Modeling humanoid swarm robots with petri nets

Master's thesis in Computer scienceRobots have become a hot topic in today‟s electronic world. There are many definitions for it. One of the definition in Oxford dictionary states “a robot is a machine capable for carrying out a complex series of action automatically especially one programmable by a computer”. This paper deals with a special kind of robot, which is also known as humanoid robot. These robots are replication of human beings with head, torso, arms and legs. A model of human is presented in this paper as discrete event system adapted from “Modeling and simulating motions of human bodies…”[1]. This model consists of sixteen interrelated limbs defined in 3D space, so most limbs/joints are able to make movement in three different angles (α, β and γ). Full details regarding Range of Motion (ROM) of rigid body in forward kinematic is illustrated. Human motions are categorized into two types: stochastic and deterministic motions. Deterministic motions are demonstrated using gait cycle of walking and running of normal adult person. The main focus of this paper is to model and simulate humanoid robot represented as Discrete Event Systems (DES); in Petri Net using GPenSIM and later expand those group of robots to swarm setting. GPenSIM is General Purpose Petri Net simulator [2] developed as toolbox for MATLAB to model and simulated discrete events using Petri net tools. Each joint‟s angle is treated as a separate Petri Net model which is independent from each other and their movement‟s limits are defined by ROM of normal human body. The instructions relating to the motion of joints for simulation are fed through a file to the instructor. These movements of joints are represented by variation of tokens displayed at the end of simulation in a graphical figure. Further, same structure of model is used in swarm of robots. Instead of feeding instructions to individual robots, a central instructor is created. This instructor acts as a master to robots acting as slaves where slaves include some predetermined commands embedded inside them. With central command system, a proper synchronization is achieved among group of robots working as swarm. A normal routine of group dance or simple group sport can be accomplished with calculated instructions on this swarm of robot

Zero-gravity movement studies

The use of computer graphics to simulate the movement of articulated animals and mechanisms has a number of uses ranging over many fields. Human motion simulation systems can be useful in education, medicine, anatomy, physiology, and dance. In biomechanics, computer displays help to understand and analyze performance. Simulations can be used to help understand the effect of external or internal forces. Similarly, zero-gravity simulation systems should provide a means of designing and exploring the capabilities of hypothetical zero-gravity situations before actually carrying out such actions. The advantage of using a simulation of the motion is that one can experiment with variations of a maneuver before attempting to teach it to an individual. The zero-gravity motion simulation problem can be divided into two broad areas: human movement and behavior in zero-gravity, and simulation of articulated mechanisms

Inter-Joint Coordination Deficits Revealed in the Decomposition of Endpoint Jerk During Goal-Directed Arm Movement After Stroke

It is well documented that neurological deficits after stroke can disrupt motor control processes that affect the smoothness of reaching movements. The smoothness of hand trajectories during multi-joint reaching depends on shoulder and elbow joint angular velocities and their successive derivatives as well as on the instantaneous arm configuration and its rate of change. Right-handed survivors of unilateral hemiparetic stroke and neurologically-intact control participants held the handle of a two-joint robot and made horizontal planar reaching movements. We decomposed endpoint jerk into components related to shoulder and elbow joint angular velocity, acceleration, and jerk. We observed an abnormal decomposition pattern in the most severely impaired stroke survivors consistent with deficits of inter-joint coordination. We then used numerical simulations of reaching movements to test whether the specific pattern of inter-joint coordination deficits observed experimentally could be explained by either a general increase in motor noise related to weakness or by an impaired ability to compensate for multi-joint interaction torque. Simulation results suggest that observed deficits in movement smoothness after stroke more likely reflect an impaired ability to compensate for multi-joint interaction torques rather than the mere presence of elevated motor noise

Prototyping environment for robot manipulators

Journal ArticlePrototyping is an important activity in engineering. Prototype development is a good test for checking the viability of a proposed system. Prototypes can also help in determining system parameters, ranges, or in designing better systems. We are proposing a prototyping environment for electro-mechanical systems, and we chosen a 3-link robot manipulator as an example. In Designing a robot manipulator, the interaction between several modules (S/W, VLSI, CAD, CAM, Robotics, and Control) illustrates an interdisciplinary prototyping environment that includes different types of information that are radically different but combined in a coordinated way. This environment will enable optimal and flexible design using reconfigurable links, joints, actuators, and sensors. Such an environment should have the right "mix" of software and hardware components for designing the physical parts and the controllers, and for the algorithmic control for the robot modules (kinematics, inverse kinematics, dynamics, trajectory planning, analog control and computer (digital) control). Specifying object-based communications and catalog mechanisms between the software modules, controllers, physical parts, CAD designs, and actuator and sensor components is a necessary step in the prototyping activities. In this report a framework for flexible prototyping environment for robot manipulators is proposed along with the required sub-systems and interfaces between the different components of this environment

Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop

This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artifical life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however resolve that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

The dynamic neural field approach to cognitive robotics

This tutorial presents an architecture for autonomous robots to generate behavior in joint action tasks. To efficiently interact with another agent in solving a mutual task, a robot should be endowed with cognitive skills such as memory, decision making, action understanding and prediction. The proposed architecture is strongly inspired by our current understanding of the processing principles and the neuronal circuitr underlying these functionalities in the primate brain. As a mathematical framework, we use a coupled system of dynamic neural fields, each representing the basic functionality of neuronal populations in different brain areas. It implements goal-directed behavior in joint action as a continuous process that builds on the interpretation of observed movements in terms of the partner’s action goal. We validate the architecture in two experimental paradigms: (1) a joint search task; (2) a reproduction of an observed or inferred end state of a grasping–placing sequence. We also review some of the mathematical results about dynamic neural fields that are important for the implementation work.European Commission fp6-IST2, project no. 00374