Quadruped locomotion in irregular and unknown terrains is still a problem to solve. The concept of reflexes is used in this work to contribute for the continuous search of answers about this theme. Biological researches show that spinal reflexes are crucial for a successful locomotion in the most varied terrains, so robotics investigation in this area could be a great advance in the robot's locomotion. In this work, we present a sensory driven reflex controller, capable of generating locomotion in a quadruped compliant robot. This controller is totally dependent on sensory information, so the robot's movements are the result of the robot interactions with the environment. Results show that the proposed controller is capable of generating movements in a flat terrain and is resilient to unexpected perturbations such as a small ramp.

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.

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.

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. In this contribution we present a CPG design based on coupled oscillators, generating the required stepping movements of a limb for omnidirectional motion. Movements are online modulated through small value changes in the CPG's parameters as required to perform the desired omnidirectional locomotion in a quadruped robot. We also present a methodology to modulate the CPG's parameters, reducing the control dimensionality, described in terms of the robot's translational speed, angular velocity and walking orientation. Results show the proposed controller is well suited for the online generation and modulation of the motor patterns required to achieve the desired omnidirectional walking motion.

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.

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.