Vítor Matos

Gait transition and modulation in a quadruped robot: A brainstem-like modulation approach

In this article, we propose a bio-inspired architecture for a quadruped robot that is able to initiate/stop locomotion; generate different gaits, and to easily select and switch between the different gaits according to the speed and/or the behavioral context. This improves the robot stability and smoothness while locomoting. We apply nonlinear oscillators to model Central Pattern Generators (CPGs). These generate the rhythmic locomotor movements for a quadruped robot. The generated trajectories are modulated by a tonic signal, that encodes the required activity and/or modulation. This drive signal strength is mapped onto sets of CPG parameters. By increasing the drive signal, locomotion can be elicited and velocity increased while switching to the appropriate gaits. This drive signal can be specified according to sensory information or set a priori. The system is implemented in a simulated and real AIBO robot. Results demonstrate the adequacy of the architecture to generate and modulate the required coordinated trajectories according to a velocity increase; and to smoothly and easily switch among the different motor behaviors.

CPG modulation for navigation and omnidirectional quadruped locomotion

Navigation in biological mechanisms represents a set of skills needed for the survival of individuals, including target acquisition and obstacle avoidance. In this article, we focus on the development of a quadruped locomotion controller able to generate omnidirectional locomotion and a path planning controller for heading direction. The heading direction controller is able to adapt to sensory-motor visual feedback, and online adapt its trajectory according to visual information that modifies the control parameters. This allows for integration of sensory-motor feedback and closed-loop control. This issue is crucial for autonomous and adaptive control, and has received little attention so far. This modeling is based on the concept of dynamical systems. We present experiments performed on a real AIBO platform. The obtained results demonstrate both the adequacy of the proposed locomotor controller to generate the required trajectories and to generate the desired movement in terms of the walking velocity, orientation and angular velocity. Further, the controller is demonstrated on a simulated quadruped robot which walks towards a visually acquired target while avoiding online-visually detected obstacles in its path.

A brainstem-like modulation approach for gait transition in a quadruped robot

The ability to traverse a wide variety of terrains while walking is basically a requirement for performing useful tasks in our human centric world. In this article, we propose a bio-inspired robotic controller able to generate locomotion and to easily switch between different type of gaits. In order to improve the robot stability and response while locomoting, we adjust both the duty factor and the interlimb phase relationships, according to the velocities. We extend previous work, by applying nonlinear oscillators to generate the rhythmic locomotor movements for a quadruped robot, similarly to the biological counterparts. The generated trajectories are modulated by a drive signal, that modifies the oscillator frequency, amplitude and the coupling parameters among the oscillators, proportionally to the drive signal strength. By increasing the drive signal, locomotion can be elicited and velocity increased while switching to the appropriate gaits. This drive signal can be specified according to sensory information or set a priori. The implementation of the central pattern generator network and the activity modulation layer is shown in simulation and in an AIBO robot.

Omnidirectional locomotion in a quadruped robot: A CPG-based approach

Quadruped locomotion on rough terrain and un-predictable environments is still a challenge, where the concept of Central Pattern Generators (CPG) has brought interesting ideas.

Multi-objective parameter CPG optimization for gait generation of a biped robot

This paper presents a biped gait optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm. CPGs are modeled as autonomous differential equations, that generate the necessary limb movements to perform the walking gait of a biped robot. The search for the best set of CPG parameters is optimized by considering multiple objectives and according to a staged evolution. A sensitivity analysis is used to evaluate the relationship between objectives, objectives and parameters, and allows to determine the functional meanings of the parameters. This resulting functional analysis enables to verify which parameters are relevant to the motor behaviors. The simulation results show the effectiveness of the proposed approach. The different obtained walking gait solutions correspond to different trade-offs between the objectives.

Central Pattern Generators with phase regulation for the control of humanoid locomotion

This work presents a Central Pattern Generator approach where the designer is able to build a basic motor repertoire that enables a biped robot to walk. The presented locomotor system includes a phase regulation feedback, which elicits or delays the transitions between swing and stance step phases according to load sensory information, adjusting the nominal walk to the environment. The approach is tested on two simulated humanoid robots and in a DARwIn-OP robot, achieving a multitude of locomotor tasks, showing how general the proposed locomotor system can be. Simulations and experiments also demonstrate the role of phase regulation on addressing small external perturbations.

Multi-objective parameter CPG optimization for gait generation of a quadruped robot considering behavioral diversity

This paper presents a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm. CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, four conflicting objectives are considered, simultaneously: minimize the body vibration, maximize the velocity, maximize the wide stability margin and maximize the behavioral diversity. The results of NSGA-II for this multi-objective problem are discussed. The effect of the inclusion of a behavioral diversity objective in the system is also studied in terms of the walking gait achieved. The experimental results show the effectiveness of this multi-objective approach. The several walking gait solutions obtained correspond to different trade-off between the objectives.

A bio-inspired postural control for a quadruped robot: An attractor-based dynamics

Postural stability is a requirement for autonomous adaptive legged locomotion. Neurobiological research lead to the idea that there are independent central systems for posture and locomotion, which interact when required.

Hexapod locomotion: A nonlinear dynamical systems approach

The ability of walking in a wide variety of terrains is one of the most important features of hexapod insects. In this paper we describe a bio-inspired controller able to generate locomotion and switch between different type of gaits for an hexapod robot. Motor patterns are generated by coupled Central Pattern Generators formulated as nonlinear oscillators. These patterns are modulated by a drive signal, proportionally changing the oscillators frequency, amplitude and the coupling parameters among the oscillators. Locomotion initiation, stopping and smooth gait switching is achieved by changing the drive signal. We also demonstrate a posture controller for hexapod robots using the dynamical systems approach. Results from simulation using a model of the Chiara hexapod robot demonstrate the capability of the controller both to locomotion generation and smooth gait transition. The postural controller is also tested in different situations in which the hexapod robot is expected to maintain balance. The presented results prove its reliability.

Towards goal-directed biped locomotion: Combining CPGs and motion primitives

Abstract In this paper it is presented a CPG approach based on phase oscillators to bipedal locomotion where the designer with little a priori knowledge is able to incrementally add basic motion primitives, reaching bipedal walking and other locomotor behaviors as final result. The proposed CPG aims to be a model free solution for the generation of bipedal walking, not requiring the use of inverse kinematical models and previously defined joint trajectories. The proposed incremental construction of bipedal walking allows an easier parametrization and performance evaluation throughout the design process. Furthermore, the approach provides for a developmental mechanism, which enables progressively building a motor repertoire. It would easily benefit from evolutionary robotics and machine learning to explore this aspect. The proposed CPG system also offers a good substrate for the inclusion of feedback mechanisms for modulation and adaptation. It is explored a phase regulation mechanism using load sensory information, observable in vertebrate legged animals. Results from simulations, on HOAP and DARwIn-OP in Webots software show the adequacy of the locomotor system to generate bipedal walk on different robots. Experiments on a DARwIn-OP demonstrates how it can accomplish locomotion and how the proposed work can generalize, achieving several distinct locomotor behaviors.