Abstract
This research aims to improve autonomous navigation of coal mine rescue and detection robot, eliminate the danger for rescuers, and enhance the security of rescue work. The concept of model predictive control is introduced into path planning of rescue and detection robot in this paper. Sampling-Based Model Predictive Control (SBMPC) algorithm is proposed basing on the construction of cost function and predictive kinematics model. Firstly, input sampling is conducted in control variable space of robot motion in order to generate candidate path planning solutions. Then, robot attitude and position in future time, which are regarded as output variables of robot motion, can be calculated through predictive kinematics model and input sampling data. The optimum solution of path planning is obtained from candidate solutions through continuous moving optimization of the defined cost function. The effects of the three sampling methods (viz., uniform sampling, Halton’s sampling, and CVT sampling) on path planning performance are compared in simulations. Statistical analysis demonstrates that CVT sampling has the most uniform coverage in two-dimensional plane when sample amount is the same for three methods. Simulation results show that SBMPC algorithm is effective and feasible to plan a secure route for rescue and detection robot under complex environment.
Highlights
As an important tool for processing coal mine accidents, rescue and detection robot can substitute rescuers to enter the accident area for implementation of detecting and rescuing
Target approaches 1 when fitting performance is improved. e plane generated by Centroidal Voronoi Tessellation (CVT) sampling points is closest to the original plane, and this demonstrates that CVT sampling points can accurately reflect the shape information of a measured plane. erefore, the performance of Sampling-Based Model Predictive Control (SBMPC) algorithm is more ideal when the control input variable is sampled through CVT method
Path planning principle of rescue and detection robot based on SBMPC is mainly analyzed in this paper
Summary
As an important tool for processing coal mine accidents, rescue and detection robot can substitute rescuers to enter the accident area for implementation of detecting and rescuing. In the process of robot exploring along its desired path, local planning is generally implemented according to real-time information from sensor feedback, and motion states are often adjusted for realization of realtime obstacle avoidance. There are three main methods aiming at the problem of path planning for mobile robot with multidegree of freedom, namely, Probabilistic Roadmap Methods (PRM) algorithm [3], Rapidly Exploring Random Tree (RRT) algorithm [4], and Vector Field (VF) algorithm. In [14], a new method called laser simulator (LS) is proposed in order to solve the path planning problem of a nonholonomic three-wheeled mobile robot (WMR). Path planning is implemented through Sampling-Based Model Predictive Control (SBMPC) method combined with nonlinear kinematics model of rescue and detection robot. Different from traditional numerical optimization, SBMPC applies the method of objective-oriented optimization which is widely used in robotics and artificial intelligence field. e process of path planning through SBMPC algorithm is deduced in this paper, and the selection of sampling methods is analyzed from the viewpoint of effect on planning performance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
