Prahlad Vadakkepat

Evolutionary artificial potential fields and their application in real time robot path planning

A new methodology named Evolutionary Artificial Potential Field (EAPF) is proposed for real-time robot path planning. The artificial potential field method is combined with genetic algorithms, to derive optimal potential field functions. The proposed EAPF approach is capable of navigating robot(s) situated among moving obstacles. Potential field functions for obstacles and goal points are also defined. The potential field functions for obstacles contain tunable parameters. The multi-objective evolutionary algorithm (MOEA) is utilized to identify the optimal potential field functions. Fitness functions such as goal-factor, obstacle-factor, smoothness-factor and minimum-pathlength-factor are developed for the MOEA selection criteria. An algorithm named escape-force is introduced to avoid the local minima associated with EAPF. Moving obstacles and moving goal positions were considered to test the robust performance of the proposed methodology. Simulation results show that the proposed methodology is efficient and robust for robot path planning with non-stationary goals and obstacles.

Multimodal Approach to Human-Face Detection and Tracking

The constructive need for robots to coexist with humans requires human-machine interaction. It is a challenge to operate these robots in such dynamic environments, which requires continuous decision-making and environment-attribute update in real-time. An autonomous robot guide is well suitable in places such as museums, libraries, schools, hospital, etc. This paper addresses a scenario where a robot tracks and follows a human. A neural network is utilized to learn the skin and nonskin colors. The skin-color probability map is utilized for skin classification and morphology-based preprocessing. Heuristic rule is used for face-ratio analysis and Bayesian cost analysis for label classification. A face-detection module, based on a 2D color model in the and YUV color space, is selected over the traditional skin-color model in a 3D color space. A modified continuously adaptive mean shift tracking mechanism in a 1D hue, saturation, and value color space is developed and implemented onto the mobile robot. In addition to the visual cues, the tracking process considers 16 sonar scan and tactile sensor readings from the robot to generate a robust measure of the persons distance from the robot. The robot thus decides an appropriate action, namely, to follow the human subject and perform obstacle avoidance. The proposed approach is orientation invariant under varying lighting conditions and invariant to natural transformations such as translation, rotation, and scaling. Such a multimodal solution is effective for face detection and tracking.

Absolute periodicity and absolute stability of delayed neural networks

Proposes to study the absolute periodicity of delayed neural networks. A neural network is said to be absolutely periodic, if for every activation function in some suitable functional set and every input periodic vector function, a unique periodic solution of the network exists and all other solutions of the network converge exponentially to it. Absolute stability of delayed neural networks is also studied in this paper. Simple and checkable conditions for guaranteeing absolute periodicity and absolute stability are derived. Simulations for absolute periodicity are given.

Fuzzy behavior-based control of mobile robots

An extensive fuzzy behavior-based architecture is proposed for the control of mobile robots in a multiagent environment. The behavior-based architecture decomposes the complex multirobotic system into smaller modules of roles, behaviors and actions. Fuzzy logic is used to implement individual behaviors, to coordinate the various behaviors, to select roles for each robot and, for robot perception, decision-making, and speed control. The architecture is implemented on a team of three soccer robots performing different roles interchangeably. The robot behaviors and roles are designed to be complementary to each other, so that a coherent team of robots exhibiting good collective behavior is obtained.

Interacting MCMC particle filter for tracking maneuvering target

In this paper, a new method, named interacting MCMC particle filter, is proposed to track maneuvering target. The particles are sampled from the target posterior distribution via direct interacting MCMC sampling method, which avoids sample impoverishment and increases the robustness of the algorithm. Moreover, the interacting MCMC particle filter algorithm accelerates the MCMC convergence rate via propagating each particle based on both its history information and the information from other particles.

