This paper discusses the key features of autonomous heterogeneous teams. Autonomy in this sense entails both the ability to overcome unforeseen circumstances and failures as well as the capability of enhancing performance over time. The paper presents a framework that provides a comprehensive solution for handling faults and dynamically configuring the team in response to varying situations, while improving its performance over time, on both individual and team levels, within a modular and expandable architecture. The performance of the framework is studied in a set of simulated foraging scenarios, under conditions of both software and hardware failure.

This article discusses effective mechanisms that enable a group of robots to autonomously generate, adapt, and enhance team behaviors while improving their individual performance simultaneously. Two promising team learning concepts, namely, cooperative learning and advice‐sharing, are integrated to provide a platform that encompasses a comprehensive approach to team‐performance enhancement. These methods were examined in relation to the performance characteristics of standard single‐robot Q‐learning to ascertain whether they retain viable learning characteristics despite the integration of individual learning into team behaviors.

A new technique for Simultaneous Localization and Mapping (SLAM) is introduced. This technique was developed as a real-time, distributed, scalable implementation for heterogeneous mobile robot teams. Update efficiency and performance under variable resources is enhanced using a new strategy called Lazy Belief Propagation. The formulations and algorithms behind the implementation are described and a simulation was used to compare several SLAM algorithms. Results demonstrate an 11% improvement in map coverage in the same amount of time compared to a traditional implementation.

This paper discusses a practical approach to the concurrent synthesis of robot manipulators, which is based on the alternative design methodology of linguistic mechatronics (LM) as well as the utilization of a modular robotic hardware-in-the-loop simulation (RHILS) platform. The RHILS platform involves physical joint modules and the control unit to reduce modeling complexities while taking into account various physical phenomena. The LM methodology simplifies the multi-objective constrained optimization problem into a single-objective unconstrained formulation and also brings subjective notions of design into the scope. The new approach is applied to redesigning kinematic, dynamic and control parameters of an industrial manipulator.

An architecture for robotic hardware-in-the- loop simulation (RHILS) has been proposed as a design and simulation tool for serial-link robot manipulators. This paper evaluates the RHILS platform's capabilities when applied to the simulation of the 5-d.o.f. CRS CataLyst-5 industrial manipulator from Thermo Fisher Scientific Inc. The results demonstrate that the RHILS platform is able to accurately simulate the robot even under extreme operating conditions, and the platform shows significant potential as a design tool for both the robot and its control unit.

This paper details the design and development of a robotic hardware-in-the-loop simulation platform that can be used for rapid-prototyping industrial manipulators. The architecture of the proposed platform has been presented by Martin and Emami (2006). Potential benefits of such a platform include allowing concurrent development of hardware and control system components and providing a reusable platform for reconfigurable manipulators through a generic and modular structure. Some preliminary tests on the platform have also been discussed in the paper

This paper describes a step by step validation procedure of a mobile manipulator simulation. The procedure includes performing basic model simulations, a sensitivity analysis, and a stochastic output analysis. Static, quasi-static, and dynamic experiments performed on a real system and simulated using DADS/sup TM/ are also presented. An algorithm to time the model parameters, to match the output of the simulations to the output of the real system, was successfully implemented. Finally, an optimization procedure on the design parameters of the system is discussed.

This paper presents a fuzzy-logic control structure, containing the fuzzy-logic model of the dynamic system and fuzzy control rules that ensure the stability and robust performance. The robust fuzzy rules are designed based on a generalized formulation of sliding mode control for a class of nonlinear multi-input multi-output systems. The proposed fuzzy-logic control scheme was applied to the trajectory control of a four-degree-of-freedom robot, and compared with the high-gain PID controllers. A superior tracking performance was achieved.

A systemic approach of fuzzy-logic modeling from the system input-output data is applied to modeling dynamics behavior of a four degree-of-freedom robot manipulator. The output of the fuzzy model is compared with the experimental data and with that of an analytical simulation. The simplicity of the fuzzy model and its improved accuracy is significant.

This paper proposes a systematic methodology of fuzzy logic modeling for complex system modeling. It has a unified parameterized reasoning formulation, an improved fuzzy clustering algorithm, and an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces 4 parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. The fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering. Major bottlenecks are addressed and analytical solutions are suggested. We also address the classification process to extend the derived fuzzy partition to the entire output space. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy fine clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples-a nonlinear function and a gas furnace dynamic procedure-are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches.