Edward Y. L. Gu

Trust method for multi-agent consensus

The consensus problem in multi-agent systems often assumes that all agents are equally trustworthy to seek agreement. But for multi-agent military applications - particularly those that deal with sensor fusion or multi-robot formation control - this assumption may create the potential for compromised network security or poor cooperative performance. As such, we present a trust-based solution for the discrete-time multi-agent consensus problem and prove its asymptotic convergence in strongly connected digraphs. The novelty of the paper is a new trust algorithm called RoboTrust, which is used to calculate trustworthiness in agents using observations and statistical inferences from various historical perspectives. The performance of RoboTrust is evaluated within the trust-based consensus protocol under different conditions of tolerance and confirmation.

Trust dynamics in multi-agent coalition formation

We present a rigorous treatment of coalition formation based on trust interactions in multi-agent systems. Current literature on trust in multi-agent systems primarily deals with trust models and protocols of interaction in noncooperative scenarios. Here, we use cooperative game theory as the underlying mathematical framework to study the trust dynamics between agents as a result of their trust synergy and trust liability in cooperative coalitions. We rigorously justify the behaviors of agents for different classes of games, and discuss ways to exploit the formal properties of these games for specific applications, such as unmanned cooperative control.

Trust-based coalition formation in multi-agent systems

In this paper, we provide a framework to study trust-based coalition formation in multi-agent systems using cooperative game theory as the underlying mathematical framework. We describe how to study trust dynamics between agents as a result of their trust synergy and trust liability in cooperative coalitions. We also rigorously justify the behaviors of agents for different classes of games and discuss how to exploit the formal properties of these games for cooperative control in an unmanned military vehicle convoy.

Modelling of human-vehicle dynamic interactions and control of vehicle active systems

This paper proposes a cascaded system model to represent dynamic interactions between human driver and vehicle. The human dynamics is the top subsystem in the cascaded model. Through a backstepping control design procedure, an overall control scheme is developed to guarantee the entire dynamic system stability. The proposed methodology can be directly applied to model and control many vehicle active systems, such as the active suspension and active restraint systems that interact with the human driver to achieve better safety, comfort and ride quality. Using a complete digital human model, an advanced simulation study is performed to visualise the realistic dynamic motion during a car frontal collision and verify the developed control scheme for the vehicle active restraint system.

Stable structures of coalitions in competitive and altruistic military teams

In heterogeneous battlefield teams, the balance between team and individual objectives forms the basis for the internal topological structure of teams. The stability of team structure is studied by presenting a graphical coalitional game (GCG) with Positional Advantage (PA). PA is Shapley value strengthened by the Axioms of value. The notion of team and individual objectives is studied by defining altruistic and competitive contribution made by an individual; altruistic and competitive contributions made by an agent are components of its total or marginal contribution. Moreover, the paper examines dynamic team effects by defining three online sequential decision games based on marginal, competitive and altruistic contributions of the individuals towards team. The stable graphs under these sequential decision games are studied and found to be structurally connected, complete, or tree respectively.

Digital Human Modeling: Dynamics and Interactive Control

Dynamic modeling, formulation and algorithm development for robotic systems have been investigated and discussed in fair details in Chapter 7. The theories and approaches can now be borrowed to benefit for digital human dynamic modeling and computations. The only difference is the modeling scale between a robot and a digital human.

Introduction to Robotics and Digital Human Modeling

Robotics research and technology development have been on the road to grow and advance for almost half a century. The history of expedition can be divided into three major periods: the early era, the middle age and the recent years.

Robotic Kinematics and Statics

In order to model an open serial-chain robot arm kinematically, we need not only to represent a 6 d.o.f. rigid motion in Cartesian space for the purpose of task/path description, but also to determine a complete transformation along the entire serial-chain robot body in joint space for the purpose of joint values determination. During the 1950’s, Denavit and Hartenberg had independently developed a wonderful procedure to uniquely determine all the joint parameters plus variables for an open serial-chain robotic manipulator. Today, almost everyone recognizes that the D-H procedure is the best approach to uniquely modeling robotic kinematics, which is commonly called the D-H Convention [1, 2, 4].

Digital Mock-Up and 3D Animation for Robot Arms

In order to draw a multi-joint robot arm, the first thing that we must do is to know how to create surfaces of the 3D basic parts in MATLAB TM . The 3D basic parts may include but are not limited to a cylinder and its variations, a sphere or an ellipsoid, a rectangular block, and a torus. Since in MATLAB TM , each 3D part drawing is always initiated at the base coordinate frame, we need to be not only aware of how to create it, but also familiar of its data structure in order to modify it and/or send it to a desired destination (both position and orientation). The detailed instructions, procedures and examples can be found either in the MATLAB TM online help manual, or refer to the literature [1]-[4]. Let us here start with a surface of cylinder drawing.

Digital Human Modeling: 3D Mock-Up and Motion Generation

In Chapter 6, we have extensively studied how to draw a robot arm in MATLAB TM and how to plan a motion and then animate it in a computer. To create a digital mannequin, the major procedures of generating every part and assembling them together are nearly the same as those for a robot drawing in principle. However, since a digital human consists of much more joints and segments along with more complex body surfaces, it will definitely be a more challenging job than creating a robot arm.