Fuzzy Learning Variable Admittance Control for Human-Robot Cooperation

Abstract-This paper presents a method for variable admittance control in human-robot cooperation tasks, that combines a human-like decision making process and an adaptation algorithm. A Fuzzy Inference System is designed that relies on the measured velocity and the force applied by the operator to modify on-line the damping of the robot admittance, based on expert knowledge for intuitive cooperation. A Fuzzy Model Reference Learning Controller is used to adapt the Fuzzy Inference System according to the minimum jerk trajectory model. To evaluate the performance of the proposed controller a point-to-point cooperation task is conducted with multiple subjects using a KUKA LWR robot

From Pillars to AI Technology-Based Forest Fire Protection Systems

The importance of forest environment in the perspective of the biodiversity as well as from the economic resources which forests enclose, is more than evident. Any threat posed to this critical component of the environment should be identified and attacked through the use of the most efficient available technological means. Early warning and immediate response to a fire event are critical in avoiding great environmental damages. Fire risk assessment, reliable detection and localization of fire as well as motion planning, constitute the most vital ingredients of a fire protection system. In this chapter, we review the evolution of the forest fire protection systems and emphasize on open issues and the improvements that can be achieved using artificial intelligence technology. We start our tour from the pillars which were for a long time period, the only possible method to oversee the forest fires. Then, we will proceed to the exploration of early AI systems and will end-up with nowadays systems that might receive multimodal data from satellites, optical and thermal sensors, smart phones and UAVs and use techniques that cover the spectrum from early signal processing algorithms to latest deep learning-based ones to achieving the ultimate goal

Hybrid kinematic control for rigid body pose stabilization using dual quaternions

In this paper, we address the rigid body pose stabilization problem using dual quaternion formalism. We propose a hybrid control strategy to design a switching control law with hysteresis in such a way that the global asymptotic stability of the closed-loop system is guaranteed and such that the global attractivity of the stabilization pose does not exhibit chattering, a problem that is present in all discontinuous-based feedback controllers. Using numerical simulations, we illustrate the problems that arise from existing results in the literature—as unwinding and chattering—and verify the effectiveness of the proposed controller to solve the robust global pose stability problem

Optimum Docking of an Unmanned Underwater Vehicle for High Dexterity Manipulation

A method for the determination of the optimum docking or hovering position of an Underwater Unmanned Vehicle is proposed for performing a desired intervention task with high dexterity. The optimization problem is formulated taken into account primarily the manipulator’s dexterity in the area of intervention as well as the distance between the current position and the optimal one and the geometric constraints imposed by the environment. A Genetic Algorithm is designed and implemented to search for the best docking position. An underwater scenario with a UUV equipped with a 6 DOF manipulator is examined in order to verify the applicability of the proposed approach.JRC.G.6-Digital Citizen Securit