2. Path planning for robots with existence of moving obstacles

In this chapter, we present an approach of the ground mobile vehicle or robot trajectory planning under the existence of moving obstacles by using improved artificial potential field method. The potential field intensity and strength instead of force vectors was adopted in the planning. Considering the speed effect of mobile obstacles and mobile robot, the velocity information was introduced into potential field function and an "added potential field" was also applied to guide the mobile robot to be free of collision with local obstacles. Based on the new method, all the potential field intensity was considered and summed by algebraic style, then the genetic trust region algorithm was used to search the minimum sum points of potential field intensity within the motion space and scope, which the mobile robot can reach target during a sampling period, and the global optimization trajectory was consisted of all the minimum points. Simulation and experiment results show that better results of path planning for a mobile robot in a complex environment with the existence of moving obstacles can be achieved using this new approach.

The problem of mobile robot path planning under a dynamic environment in mobile robot domain is hot and difficult. Its task is to find a path, which is from the initial state to goal state. Currently, common path planning methods are artificial potential field method, grid method, neural network method, chaos genetic algorithm, aand so on [5, 6]. Artificial potential field method in the algorithm of path planning is relatively mature and efficient, because of the simplicity of the mathematical calculation, it is widely used. However, there are the issues of local minimum point and destination unreachable in traditional artificial potential field method. There are a variety of ways to escape from the local minimum point, such as random fleeing method, heuristic search, walking along the wall, Tangent bug's method, and so on [7, 8], These methods need to apply an additional control force to the robot, which could not fundamentally solve problems. An improved artificial potential field method based on genetic algorithm was proposed in [8] to solve the problem of goal unreachable and local minimum, but the convergent speed of the algorithm still needs to be improved.

In this chapter, aimed at the shortcoming of traditional artificial potential field method, an improved artificial potential field applied in a dynamic environment is proposed. Firstly, an improved artificial potential field model is established; then the genetic trust region algorithm is applied to solve the sub-target point problem of the improved artificial potential field model and then the optimal path is planned.

## 2.1. Artificial potential field model

colony algorithm, genetic algorithm, and artificial potential field algorithm, dealing with some motion planning have its unique superiority. In addition, the algorithm based on potential field or heuristic function, such as A\*, D\*, and artificial potential field method, in addressing the problem of planning can meet the requirements of real-time and the optimality. This chapter introduces two kinds of path planning algorithm of the robot, including mobile robots

In this chapter, we present an approach of the ground mobile vehicle or robot trajectory planning under the existence of moving obstacles by using improved artificial potential field method. The potential field intensity and strength instead of force vectors was adopted in the planning. Considering the speed effect of mobile obstacles and mobile robot, the velocity information was introduced into potential field function and an "added potential field" was also applied to guide the mobile robot to be free of collision with local obstacles. Based on the new method, all the potential field intensity was considered and summed by algebraic style, then the genetic trust region algorithm was used to search the minimum sum points of potential field intensity within the motion space and scope, which the mobile robot can reach target during a sampling period, and the global optimization trajectory was consisted of all the minimum points. Simulation and experiment results show that better results of path planning for a mobile robot in a complex environment with the existence of moving obstacles can be

The problem of mobile robot path planning under a dynamic environment in mobile robot domain is hot and difficult. Its task is to find a path, which is from the initial state to goal state. Currently, common path planning methods are artificial potential field method, grid method, neural network method, chaos genetic algorithm, aand so on [5, 6]. Artificial potential field method in the algorithm of path planning is relatively mature and efficient, because of the simplicity of the mathematical calculation, it is widely used. However, there are the issues of local minimum point and destination unreachable in traditional artificial potential field method. There are a variety of ways to escape from the local minimum point, such as random fleeing method, heuristic search, walking along the wall, Tangent bug's method, and so on [7, 8], These methods need to apply an additional control force to the robot, which could not fundamentally solve problems. An improved artificial potential field method based on genetic algorithm was proposed in [8] to solve the problem of goal unreachable and local minimum,

In this chapter, aimed at the shortcoming of traditional artificial potential field method, an improved artificial potential field applied in a dynamic environment is proposed. Firstly, an improved artificial potential field model is established; then the genetic trust region algorithm is applied to solve the sub-target point problem of the improved artificial potential field model

but the convergent speed of the algorithm still needs to be improved.

2. Path planning for robots with existence of moving obstacles

and unmanned ground vehicles (UGV).

146 Advanced Path Planning for Mobile Entities

achieved using this new approach.

and then the optimal path is planned.