Improved Particle Filter in Sensor Fusion for Tracking Randomly Moving Object

An improved particle-filter algorithm is proposed to track a randomly moving object. The algorithm is implemented on a mobile robot equipped with a pan-tilt camera and 16 sonar sensors covering 360deg. Initially, the moving object is detected through a sequence of images taken by the stationary pan-tilt camera using the motion-detection algorithm. Then, the particle-filter-based tracking algorithm, which relies on information from multiple sensors, is utilized to track the moving object. The robot vision system and the control system are integrated effectively through the state variable representation. The object size deformation problem is taken care of by a variable particle-object size. When moving randomly, the objects position and velocity vary quickly and are hard to track. This results in serious sample impoverishment (all particles collapse to a single point within a few iterations) in the particle-filter algorithm. A new resampling algorithm is proposed to tackle sample impoverishment. The experimental results with the mobile robot show that the new algorithm can reduce sample impoverishment effectively. The mobile robot continuously follows the object with the help of the pan-tilt camera by keeping the object at the center of the image. The robot is capable of continuously tracking a humans random movement at walking rate

Application of evolutionary artificial potential field in robot soccer system

Evolutionary artificial potential field (EAPF) functions are utilized for mobile robot navigation in a microrobot soccer (MiroSot) environment. In a micro-robot soccer system the robots are monitored using an overhead CCD Camera, making it suitable for real time application of the EAPF functions. The effectiveness of the EAPF functions in real time mobile robot navigation are verified through experimentation. The EAPF functions proposed were tested in different scenarios related to ball tracking and ball kicking, while facing competition from other robots.

HAND POSTURE AND FACE RECOGNITION USING A FUZZY-ROUGH APPROACH

A fuzzy-rough multi cluster (FRMC) classifier for the recognition of hand postures and face is presented in this chapter. Features of the image are extracted using the computational model of the ventral stream of visual cortex. The recognition algorithm translates each quantitative value of the feature into fuzzy sets of linguistic terms using membership functions. The membership functions are generated by the fuzzy partitioning of the feature space into fuzzy equivalence classes, using the feature cluster centers generated by the subtractive clustering technique. A rule base generated from the lower and upper approximations of the fuzzy equivalence classes classifies the images through a voting process. Using Genetic Algorithm (GA), the number of features required for classification is reduced by identifying the predictive image features. The margin of classification, which is a measure of the discriminative power of the classifier, is used to ensure the quality of classification process. The fitness function suggested assists in the feature selection process without compromising on the classification accuracy and margin. The algorithm is tested using two hand posture and three face datasets. The algorithm provides good classification accuracy, at a less computational effort. The selection of relevant features further reduced the computational costs of both feature extraction and classification algorithms, which makes it suitable for real-time applications. The performance of the algorithm is compared with that of Support Vector Machines.

Multi-Agent Systems: A Survey from the Robot-Soccer Perspective

ABSTRACTOne of the most challenging goals in artificial intelligence (AI) is the development of artificial intelligent autonomous agents with human-level performance. The past few years has witnessed a tremendous interest in research and discussions on intelligent agents. With the ever increasing number of robots in an industrial environment, scientists and technologists are often faced with issues on cooperation and coordination among different robots and their self governance in a common work-space. This has led to developments in multi-robot cooperative autonomous systems. With an aim to study issues such as group architecture, resource conflict, origin of cooperation, learning and geometric problems, groups of robots are constructed. The proponents of multi-robot autonomous systems needed a model to validate the theories being proposed and to test their efficacy and efficiency. It is no surprise that they started focussing on robot soccer. Robot soccer makes heavy demands in all the key areas of robot...

Evolution of fuzzy behaviors for multi-robotic system

In a multi-robotic system, robots interact with each other in a dynamically changing environment. The robots need to be intelligent both at the individual and group levels. In this paper, the evolution of a fuzzy behavior-based architecture is discussed. The behavior-based architecture decomposes the complicated interactions of multiple robots into modular behaviors at different complexity levels. The fuzzy logic approach brings in human-like reasoning to the behavior construction, selection and coordination. Various behaviors in the fuzzy behavior-based architecture are evolved by genetic algorithm (GA). At the lowest level of the architecture hierarchy, the evolved fuzzy controllers enhanced the smoothness and accuracy of the primitive robot actions. At a higher level, the individual robot behaviors have become more skillful after the evolution. At the topmost level, the evolved group behaviors have resulted in aggressive competition strategy. The simulation and real-world experimentation on a robot-soccer system justify the effectiveness of the approach.