Trot Gait Locomotion of A Cat Sized Quadruped Robot

We are proposing and testing two model-free approaches for locomotion control of a light-weight, compliant, quadruped robot: open loop central pattern generators (CPG), and open and closed- loop dynamical movement primitives (DMP). We are presenting two different knee joint controllers, based on the hypothesis that the passive- compliant leg design might require less control effort for the knee joint control. CPG-control parameters are optimized applying extensive optimization runs using PSO, resulting trot gaits are evaluated for speed and robustness. One derived gait is selected as learning input for a gait controller based on DMP. We design the DMP-based trot gait controller both for open and closed-loop control. For the latter we are feeding back gyroscope and touch sensor signals to modulate the core phase pattern. Found CPG based trot gaits are up to 90cm/sec fast (more than 3 BL/sec). The joint controller assisting the passive compliant knee joint shows gaits with higher speed, and larger hip amplitude. Closed- loop DMP based gaits let the robot handle frontal and sideways slopes, step-downs, and random pushes to the robot’s COM more robustly.

Pattern Generation for Rough Terrain Locomotion with Quadrupedal Robots

Mostafa Ajallooeian

Animals are able to locomote on rough terrain without any apparent difficulty, but this does not mean that the locomotor system is simple. The locomotor system is actually a complex multi-input multi-output closed-loop control system. This thesis is dedicated to the design of controllers for rough terrain locomotion, for animal-like quadrupedal robots. We choose the problem of blind rough terrain locomotion as the target of experiments. Blind rough terrain locomotion requires continuous and momentary corrections of leg movements and body posture, and provides a proper testbed to observe the interaction of different mod- ules involved in locomotion control. As for the specific case of this thesis, we have to design rough terrain locomotion controllers that do not depend on the torque-control capability, have limited sensing, and have to be computationally light, all due to the properties of the robotics platform that we use. We propose that a robust locomotion controller, taking into account the aforementioned constraints, is constructed from at least three modules: 1) pattern generators providing the nominal patterns of locomotion; 2) A posture controller continuously adjusting the attitude of the body and keeping the robot upright; and 3) quick reflexes to react to unwanted momentary events like stumbling or an external force impulse. We introduce the framework of morphed oscillators to systematize the design of pattern gen- erators realized as coupled nonlinear oscillators. Morphed oscillators are nonlinear oscillators that can encode arbitrary limit cycle shapes and simultaneously have infinitely large basins of attraction. More importantly, they provide dynamical systems that can assume the role of feedforward locomotion controllers known as Central Pattern Generators (CPGs), and accept discontinuous sensory feedback without the risk of producing discontinuous output. On top of the CPG module, we add a kinematic model-based posture controller inspired by virtual model control (VMC), to control the body attitude. Virtual model control produces forces, and through the application of the Jacobian transpose method, generates torques which are added to the CPG torques. However, because our robots do not have a torque- control capability, we adapt the posture controller by producing task-space velocities instead of forces, thus generating joint-space velocity feedback signals. Since the CPG model used for locomotion generates joint velocities and accepts feedback without the fear of instability or discontinuity, the posture control feedback is easily integrated into the CPG dynamics. More- over, we introduce feedback signals for adjusting the posture by shifting the trunk positions, which directly update the limit cycle shape of the morphed oscillator nodes of the CPG. Reflexes are added, with minimal complexity, to react to momentary events. We implement simple impulse-based feedback mechanisms inspired by animals and successful rough terrain robots to 1) flex the leg if the robot is stumbling (stumbling correction reflex); 2) extend the leg if an expected contact is missing (leg extension reflex); or 3) initiate a lateral stepping sequence in response to a lateral external perturbation. CPG, posture controller, and reflexes are put together in a modular control architecture alongside additional modules that estimate inclination, control speed and direction, maintain timing of feedback signals, etc. [...]

