An Underwater Biomimetic Robot that can Swim,Bipedal Walk and Grasp

In developing and exploring extreme and harsh underwater environments,underwater robots can effectively replace humans to complete tasks.To meet the requirements of underwater flexible motion and comprehensive subsea operation,a novel octopus-inspired robot with eight soft limbs was designed and developed.This robot possesses the capabilities of underwater bipedal walking,multi-arm swimming,and grasping objects.To closely interact with the underwater seabed environment and minimize disturbance,the robot employs a cable-driven flexible arm for its walking in underwater floor through a bipedal walking mode.The multi-arm swimming offers a means of three-dimensional spatial movement,allowing the robot to swiftly explore and navigate over large areas,thereby enhancing its flexibility.Furthermore,the robot’s walking arm enables it to grasp and transport objects underwater,thereby enhancing its practicality in underwater environments.A simplified motion models and gait generation strategies were proposed for two modes of robot locomotion:swimming and walking,inspired by the movement characteristics of octopus-inspired multi-arm swimming and bipedal walking.Through experimental verification,the robot’s average speed of underwater bipedal walking reaches 7.26 cm/s,while the horizontal movement speed for multi-arm swimming is 8.6 cm/s.

Development of Minimalist Bipedal Walking Robot with Flexible Ankle and Split-mass Balancing Systems

This paper presents a novel design of minimalist bipedal walking robot with flexible ankle and split-mass balancing systems.The proposed approach implements a novel strategy to achieve stable bipedal walk by decoupling the walking motion control from the sideway balancing control.This strategy allows the walking controller to execute the walking task independently while the sideway balancing controller continuously maintains the balance of the robot.The hip-mass carry approach and selected stages of walk implemented in the control strategy can minimize the efect of major hip mass of the robot on the stability of its walk.In addition,the developed smooth joint trajectory planning eliminates the impacts of feet during the landing.In this paper,the new design of mechanism for locomotion systems and balancing systems are introduced.An additional degree of freedom introduced at the ankle joint increases the sensitivity of the system and response time to the sideway disturbances.The efectiveness of the proposed strategy is experimentally tested on a bipedal robot prototype.The experimental results provide evidence that the proposed strategy is feasible and advantageous.

Dynamic Stability of Passive Bipedal Walking on Rough Terrain： A Preliminary Simulation Study

A simplified 2D passive dynamic model was simulated to walk down on a rough slope surface defined by deterministic profiles to investigate how the walking stability changes with increasing surface roughness. Our results show that the passive walker can walk on rough surfaces subject to surface roughness up to approximately 0.1% of its leg length. This indicates that bipedal walkers based on passive dynamics may possess some intrinsic stability to adapt to rough terrains although the maxi- mum roughness they can tolerate is small. Orbital stability method was used to quantify the walking stability before the walker started to fall over, It was found that the average maximum Floquet multiplier increases with surface roughness in a non-linear form. Although the passive walker remained orbitally stable for all the simulation cases, the results suggest that the possibility of the bipedal model moving away from its limit cycle increases with the surface roughness if subjected to additional perturbations. The number of consecutive steps before falling was used to measure the walking stability after the passive walker started to fall over. The results show that the number of steps before falling decreases exponentially with the increase in surface roughness. When the roughness magnitude approached to 0.73% of the walker＇s leg length, it fell down to the ground as soon as it entered into the uneven terrain. It was also found that shifting the phase angle of the surface profile has apparent affect on the system stability. This is probably because point contact was used to simulate the heel strikes and the resulted variations in system states at heel strikes may have pronounced impact on the passive gaits, which have narrow basins of attraction. These results would provide insight into how the dynamic stability of passive bipedal walkers evolves with increasing surface roughness.

Computational Models to Synthesize Human Walking

The synthesis of human walking is of great interest in biomechanics and biomimetic engineering due to its predictive capabilities and potential applications in clinical biomechanics, rehabilitation engineering and biomimetic robotics. In this paper, the various methods that have been used to synthesize humanwalking are reviewed from an engineering viewpoint. This involves a wide spectrum of approaches, from simple passive walking theories to large-scale computational models integrating the nervous, muscular and skeletal systems. These methods are roughly categorized under four headings： models inspired by the concept of a CPG （Central Pattern Generator）, methods based on the principles of control engineering, predictive gait simulation using optimisation, and models inspired by passive walking theory. The shortcomings and advantages of these methods are examined, and future directions are discussed in the context of providing insights into the neural control objectives driving gait and improving the stability of the predicted gaits. Future advancements are likely to be motivated by improved understanding of neural control strategies and the subtle complexities of the musculoskeletal system during human locomotion. It is only a matter of time before predictive gait models become a practical and valuable tool in clinical diagnosis, rehabilitation engineering and robotics.

Planning and Control of COP-Switch-Based Planar Biped Walking

Efficient walking is one of the main goals of researches on biped robots. A feasible way is to translate the understanding from human walking into robot walking, for example, an artificial control approach on a human like walking structure. In this paper, a walking pattern based on Center of Pressure （COP） switched and modeled after human walking is introduced firstly. Then, a parameterization method for the proposed walking gait is presented. In view of the complication, a multi-space planning method which divides the whole planning task into three sub-spaces, including simplified model space, work space and joint space, is proposed. Furthermore, a finite-state-based control method is also developed to implement the proposed walking pattern. The state switches of this method are driven by sensor events. For convincing verification, a 2D simulation system with a 9-1ink planar biped robot is developed. The simulation results exhibit an efficient walking gait.

Stability Control and Simulation Experiments of Biped Humanoid Robot Walking on Rough Terrains

In this paper, we provide several methods to solve the problem of humanoid robot walking on rough terrains. By using the Passive Inverted Pendulum Model（PIPM） and predictive control, with some optimizing strategy added, we realized the smooth walking on a slope with rocks on it.

Preface

Micromechanics aims mainly at establishing the quantitative relation between the macroscopic mechanical behavior and the microstructure of heterogeneous materials.

Capturability-based Fuzzy Footstep Planner for a Biped Robot with Centroidal Compliance

Compliance motion and footstep adjustment are active balance control strategies from learning human subconscious behaviors.The force estimation without direct end-actuator force measurement and the optimal footsteps based on complex analytical calculation are still challenging tasks for elementary and kid-size position-controlled robots.In this paper,an online compliant controller with Gravity Projection Observer(GPO),which can express the external force condition of perturbations by the estimated Projection of Gravity(PoG)with estimation covariance,is proposed for the realization of disturbance absorption,with which the robustness of the humanoid contact with environments can be maintained.The fuzzy footstep planner based on capturability analysis is proposed,and the Model Predictive Control(MPC)is applied to generate the desired steps.The fuzzification rules are well-designed and give the corresponding control output responding to complex and changeable external disturbances.To validate the presented methods,a series of experiments on a real humanoid robot are conducted.The results verify the effectiveness of the proposed balance control framework.