A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot

Path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot

Optimal Control Algorithm for Stochastic Systems with Parameter Drift

A novel optimal control problem is considered for multiple input multiple output (MIMO) stochastic systems with mixed parameter drift, external disturbance and observation noise. The proposed controller can not only track and identify the drift parameters in finite time but, furthermore, drive the system to move towards the desired trajectory. However, there is a conflict between control and estimation, which makes the analytic solution unattainable in most situations. A dual control algorithm based on weight factor and innovation is, therefore, proposed. First, the innovation is added to the control goal by the appropriate weight and the Kalman filter is introduced to estimate and track the transformed drift parameters. The weight factor is used to adjust the degree of drift parameter estimation in order to achieve a balance between control and estimation. Then, the optimal control is derived by solving the modified optimization problem. In this strategy, the analytic solution of the control law can be obtained. The control law obtained in this paper is optimal because the estimation of drift parameters is integrated into the objective function rather than the suboptimal control law, which includes two parts of control and estimation in other studies. The proposed algorithm can achieve the best compromise between optimization and estatimation. Finally, the effectiveness of the algorithm is verified by numerical experiments in two different cases

Robust Quadratic Optimal Control for Discrete-Time Linear Systems with Non-Stochastic Noises

In this paper, the quadratic optimal control problem is investigated for the discrete-time linear systems with process and measurement noises which belong to specified ellipsoidal sets. As the noises are non-stochastic, the traditional Kalman filtering and Dynamic Bellman Equation are not applicable for the proposed control problem. To obtain the optimal control, we firstly converted the multi-step quadratic global optimal control problem to multiple one-step quadratic local approximate optimal control problems. For each one-step quadratic optimal control problem, considering that the system states are not fully available, the set-membership filtering is applied to estimate the true state feasible set. Then based on robust optimization, a robust state feedback control strategy can be obtained by solving a certain semidefinite programming (SDP) problem. The method can not only achieve the optimal control, but also estimate the system states more accurately. Finally, the simulation results verify the effectiveness of the proposed algorithm

Robust Quadratic Optimal Control for Discrete-Time Linear Systems with Non-Stochastic Noises

In this paper, the quadratic optimal control problem is investigated for the discrete-time linear systems with process and measurement noises which belong to specified ellipsoidal sets. As the noises are non-stochastic, the traditional Kalman filtering and Dynamic Bellman Equation are not applicable for the proposed control problem. To obtain the optimal control, we firstly converted the multi-step quadratic global optimal control problem to multiple one-step quadratic local approximate optimal control problems. For each one-step quadratic optimal control problem, considering that the system states are not fully available, the set-membership filtering is applied to estimate the true state feasible set. Then based on robust optimization, a robust state feedback control strategy can be obtained by solving a certain semidefinite programming (SDP) problem. The method can not only achieve the optimal control, but also estimate the system states more accurately. Finally, the simulation results verify the effectiveness of the proposed algorithm