Rico Moeckel

Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization

This article addresses the problem of how modular robotics systems, i.e. systems composed of multiple modules that can be configured into different robotic structures, can learn to locomote. In particular, we tackle the problems of online learning, that is, learning while moving, and the problem of dealing with unknown arbitrary robotic structures. We propose a framework for learning locomotion controllers based on two components: a central pattern generator (CPG) and a gradient-free optimization algorithm referred to as Powells method. The CPG is implemented as a system of coupled nonlinear oscillators in our YaMoR modular robotic system, with one oscillator per module. The nonlinear oscillators are coupled together across modules using Bluetooth communication to obtain specific gaits, i.e. synchronized patterns of oscillations among modules. Online learning involves running the Powell optimization algorithm in parallel with the CPG model, with the speed of locomotion being the criterion to be optimized. Interesting aspects of the optimization include the fact that it is carried out online, the robots do not require stopping or resetting and it is fast. We present results showing the interesting properties of this framework for a modular robotic system. In particular, our CPG model can readily be implemented in a distributed system, it is computationally cheap, it exhibits limit cycle behavior (temporary perturbations are rapidly forgotten), it produces smooth trajectories even when control parameters are abruptly changed and it is robust against imperfect communication among modules. We also present results of learning to move with three different robot structures. Interesting locomotion modes are obtained after running the optimization for less than 60 minutes.

Exploring adaptive locomotion with YaMoR, a novel autonomous modular robot with Bluetooth interface

Purpose – This paper aims to present a novel modular robot that provides a flexible framework for exploring adaptive locomotion.Design/methodology/approach – A new modular robot is presented called YaMoR (for “Yet another Modular Robot”). Each YaMoR module contains an FPGA and a microcontroller supporting a wide range of control strategies and high computational power. The Bluetooth interface included in each YaMoR module allows wireless communication between the modules and controlling the robot from a PC. A control software called Bluemove was developed and implemented that allows easy testing of the capabilities for locomotion of a large variety of robot configurations.Findings – With the help of the control software called Bluemove, different configurations of the YaMoR modules were tested like a wheel, caterpillar or configurations with limbs and their capabilities for locomotion.Originality/value – This paper demonstrates that modular robots can act as a powerful framework for exploring locomotion o...

YaMoR and Bluemove -- an autonomous modular robot with Bluetooth interface for exploring adaptive locomotion

Modular robots offer a robust and flexible framework for exploring adaptive locomotion control. They allow assembling robots of different types e.g. snakelike robots, robots with limbs, and many other different shapes. In this paper we present a new cheap modular robot called YaMoR (for “Yet another Modular Robot”). Each YaMoR module contains an FPGA and a microcontroller supporting a wide range of control strategies and high computational power. The Bluetooth interface included in each YaMoR module allows wireless communication between the modules and controlling the robot from a PC. With the help of our control software called Bluemove, we tested different configurations of our YaMoR robots like a wheel, caterpillar or configurations with limbs and their capabilities for locomotion.

Roombots: A Hardware Perspective on 3D Self-Reconfiguration and Locomotion with a Homogeneous Modular Robot

In this work we provide hands-on experience on designing and testing a self-reconfiguring modular robotic system, Roombots (RB), to be used among others for adaptive furniture. In the long term, we envision that RB can be used to create sets of furniture, like stools, chairs and tables that can move in their environment and that change shape and functionality during the day. In this article, we present the first results towards that long term vision. We demonstrate locomotion and reconfiguration of single and metamodule RB over 3D surfaces, in a structured environment equipped with embedded connection ports. RB assemblies can move around in non-structured environments, by using rotational or wheel-like locomotion. We show a proof of concept for transferring a Roombots metamodule back into the structured grid, by aligning it in an entrapment mechanism. Finally, we analyze remaining challenges to master the full Roombots scenario, and discuss the impact on future Roombots hardware.

Motion Detection Circuits for a Time-To-Travel Algorithm

The paper describes a new motion detection circuit that extracts motion information based on a time-to-travel algorithm. The front-end photoreceptor adapts over 7 decades of background intensity and motion information can be extracted down to a contrast value of 2.5%. Results from the circuits which were fabricated in a 2-metal 2-poly 1.5mum CMOS process, show that the motion information can be extracted over 2 decades of speed.

