been installed on the top of the robot platform for scene perception.The positions of the sensors are shown on figure 1.
The software of our robot platform is designed by C++ based on the Microsoft VS2010. The control architecture is shown in figure 4.
The control software is running on a laptop computer, the sensors input and control output for drive units are all through USB ports. The tasks of the RoboCup@Home are divided into a few subtasks, and every subtask is realized by a functional module.
The SLAM module is shown in figure 5. Only the data of Laser Range finder is used in our SLAM approach, and the odometry is not needed. Scan matching is performed between two laser scans to determine the relative positions from which the scans were obtained. We have implemented the Real-Time Correlative Scan Matching algorithm proposed by Edwin B. Olson. It is robust to initialization error and can find the global maximum of the cost function of scan match. The effect of this method is shown in figure 6, a comparison of the actual lobby environment of our lab to what is mapped by the robot is shown. The bright points represent free region, the black points represent occupy region, and the grey points represent unknown region. There are three tables in the lobby, and the footprints of the table’s legs are clear shown in the map. The map is the base of robot location and navigation.
We present a path-planning algorithm for mobile robot platform based on Cellular Automata(CA) and artificial potential field and the algorithm have been implemented by a 4-layer cellular automata model. Firstly, an expanded occupancy grid map is constructed so that the mobile robot can be simplified as a point in the planning algorithm. Secondly, a digital obstacles artificial potential field map is obtained to include the local influence of the obstacles. Then, a distance propagation map is generated by a CA model. Finally, the optimal collision-free path from start point to goal is extracted by following the minimum valley of the potential hyper-surface. The simulation result is shown as figure 7. The result shows that the optimal collision-free paths can be found by the proposed algorithm. The optimal paths are smooth enough and have larger safety distance from the obstacles. So the optimal paths are convenient to track by our mobile robot platform. Our navigation method can make our robot to track the optimal paths, and in the same time, perform local obstacle avoidance when there are moving objects.