Online trajectory generation in an amphibious snake robot using a lamprey-like central pattern generator model

This article presents a control architecture for controlling the locomotion of an amphibious snake/lamprey robot capable of swimming and serpentine locomotion. The control architecture is based on a central pattern generator (CPG) model inspired from the neural circuits controlling locomotion in the lamprey's spinal cord. The CPG model is implemented as a system of coupled nonlinear oscillators on board of the robot. The CPG generates coordinated travelling waves in real time while being interactively modulated by a human-operator. Interesting aspects of the CPG model include (1) that it exhibits limit cycle behavior (i.e. it produces stable rhythmic patterns that are robust against perturbations), (2) that the limit cycle behavior has a closed-form solution which provides explicit control over relevant characteristics such as frequency, amplitude and wavelength of the travelling waves, and (3) that the control parameters of the CPG can be continuously and interactively modulated by a human operator to offer high maneuverability. We demonstrate how the CPG allows one to easily adjust the speed and direction of locomotion both in water and on ground while ensuring that continuous and smooth setpoints; are sent to the robot's actuated joints

Online optimization of swimming and crawling in an amphibious snake robot

An important problem in the control of locomotion of robots with multiple degrees of freedom (e.g., biomimetic robots) is to adapt the locomotor patterns to the properties of the environment. This article addresses this problem for the locomotion of an amphibious snake robot, and aims at identifying fast swimming and crawling gaits for a variety of environments. Our approach uses a locomotion controller based on the biological concept of central pattern generators (CPGs) together with a gradient-free optimization method, Powell’s method. A key aspect of our approach is that the gaits are optimized online, i.e., while moving, rather than as an off-line optimization process. We present various experiments with the real robot and in simulation: swimming, crawling on horizontal ground, and crawling on slopes. For each of these different situations, the optimized gaits are compared with the results of systematic explorations of the parameter space. The main outcomes of the experiments are: 1) optimal gaits are significantly different from one medium to the other; 2) the optimums are usually peaked, i.e., speed rapidly becomes suboptimal when the parameters are moved away from the optimal values; 3) our approach finds optimal gaits in much fewer iterations than the systematic search; and 4) the CPG has no problem dealing with the abrupt parameter changes during the optimization process. The relevance for robotic locomotion control is discussed

Design and control of amphibious robots with multiple degrees of freedom

This thesis presents the design and realization of two generations of robot elements that can be assembled together to construct amphibious mobile robots. These elements, designed to be individually waterproof and having their own battery, motor controller, and motor, have been used to actually construct a snake, a boxfish and a salamander robot. Central pattern generator (CPG) models inspired from those found in vertebrates have been used for online trajectory generation on these robots and implemented on their onboard locomotion controllers. CPGs proved to be an interesting way of controlling complex robots, providing a simple interface which hides the complexity of the robot to the end user. Online learning algorithms that can be used to dynamically adapt the locomotion parameters to the environment have been implemented. Finally, this work also shows how robotics can be a useful tool to verify biological hypotheses. For instance, the salamander robot has been used to test a model of CPG for salamander locomotion

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Analisi e sintesi di Central Pattern Generator

Negli esseri viventi, un Central Pattern Generator (CPG) \ue8 una rete di neuroni relativamente piccola, in grado di produrre pattern ritmici anche in assenza di feedback sensoriali o di segnali provenienti dal sistema nervoso centrale. Queste reti hanno un ruolo fondamentale nella regolazione di molte attivit\ue0 ritmiche, come per esempio la nuotata, la respirazione, la masticazione e la locomozione. Lo studio di queste reti \ue8 di interesse per diverse discipline, non solo per la loro valenza biologica, ma anche per le loro possibili applicazioni alla riabilitazione e al controllo di robot biologicamente ispirati. In questa tesi sono proposti alcuni strumenti per l'analisi, la riduzione, la sintesi e l'emulazione circuitale di tali reti neuronali. In particolare, i tool proposti sono stati applicati ad un caso di studio in cui ci si \ue8 concentrati sul CPG responsabile della locomozione dei topi

Fast Sensing and Adaptive Actuation for Robust Legged Locomotion

Robust legged locomotion in complex terrain demands fast perturbation detection and reaction. In animals, due to the neural transmission delays, the high-level control loop involving the brain is absent from mitigating the initial disturbance. Instead, the low-level compliant behavior embedded in mechanics and the mid-level controllers in the spinal cord are believed to provide quick response during fast locomotion. Still, it remains unclear how these low- and mid-level components facilitate robust locomotion. This thesis aims to identify and characterize the underlining elements responsible for fast sensing and actuation. To test individual elements and their interplay, several robotic systems were implemented. The implementations include active and passive mechanisms as a combination of elasticities and dampers in multi-segment robot legs, central pattern generators inspired by intraspinal controllers, and a synthetic robotic version of an intraspinal sensor. The first contribution establishes the notion of effective damping. Effective damping is defined as the total energy dissipation during one step, which allows quantifying how much ground perturbation is mitigated. Using this framework, the optimal damper is identified as viscous and tunable. This study paves the way for integrating effective dampers to legged designs for robust locomotion. The second contribution introduces a novel series elastic actuation system. The proposed system tackles the issue of power transmission over multiple joints, while featuring intrinsic series elasticity. The design is tested on a hopper with two more elastic elements, demonstrating energy recuperation and enhanced dynamic performance. The third contribution proposes a novel tunable damper and reveals its influence on legged hopping. A bio-inspired slack tendon mechanism is implemented in parallel with a spring. The tunable damping is rigorously quantified on a central-pattern-generator-driven hopping robot, which reveals the trade-off between locomotion robustness and efficiency. The last contribution explores the intraspinal sensing hypothesis of birds. We speculate that the observed intraspinal structure functions as an accelerometer. This accelerometer could provide fast state feedback directly to the adjacent central pattern generator circuits, contributing to birds’ running robustness. A biophysical simulation framework is established, which provides new perspectives on the sensing mechanics of the system, including the influence of morphologies and material properties. Giving an overview of the hierarchical control architecture, this thesis investigates the fast sensing and actuation mechanisms in several control layers, including the low-level mechanical response and the mid-level intraspinal controllers. The contributions of this work provide new insight into animal loco-motion robustness and lays the foundation for future legged robot design