Roombots—Towards decentralized reconfiguration with self-reconfiguring modular robotic metamodules

This paper presents our work towards a decentralized reconfiguration strategy for self-reconfiguring modular robots, assembling furniture-like structures from Roombots (RB) metamodules. We explore how reconfiguration by locomotion from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using Roombots metamodules—two Roombots modules connected serially—that use broadcast signals, lookup tables of their movement space, assumptions about their neighborhood, and connections to a structured surface to collectively build desired structures without the need of a centralized planner.

Gait optimization for roombots modular robots — Matching simulation and reality

The design of efficient locomotion gaits for robots with many degrees of freedom is challenging and time consuming even if optimization techniques are applied. Control parameters can be found through optimization in two ways: (i) through online optimization where the performance of a robot is measured while trying different control parameters on the actual hardware and (ii) through offline optimization by simulating the robots behavior with the help of models of the robot and its environment. In this paper, we present a hybrid optimization method that combines the best properties of online and offline optimization to efficiently find locomotion gaits for arbitrary structures. In comparison to pure online optimization, both the number of experiments using robotic hardware as well as the total time required for finding efficient locomotion gaits get highly reduced by running the major part of the optimization process in simulation using a cluster of processors. The presented example shows that even for robots with a low number of degrees of freedom the time required for optimization can be reduced by a factor of 2.5 to 30, at least, depending on how extensive the search for optimized control parameters should be. Time for hardware experiments becomes minimal. More importantly, gaits that can possibly damage the robotic hardware can be filtered before being tried in hardware. Yet in contrast to pure offline optimization, we reach well matched behavior that allows a direct transfer of locomotion gaits from simulation to hardware. This is because through a meta-optimization we adapt not only the locomotion parameters but also the parameters for simulation models of the robot and environment allowing for a good matching of the robot behavior in simulation and hardware. We validate the proposed hybrid optimization method on a structure composed of two Roombots modules with a total number of six degrees of freedom. Roombots are self-reconfigurable modular robots that can form arbitrary structures with many degrees of freedom through an integrated active connection mechanism.

Compliant and adaptive control of a planar monopod hopper in rough terrain

In this paper, a method is proposed for controlling a hopping monopod. It takes dynamics of the robot into account to have better nominal tracking of desired trajectories and more compliant environmental interactions at the same time. We have incorporated also natural dynamics of the robot into the system by using off-line gaits extracted from optimizations on energy. The main control loop consists of the projected inverse dynamics that generates actuator torques given desired trajectories and also a feedback loop designed and tuned specifically for the structure of the robot. A trajectory generator uses known optimal trajectories together with some stabilizing control laws that modify these trajectories to have better robustness in different situations. The average speed of the robot is also regulated by means of a self-organizing controller. We apply soft transitions in trajectories from phase to phase to avoid sharp actuator input profiles. Our method is successfully tested on a monopod hopper robot in simulation. It can handle slightly rough or sloped terrains while maintaining a given average speed. Simulation results suggest that our method is a promising candidate to control a real robot under construction.

An experimental study on the role of compliant elements on the locomotion of the self-reconfigurable modular robots Roombots

This paper presents the results of a study on the exploitation of compliance in structures made of self-reconfigurable modular robots - Roombots. This research was driven by the following three hypotheses: (1) compliance can improve locomotion performance; (2) different types of compliance will result in diverse locomotion behaviors; (3) control parameters optimized for a medium level of compliance will perform better for other values of compliance than parameters optimized for extremal compliance. Two types of in-series compliant elements were tested, with five different stiffness values for each of them, on a structure made of two Roombots modules. We ran dedicated on-line locomotion parameter optimizations for six different configurations and evaluated their performance for different stiffness values. Hypothesis 1 was confirmed for both types of compliant elements, with a peak of performance for an optimal level of compliance. The variety of locomotion strategies obtained for the different structures confirms hypothesis 2. Hypothesis 3 was only partially confirmed.

Motion Detection Chips for Robotic Platforms

The on-board requirements for small, light, low-power sensors, and electronics on autonomous micro-aerial vehicles limit the computational power and speed available for processing sensory signals. The sensory processing on these platforms is usually inspired by the sensory information extracted by insects from their world, in particular optic flow. This information is also useful for distance estimation of the vehicle from objects in its path. Custom Very Large Scale Integrated (VLSI) sensor chips which perform focal-plane motion estimation are beneficial for such platforms because of properties including compactness, continuous-time operation, and low-power dissipation. This chapter gives an overview of the various monolithic analog VLSI motion detection/optic flow chips that have been designed over the last 2 decades. We contrast the pros and cons of the different algorithms that have been implemented and we identify promising chip architectures that are suitable for flying platforms.