Cai Zi-xing

Fault Diagnosis and Fault Tolerant Control for Wheeled Mobile Robots under Unknown Environments: A Survey

Fault detection and diagnosis (FDD) and fault tolerant control (FTC) are increasingly important for wheeled mobile robots (WMRs), especially those in unknown environments such as planetary exploration. Due to the importance of reliability and safe operation of WMRs, this paper presents a survey of state-of-the-art in FDD & FTC of WMRs under unknown environments. Firstly, we briefly introduce main components, typical kinematics models and fault models of WMRs and error models of inertial navigation sensors. Secondly, we discuss main approaches for FDD/FTC of WMRs, including multiple model based approach, particle filter based approach, sensor fusion based approach, layered fault tolerant architecture and so on. At last, the main challenges, difficulties and some future trends for the field are offered.

An adaptive genetic algorithm with diversity-guided mutation and its global convergence property

An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and genetic algorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of adaptive genetic algorithm with adaptive probabilities and genetic algorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten.

Premature convergence in genetic algorithm: analysis and prevention based on chaos operator

This paper proposes a novel genetic algorithm containing chaos operator based on the analysis of population diversity and premature convergence within the framework of Markov chain. This algorithm increases the population size dynamically so as to restore the population diversity and prevent premature convergence effectively. Its validity and superiority are illustrated by two applications.

Global path planning approach based on ant colony optimization algorithm

Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted, the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.

Distributed data acquisition unit based on GPS and ZigBee for electromagnetic exploration

The design and implementation of the distributed data acquisition unit (DDAU), which is based on GPS synchronization and ZigBee, are described. The DDAU is used in three-dimensional electromagnetic exploration targeted for oil and gas (hydrocarbon) detection. It is composed of data acquisition and DSP module, embedded control module, GPS sync and timing module, and power supply module. The data acquisition and DSP module, which owns 4 signal channels, amplifies, converts, and processes weak signals from electric field and magnetic field sensors. The embedded control module, which includes ZigBee OEM board, temperature sensor, 10M bps Ethernet, 4 UARTs, 4 SPIs, 2 SSCs, 8 GB NAND flash and 8 MB NOR flash, is based on AT91RM9200 and Linux 2.6. The GPS sync and timing module, which ensures precise synchronized data acquisition in field, generates sync signal and precise clock for A/D convertor and digital signal processing. A large-scale distributed data acquisition system can be built by combining large amount of DDAUs to a wireless sensor network based on ZigBee. The testing result showed that the RMS noise of each analog channel was less than 0.63 uV, and the THD is less than −111 dB.

Artificial Bee Colony Programming Made Faster

The artificial bee colony (ABC) algorithm is a stochastic, population-based evolutionary method that can be applied to a wide range of problems, including global optimization. The paper proposes a variation on the traditional ABC algorithm, called the artificial bee colony programming, or ABCP, employing randomized distribution, bit hyper-mutation and a novel crossover operator to significantly improve the performance of the original algorithm. Application of the new ABC algorithm on fifteen benchmark optimization problems shows a marked improvement in performance over the traditional ABC.

Non-smooth environment modeling and global path planning for mobile robots

An Approximate Voronoi Boundary Network is constructed as the environmental model by way of enlarging the obstacle raster. The connectivity of the path network under complex environment is ensured through building the second order Approximate Voronoi Boundary Network after adding virtual obstacles at joint-close grids. This method embodies the network structure of the free area of environment with less nodes, so the complexity of path planning problem is reduced largely. An optimized path for mobile robot under complex environment is obtained through the Genetic Algorithm based on the elitist rule and re-optimized by using the path-tightening method. Since the elitist one has the only authority of crossover, the management of one group becomes simple, which makes for obtaining the optimized path quickly. The Approximate Voronoi Boundary Network has a good tolerance to the imprecise a priori information and the noises of sensors under complex environment. Especially it is robust in dealing with the local or partial changes, so a small quantity of dynamic obstacles is difficult to alter the overall character of its connectivity, which means that it can also be adopted in dynamic environment by fusing the local path planning.

Notice of Retraction Traffic lights recognition based on morphology filtering and statistical classification

Detection and recognition of the traffic lights are key processes for path planning of the intelligent vehicle. In this research, a novel method is introduced to recognize the traffic lights in urban environment. Firstly, an original image is converted to a binary image by the top-hat transform and threshold segmentation to obtain the brighter regions. Then the candidate regions without satisfying condition are removed by methods of the morphology and geometry feature filtering. Furthermore, a novel recognition method is carried out based on statistical analysis with amount of traffic lights image samples. It utilizes the color feature extracted by the Hue component in the HSV color space for classifying the types of traffic lights. Amount of experiments indicate that the novel algorithm is better adapted to the complex weather conditions, and the rate of recognition is higher than 97%, as well as the time performance could achieve the requirement of real-time processing.

Evolutionary Computation Approach to Decentralized Multi-robot Task Allocation

The problem of allocating exploration tasks to a team of mobile robots was addressed in this paper. Each task consists of a site location that needs to be explored by a robot. The objective of the allocation is to minimize the maximum path cost of the robots. Auction-based methods are efficient for decentralized mobile robots to allocate tasks. However, the quality of allocation cannot be guaranteed. This paper presents a decentralized allocation algorithm which combines a sequential single-task auction and task transfer among the robots. After all of the tasks are auctioned off, the robots of the same sub-team transfer tasks to improve the quality of allocation. In order to increase the efficiency of task transferring, the tasks allocated to the sub-team are clustered using an orthogonal genetic algorithm. Each robot determines which tasks should be transferred, and to which robots the tasks should be transferred according to the clusters. The validity of the proposed algorithm was verified with some benchmarks of vehicle routing problem and traveling salesperson problem. The results showed that the proposed algorithm decreased the robot path costs 40% more than that of a well-known auction-based algorithm in most cases.

A novel genetic algorithm preventing premature convergence by chaos operator

An improved genetic algorithm (GA) is proposed based on the analysis of population diversity within the framework of Markov chain. The chaos operator to combat premature convergence concerning two goals of maintaining diversity in the population and sustaining the convergence capacity of the GA is introduced. In the CHaos Genetic Algorithm (CHGA), the population is recycled dynamically whereas the most highly fit chromosome is intact so as to restore diversity and reserve the best schemata which may belong to the optimal solution. The characters of chaos as well as advanced operators and parameter settings can improve both exploration and exploitation capacities of the algorithm. The results of multimodal function optimization show that CHGA performs simple genetic algorithms and effectively alleviates the problem of premature convergence.