EPFL2015

Roombots: Design and Implementation of a Modular Robot for Reconfiguration and Locomotion

Alexander Spröwitz

In this thesis we present the design and implementation of a novel self-reconfiguring modular (SR-MR) robotic system: Roombots. We are aiming at three main applications with Roombots; locomotion through self-reconfiguration in the regular cubic 3D-lattice on structured surfaces, locomotion in non-structured environments applying central pattern generators (CPG) as the locomotion controller, and self-assembly and reconfiguration of static objects of the day-to-day environment, such as furniture. Robot assemblies from self-reconfigurable modular robots have the ability to adapt to a given task and working environment by altering their shape through a series of reconfiguration moves, and attachments and detachments between the modules. We are interested in self-reconfiguring modular robots for their shape-changing capabilities, and their distributed characteristics. We envision the following applications for Roombots: First, self-reconfiguration in a structured 3D lattice, i.e. a floor and walls equipped with connectors. Embedded connectors can provide pivot points for locomotion of SR-MR assemblies, and docking and recharging places for our adaptive furniture pieces. Second, our proposed concept for locomotion control of modular robots on non-structured ground are central pattern generators. Third, we would like to build adaptive and versatile furniture from modular robots and light-weight elements. In the following we are able to count more than 60 modular robotic systems, developed over the last two decades. However we were unable to identify an existing system which could provide us with all desired kinematic and geometric capabilities. This led us to design and implement a novel self-reconfiguring modular robotic system: Roombots. To tackle the module design we attempt to identify both meaningful design parameters from existing modular robots, and essential features for our applications. The combination of both leads to the kinematic and geometric description of the Roombots modules, and eventually to its implementation. In order to be able to assemble furniture from our Roombots units in the future, we need a reconfiguration framework which supports the specific requirements of Roombots. Metamodules made of two units attached in-series are attracted and guided by a virtual force-field, they use broadcast signals, look-up tables of collision clouds and simple assumptions about their near environment to reach their seeding positions, which are currently hand coded. For the task of locomotion in non-structured environments we propose a framework for learning to move with modular robots using central pattern generators and online optimization. The distributed implementation of CPGs offers an ideal substrate for producing locomotion patterns and for online learning, and an optimization framework for fast learning.

EPFL2010

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Nicolas Benoît Dominique Van der Noot

Bipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. Yet, humans usually manage to move without any apparent difficulty, even on rough terrains. This requires a complex control scheme which is far from being understood. In this thesis, we take inspiration from the impressive human walking capabilities to design neuromuscular controllers for humanoid robots. More precisely, we control the robot motors to reproduce the action of virtual muscles commanded by stimulations (i.e. neural signals), similarly to what is done during human locomotion. Because the human neural circuitry commanding these muscles is not completely known, we make hypotheses about this control scheme to simplify it and progressively refine the corresponding rules. This thesis thus aims at developing new walking algorithms for humanoid robots in order to obtain fast, human-like and energetically efficient gaits. In particular, gait robustness and richness are two key aspects of this work. In other words, the gaits developed in the thesis can be steered by an external operator, while being resistant to external perturbations. This is mainly tested during blind walking experiments on COMAN, a 95 cm tall humanoid robot. Yet, the proposed controllers can be adapted to other humanoid robots. In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model to the real COMAN platform. When tested in a 2D simulation environment, this model was capable of reproducing stable human-like locomotion. By porting it to real hardware, we show that these neuromuscular controllers are viable solutions to develop new controllers for robotics locomotion. Starting from this reflex-based model, we progressively iterate and transform the stimulation rules to add new features. In particular, gait modulation is obtained with the inclusion of a central pattern generator (CPG), a neural circuit capable of producing rhythmic patterns of neural activity without receiving rhythmic inputs. Using this CPG, the 2D walker controllers are incremented to generate gaits across a range of forward speeds close to the normal human one. By using a similar control method, we also obtain 2D running gaits whose speed can be controlled by a human operator. The walking controllers are later extended to 3D scenarios (i.e. no motion constraint) with the capability to adapt both the forward speed and the heading direction (including steering curvature). In parallel, we also develop a method to automatically learn stimulation networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency. In sum, we develop neuromuscular controllers generating human-like gaits with steering capabilities. These controllers recruit three main components: (i) virtual muscles generating torque references at the joint level, (ii) neural signals commanding these muscles with reflexes and CPG signals, and (iii) higher level commands controlling speed and heading. Interestingly, these developments target humanoid robots locomotion but can also be used to better understand human locomotion. In particular, the recruitment of a CPG during human locomotion is still a matter open to debate. This question can thus benefit from the experiments performed in this thesis.

EPFL2017