Environmental Boundary Tracking Control For Mobile Rrobots

Monitoring environmental (e.g. radiation pollution area, poisionous gas spills area) boundaries has been an interesting topic for researchers for recent several years due to it’s scientific and public safety applications. Though many research results have been proposed for environmental boundary monitoring by mobile robots, a new research field, the research on environmental bondary monitoring is still in its infancy.Since the mobile robot can only measure the concentration value on its own position, no knowledge about the environmental concentration topologies (gradient, curvature of the environmental contour lines etc.), multiple environmental concentration measurements are need to estimate the concentration topologies, so as to construct the environmental boundary tracking control for the mobile robot. In this paper, we consider the environmental boundary tracking problem where the objective is to steer a leader-following formation of nonholonomic mobile robots to track the desired environmental boundary. The followers in the formation play the roles of supplying environmental concentration measurements for the leader robot to estimate the concentration topologies. Based on the concentration topologies control laws are designed for the leader to track the desired boundary.Based on the current research, we mainly designed control laws for the followers to track the leader robot in the desired formation and two kinds of environmetal boundary tracking control laws for the leader robot to track the desired environmental boundary, which are stated in the following in detail.(1) The leader-following formation composed of one leader and multiples followers can be decomposed into several 2-robot leader-following formation. In Chapter 3, we consider the 2-robot leader-following formation control problem, where the follower robots can only get the leader’s position and direction angle. Given the leader’s position and direction angle, the follower’s desired trajectory can be determined. So the leader-following formation problem can be changed into a specail trajectory tracking problem for the following robot. In Chapter 3, according to the leader’s position and direction angle, a neural network observer is designed to estimate the leader’s dynamics. Then based on the estimated information by the neural network observer and the multiple sliding surfaces technique, a nonlinear controller is desiged for the follower robot to track the desired trajectory. The efficiency of the neural network observer and the leader-following formation control is illustrated by formal proof and simulation results. (2) Based on the environmental concentration topologies, a robust adaptive neural network control is designed for the (leader) robot to track the desired environmental boundary. Firstly, according to the charactoristics of the environmental concentration function and the desired boundary, two motion rules are defined for the (leader) robot’s velocity in the Cartesian coordinates. One motion rule makes the robot move to the desired environmental boundary, while the other motion rule results in the robot travel along the environmental boundary. Based on the two motion rules, a reference velocity is designated for the robot. Then a robust adaptive neural network is designed for the robot to track the reference velocity, so as to track the desired environmental boundary. In the control law design, a RBF neural network is used to estimate a nonlinear function containing the elements of environmental concentration Hessian matrix. Since the existence of approximation errors in neural network approximation of nonlinear functions and the inherent uncertainties or disturbance in the system model, most neural network-basedtracking control only result in tracking errors’ Uniformly Utimately Bounded. In Chapter 4, the designed robust adaptive controlresults in the environmental boundary tracking errors convergence to zero.(3) Based on environmental concentration topologies information, a nonlinear control law is designed for the (leader) robot to track the desired environmental boundary in Chapter 5. The environmental boundary tracking problem can be seen as: design control law for the mobile robot so that the concentration value at the robot’s position converges to the desired value, the error between the robot’s direction angle and the tangential line angle of the environmental contour lines converges to zero. Firstly, the dynamics of the two values, the environmental concentration and the error between the robot’s direction angle and the tangential line angle of the environmental contour line, along the mobile robot’s trajectory are derived based on the robot’s kinematic model. Then a angular velocity control law is designed for the robot to track the desired environmental boundary in constant linear velocity. Then, based on the robot’s dynamic model, a nonlinear control law is designed for the robot to track the designed angular velocity and the constant linear velocity, so as to track the desired environmental boundary.