The memristive artificial neuron high level architecture for biologically inspired robotic systems

© 2017 IEEE. In this paper we propose a new hardware architecture for the implementation of an artificial neuron based on organic memristive elements and operational amplifiers. This architecture is proposed as a possible solution for the integration and deployment of the cluster based bio- realistic simulation of a mammalian brain into a robotic system. Originally, this simulation has been developed through a neuro-biologically inspired cognitive architecture (NeuCogAr) re-implementing basic emotional states or affects in a computational system. This way, the dopamine, serotonin and noradrenaline pathways developed in NeuCogAr are synthesized through hardware memristors suitable for the implementation of basic emotional states or affects on a biologically inspired robotic system

Spiking Reasoning System

© 2017 IEEE. In this position paper the newel approach for the spiking reasoning system for the real-time processing of a robotic system represented. This is the development of the 'Robot dream' architecture presented earlier, specifically the real-time robotic management system. The main idea of the architecture is inherited from our previous works on machine cognition that have their roots in works of Marvin Minsky, specifically 'model of six' as six levels of the mental activity. The principal approach for the high-level architecture and provide examples of the data structures of the spiking reasoning system and robotic system management architecture was demonstrated

Gait generation via intrinsically stable MPC for a multi-mass humanoid model

We consider the problem of generating a gait with no a priori assigned footsteps while taking into account the contribution of the swinging leg to the total Zero Moment Point (ZMP). This is achieved by considering a multi-mass model of the humanoid and distinguishing between secondary masses with known pre-defined motion and the remaining, primary, masses. In the case of a single primary mass with constant height, it is possible to transform the original gait generation problem for the multi-mass system into a single LIP-like problem. We can then take full advantage of an intrinsically stable MPC framework to generate a gait that takes into account the swinging leg motion

Real-time pursuit-evasion with humanoid robots

We consider a pursuit-evasion problem between humanoids. In our scenario, the pursuer enters the safety area of the evader headed for collision, while the latter executes a fast evasive motion. Control schemes are designed for both the pursuer and the evader. They are structurally identical, although the objectives are different: the pursuer tries to align its direction of motion with the line-of-sight to the evader, whereas the evader tries to move in a direction orthogonal to the line-of-sight to the pursuer. At the core of the control scheme is a maneuver planning module which makes use of closed- form expressions exclusively. This allows its use in a replanning framework, where each robot updates its motion plan upon completion of a step to account for the perceived motion of the other. Simulation and experimental results on NAO humanoids reveal an interesting asymptotic behavior which was predicted using unicycle as template models for trajectory generation

A framework for safe human-humanoid coexistence

This work is focused on the development of a safety framework for Human-Humanoid coexistence, with emphasis on humanoid locomotion. After a brief introduction to the fundamental concepts of humanoid locomotion, the two most common approaches for gait generation are presented, and are extended with the inclusion of a stability condition to guarantee the boundedness of the generated trajectories. Then the safety framework is presented, with the introduction of different safety behaviors. These behaviors are meant to enhance the overall level of safety during any robot operation. Proactive behaviors will enhance or adapt the current robot operations to reduce the risk of danger, while override behaviors will stop the current robot activity in order to take action against a particularly dangerous situation. A state machine is defined to control the transitions between the behaviors. The behaviors that are strictly related to locomotion are subsequently detailed, and an implementation is proposed and validated. A possible implementation of the remaining behaviors is proposed through the review of related works that can be found in literature

Boundedness Issues in Planning of Locomotion Trajectories for Biped Robots

It is in general complex to consider the complete robot dynamics when planning trajectories for bipedal locomotion. We present an approach to trajectory planning, with the classical Linear Inverted Pendulum Model (LIPM), that takes explicit consideration of the unstable dynamics. We derive a relationship between initial state and the control input that ensures the overall system dynamics will converge to a stable steady state solution. This allows us to exploit the unstable dynamics to achieve system goals, while imposing constraints on certain degrees of freedom of the input and initial conditions. Based on this, we propose an approach to trajectory planning, and derive solutions for several typical applications. Experimental simulations using the REEM-C biped robot platform of Pal Robotics validate our approach