Research On Adaptive Obstacle Recognition And Path Planning Of Exploration Vehicle

Autonomous have many valuable attributes that can benefit human beings in all fields of modern life. The exploitation of space&ocean provides a huge market for robotics. The functions of autonomous vehicles include reconnaissance, surveillance, target acquisition and so on. Several important Autonomous Exploration Vehicles technologies are discussed in these topics which include: Path Planning, Obstacle Avoidance, Perception Technologies, Control System Architecture and etc. All the research work in this paper are not only discussed theoretically autonomous about the obstacle avoidance but also performed with the intelligent four-wheeled vehicle"EV-II".The experiments'results of the Explorer under different conditions are presented. The main content and achievements are as follows:(1)The present state and the feature of the wheeled robot in the world are summarized.The Perception Technologies, the Multi-sensor data fusion, and the path planning technology are integrated systematically and comprehensive.(2)Exploration vehicle body, driven system, power system and control system are designed. The"EV-II"body is made. The open controller based on DSP and PC104 is built. According to request of the actual control, the appropriate operating system is selected. Based on technical indicators the required power to drive system is computed. According to dynamic factors, motors are selected and adjusted. The embedded hardware platform of the driven system is set up. According to the control system and power system, the power supply system of the driven system is designed.(3)The hardware of obstacle recognition is designed.The data fusion algorithm is supposed.In order to ensure the travling safety of the exploration vehicle on the ground, the advantages and disadvantages of a variety of sensors are compared, the circuit of multi- ultrasonics which to obtain distance information is designed. The communication process between the distance information and PC104 are given. The visual-aided navigation is adopted. A new algorithm based on fuzzy close-degree of data fusion is proposed. So the navigation in environmental measurements and data processing time are shorten. Data collection speed and accuracy is improved.(4)Based on particle swarm optimization algorithm, the visualization global path planning is studied.Aimming to the exploration vehicle's path optimization problems in the known static environment, equal portions France is used to carry out environmental modeling. Based on the particle swarm optimization algorithm, the repulsion field function of artificial potential field method is introducted. a new path planning approach based on artificial potential field (APF) and particleswarm optimization (PSO) is presented. And the learning factors are adjusted automatically on-line according to the global optimal solution and local optimal solution. The first step is to make a danger degree map(DDM) based on the repulsive force of obstacles in the environment. Then the PSO whose fitness function is the weighted sum of the path length and the path danger degree is introduced to get a global optimized path. The method has a simple model and a rapid convergence which can meet the safe and real-time demands of robot navigation. The feasibility and effectiveness are proved by the simulation results.(5)In the dynamic environment, the problem of exploration vehicle dynamic path planning is difficult to solve。Then a mathematical model of dynamic environment is proposed based on velocity obstacle and the concept of risk degree for collision, also a method of path planning with improved fuzzy neural network is given. The input / output of the controller are considered into the precise directly. So the improved algorithm includes only two elements: pattern matching and weighted average, thus the tedious process of fuzzication and precision are removed. The concept of patterns and pattern matching are introducted. Patterns includes input mode and rules mode, the norm is used to express the matching degree between them. According to the matching degree, a weighted average algorithm is used to determine the output. The Simulation is carried in the environment of removed obstacles and encountered obstacles. The result shows that method is valid. The exploration vehicle could avoid the complex and dynamic obstacles, walk towards the target point using optimization strategy, and could not fall into the trap.(6)By applying the above results of the study synthetically,the controller based path planning system of exploration vehicle is designed and implemented. A large number of tests on the exploration vehicle platform is finished. The feasibility of the above methods and algorithms'reasonablity are verified by the tests.