The control of complex systems poses new challenges that fall beyond the traditional methods of control theory. One of these challenges is given by the need to control, coordinate and synchronize the operation of several interacting submodules within a system. The desired objectives are no longer captured by usual control specifications such as stabilization or output regulation. Instead, we consider specifications given by linear temporal logic (LTL) formulas. We show that existence of controllers for discrete-time controllable linear systems and LTL specifications can be decided and that such controllers can be effectively computed. The closed-loop system is of hybrid nature, combining the original continuous dynamics with the automatically synthesized switching logic required to enforce the specification

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Linear Time Logic Control of Discrete-Time Linear Systems

During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degeneracy and impoverishment. We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices. These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization), machine learning approaches (e.g., clustering, splitting and merging) and their hybrids, forming a coherent standpoint to enhance the particle filter. The working mechanism, interrelationship, pros and cons of these approaches are provided. In addition, approaches that are effective for dealing with high-dimensionality are reviewed. While improving the filter performance in terms of accuracy, robustness and convergence, it is noted that advanced techniques employed in PF often causes additional computational requirement that will in turn sacrifice improvement obtained in real life filtering. This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters.

Review: Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches

This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.

Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model

We present a head-mounted, stereo-vision based navigational assistance device for the visually impaired. The head-mounted design enables our subjects to stand and scan the scene for integrating wide-field information, compared to shoulder or waist-mounted designs in literature which require body rotations. In order to extract and maintain orientation information for creating a sense of egocentricity in blind users, we incorporate visual odometry and feature based metric-topological SLAM into our system. Using camera pose estimates with dense 3D data obtained from stereo triangulation, we build a vicinity map of the user's environment. On this map, we perform 3D traversability analysis to steer subjects away from obstacles in the path. A tactile interface consisting of microvibration motors provides cues for taking evasive action, as determined by our vision processing algorithms. We report experimental results of our system (running at 10 Hz) and conduct mobility tests with blindfolded subjects to demonstrate the usefulness of our approach over conventional navigational aids like the white cane.

Robot vision for the visually impaired

A logical data model is defined using a data-modeling framework. The data-modeling framework enables a user to define the logical data model using a series of graphical user interfaces (GUI) or an application-programming interface (API). The data-modeling framework dynamically translates the logical data model into a corresponding physical data model. The logical data model can be changed using the GUI or API and the physical data model is automatically adjusted to accommodate the changes to the logical data model. The logical data model extends capabilities of the physical data model and automatically allows these capabilities to be available to software developers using an application framework integrated with the data-modeling framework. The application framework enables the developer to configure various application features and data management operations using GUI or API. The application framework is tightly coupled with the data-modeling framework such that changes in the logical data model is automatically reflected in the applications.

Logical data modeling and integrated application framework

The military forces always tried to use new gadgets and weapons for reducing the risk of their casualties and to defeat their enemies. With the development of sophisticated technology, it mostly relies on the high tech weapons or machinery being used. Robotics is one of the hot fields of modern age in which the nations are concentrating upon for military purposes in the state of war and peace. They have been in use for some time for demining and rescue operations but now they are under research for combat or spy missions. Today's modern military forces are using different kinds of robots for different applications ranging from mine detection, surveillance, logistics and rescue operations. In the future they will be used for reconnaissance and surveillance, logistics and support, communications infrastructure, forward-deployed offensive operations, and as tactical decoys to conceal maneuver by manned assets. In order to make robots for the unpredicted cluttered environment of the battlefield, research on different aspects of robots is under investigation in laboratories to be able to do its job autonomously, as efficiently as a human operated machine can do. Latest techniques are being investigated to have advanced and intelligent robots for different operations. This paper presents different kinds of robotic technologies being used in all the three main forces, Navy, Army and Air. Some of the robots discussed are also being used in the wars of Afghanistan and Iraq, also, the robots that are under investigation in laboratories for future military operations. These robots are under investigation for autonomous and cooperative environment. We focus our attention on the uses of robots in war and peace as well as their impact on society.

Military robots - a glimpse from today and tomorrow

This paper presented a knowledge based genetic algorithm.This algorithm used the specialized genetic operator and adjusted adaptively the parameter.In addition,it incorporated the domain knowledge into its initial population.This method increases greatly the efficiency of the algorithm and overcomes the problem of premature convergence of the simple genetic algorithm.Experimental results show the feasibility and effectiveness of the algorithm in path planning for mobile robot.

A Knowledge Based Genetic Algorithm for Path Planning of a Mobile Robot

An adaptive Monte Carlo localization algorithm based on coevolution mechanism of ecological species is proposed. Samples are clustered into species, each of which represents a hypothesis of the rob...

https://journals.sagepub.com/doi/pdf/10.5772/5634

Coevolution Based Adaptive Monte Carlo Localization (CEAMCL)

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Research on High Speed Evader vs. Multi Lower Speed Pursuers in Multi Pursuit-evasion Games

Self-localization, a basic problem in mobile robot systems, can be divided into two subproblems: pose tracking and global localization. In pose tracking, the initial robot pose is known, and localization seeks to identify small, incremental errors in a robot’s odometry (Leonard & Durrant-Whyte, 1991). In global localization, however the robot is required to estimate its pose by local and incomplete observed information under the condition of uncertain initial pose. Global localization is a more challenging problem. Only most recently, several approaches based on probabilistic theory are proposed for global localization, including grid-based approaches (Burgard et al., 1996), topological approaches (Kaelbling et al., 1996) (Simmons & Koenig, 1995), Monte Carlo localization (Dellaert et al., 1999) and multi-hypothesis tracking (Jensfelt & Kristensen, 2001) (Roumeliotis & Bekey, 2000). By representing probability densities with sets of samples and using the sequential Monte Carlo importance sampling (Andrieu & Doucet, 2002), Monte Carlo localization (MCL) can represent non-linear and non-Gaussian models well and focus the computational resources on regions with high likelihood. So MCL has attracted wide attention and has been applied in many real robot systems. But traditional MCL has some shortcomings. Since samples are actually drawn from a proposal density, if the observation density moves into one of the tails of the proposal density, most of the samples’ non-normalized importance factors will be small. In this case, a large sample size is needed to represent the true posterior density to ensure stable and precise localization. Another problem is that samples often too quickly converge to a single, high likelihood pose. This might be undesirable in the case of localization in symmetric environments, where multiple distinct hypotheses have to be tracked for extended periods of time. How to get higher localization precision, to improve efficiency and to prevent premature convergence of MCL are the key concerns of the researchers. To make the samples represent the posterior density better, Thrun et al. proposed mixtureMCL (Thrun et al., 2001), but it needs much additional computation in the sampling process. To improve the efficiency of MCL, methods adjusting sample size adaptively over time are proposed (Fox, 2003) (Koller & Fratkina, 1998), but they increase the probability of premature convergence. Although clustered particle filters are applied to solve premature convergence (Milstein et al., 2002), the method loses the advantage of focusing the computational resources on regions with high likelihood because it maintains the same sample size for all clusters. In this paper, a new version of MCL is proposed to overcome those limitations. Samples are clustered into groups which are also called species. A coevolutionary model derived from competition of ecological species is introduced to

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Hong Bingrong

Papers

Military robots - a glimpse from today and tomorrow

A Knowledge Based Genetic Algorithm for Path Planning of a Mobile Robot

Coevolution Based Adaptive Monte Carlo Localization (CEAMCL)

Research on High Speed Evader vs. Multi Lower Speed Pursuers in Multi Pursuit-evasion Games

Coevolution Based Adaptive Monte Carlo Localization