The embedded system that is distributed within a bipedal robot is a key component of such a highly interwoven mechatronic system. Generally, it has to handle two competing main tasks – executing the embedded closed-loop control of the actuators and handling the communication with the higher-level control system. As the restrictions on physical size and energy consumption limit its computational resources, the design of the embedded nodes poses a potential bottleneck for the performance of the overall system. Hence, the following presents an approach to mitigate the conflicting requirements by deploying FPGA-based embedded nodes. It is illustrated how the additional flexibility at the logic level is used to implement the closed-loop force and impedance control of a series elastic actuator. Furthermore, it is shown how the consequent hardware/software co-design enables the deployment of a full featured robotic framework. To validate the concept, the properties of the implementation are characterized.

The Bio-inspired Behavior-Based Bipedal Locomotion Control (B4LC) system consists of control units encapsulating feed-forward and feedback mechanisms, namely motor patterns and reflexes. To optimize the performance of motor patterns and reflexes in terms of stable locomotion on both even and uneven terrains, we present a learning scheme embedded in the B4LC system. By combining the Particle Swarm Optimization (PSO) method and the Expectation-maximization based Reinforcement Learning (EM-RL) method, a learning unit is comprised of an optimization module and a learning module embedded in the hierarchical control structure. The optimization module optimizes the motor patterns at hip and ankle joints with respect to energy consumption, stability and velocity control. The learning module generates compensating torques against disturbances at the ankle joints by combining the basis function derived from state information and the policy parameters. The optimization and learning procedures are conducted on a simulated robot with 21 DoFs. The simulation results show that the robot with optimized motor patterns and learned reflexes performs a more robust and stable locomotion on even and uneven terrains.

CREA robot is designed to climb up concrete walls. The robot uses the suction mechanism to provide adhesion and wheel mechanism for locomotion. Eleven chambers which are connected to one common reservoir are responsible to produce adhesion force. A controller is developed to independently control each chamber while satisfying certain criteria on the safety of the robot. It is also designed to reach minimum friction between active inflatable seals and wall. In conclusion, the controller is able to successfully meet the conditions of stability, minimum friction and safety.

Human shows remarkable skills in reactive balancing control on unknown disturbances while standing and walking. Though current bipedal robots can walk, run and step obstacles, they normally perform in a well-controlled environment. Unexpected perturbations can cause the tumbling of bipedal robots when they possess limited capability of rejecting disturbances. Studies upon neurology and psychophysics of human stance attempt to trace how the human deals with external disturbances. This paper introduces a methodology for disturbances estimation and compensation (DEC) for bipedal robot during standing. Previous psychophysical studies of human self-motion perception indicate that humans estimate and compensate disturbances as follows: firstly, multi-sensory inputs are fused to provide explicit measures and then the estimations of the external disturbances are performed based on them. Then, the estimations are fed into a local feedback control loop, compensating the disturbances. Thus, an approach of disturbance estimation and compensation is developed according to the psychophysical aspects of human. Various experiments, for instance, standing on a rotating plate with varying frequency and amplitudes and continuous external contact forces upon the torso of a bipedal robot, are implemented. Through analyzing and verifying the experimental results on a simulated biped, one can state that the DEC approach successfully serves the bipedal robot during perturbed stance.