By making use of the characteristics of ergodicity, randomicity and regularity of chaotic variables and information entropy, a novel chaotic small-world algorithm is presented to improve the optimization performance of the simple small-world algorithm. Compared with the corresponding simple small-world algorithm and the modified genetic algorithm approach, the optimization results of selected complex functions indicate that the proposed chaotic small-world algorithm is characterized by a strong search capability and a quick convergence speed. A study of parameter performance of the chaotic small-world algorithm aids in further improvement of its optimization capability. Additionally, the chaotic small-world algorithm is applied to mobile robot path planning, and the global path is optimized by the chaotic small-world algorithm based on a MAKLINK graph. Finally, experimental results verify the validity of the chaotic smallworld algorithm for robot path planning.

No abstract

Wheeled mobile platform is a common structure of mobile robots and electric vehicles. If the wheels driven by individual motors can be accurately controlled, the mechanical construction will be simplified and the composed movement would be precise. The high accuracy tachometric control is based on precise measurement of each driving wheel’s speed. And the mobile platform introduced in this paper has simple structure with light weight and fast dynamic response. Therefore, the digital control cycle should not be long. When the wheels are traveling at a low speed and the sampling period is short, the measurement error would be great, using the traditional methods. We adopted multi-microprocessor and external circuit as the hardware, and chose equal precision method to measure the rotational speed. The results show that this system has achieved high accuracy measurement with errors within ±1‰, which would secure the latter precise control.

Rapid and high-accuracy guidance line identification and tracking are the keys to ensure AGV’s real-time control performance. Analyzing outdoor environment under different lighting conditions, we proposed a feature extraction method which could guarantee the efficiency and stability of line recognition for different surroundings; analyzing how angular deviation and distance deviation affect AGV posture, we designed a subdivision-control tracking strategy. The results show that the algorithm has achieved effective identification and tracking with an error less than ±5cm.

One optimization decision method is presented in order to solve the problem of how to realize demand response according to RTP in residential electricity. This method aims at minimizing the cost of electricity and the dissatisfaction of the power consumer. Optimization decision model is built based on the classification of residential electricity load and the model is solved by genetic algorithm.The results of an example show that optimal decision-making method can help reduce the cost of electricity and be beneficial to regional power grids’ load shifting. Applying this optimization decision method to AMI can realize Auto-DR according to price signal.