Type Design and Behavior Control for Six Legged Robots

The research on legged robots attracted much attention both from the academia and industry. Legged robots are multi-input multi-output with multiple end-e ector systems. Therefore,the mechanical design and control framework are challenging issues. This paper reviews the development of type synthesis and behavior control on legged robots; introduces the hexapod robots developed in our research group based on the proposed type synthesis method. The control framework for legged robots includes data driven layer,robot behavior layer and robot execution layer. Each layer consists several components which are explained in details. Finally,various experiments were conducted on several hexapod robots. The summarization of the type synthesis and behavior control design constructed in this paper would provide a unified platform for communications and references for future advancement for legged robots.

Explosive Electric Actuator and Control for Legged Robots

Unmanned systems such as legged robots require fast-motion responses for operation in complex envi-ronments.These systems therefore require explosive actuators that can provide high peak speed or high peak torque at specific moments during dynamic motion.Although hydraulic actuators can provide a large force,they are relatively inefficient,large,and heavy.Industrial electric actuators are incapable of providing instant high power.In addition,the constant reduction ratio of the reducer makes it difficult to eliminate the tradeoff between high speed and high torque in a given system.This study proposes an explosive electric actuator and an associated control method for legged robots.First,a high-power-density variable transmission is designed to enable continuous adjustment of the output speed to torque ratio.A heat-dissipating structure based on a composite phase-change material(PCM)is used.An integral torque control method is used to achieve periodic and controllable explosive power output.Jumping experiments are conducted with typical legged robots to verify the effectiveness of the proposed actuator and control method.Single-legged,quadruped,and humanoid robots jumped to heights of 1.5,0.8,and 0.5 m,respectively.These are the highest values reported to date for legged robots powered by electric actuators.

Ground substrates classification and adaptive walking through interaction dynamics for legged robots

Adaptive locomotion in different types of surfaces is of critical importance for legged robots.The knowledge of various ground substrates,especially some geological properties,plays an essential role in ensuring the legged robots'safety.In this paper,the interaction between the robots and the environments is investigated through interaction dynamics with the closed-loop system model,the compliant contact model,and the friction model,which unveil the influence of environment's geological characteristics for legged robots'locomotion.The proposed method to classify substrates is based on the interaction dynamics and the sensory-motor coordination.The foot contact forces,joint position errors,and joint motor currents,which reflect body dynamics,are measured as the sensing variables.We train and classify the features extracted from the raw data with a multilevel weighted k-Nearest Neighbor(kNN) algorithm.According to the interaction dynamics,the strategy of adaptive walking is developed by adjusting the touchdown angles and foot trajectories while lifting up and dropping down the foot.Experiments are conducted on five different substrates with quadruped robot FROG-I.The comparison with other classification methods and adaptive walking between different substrates demonstrate the effectiveness of our approach.

An online terrain classification framework for legged robots based on acoustic signals

Terrain classification information is of great significance for legged robots to traverse various terrains.Therefore,this communication presents an online terrain classification framework for legged robots,utilizing the acoustic signals produced during locomotion.The Mel-Frequency Cepstral Coefficient(MFCC)feature vectors are extracted from the acoustic data recorded by an on-board microphone.Then the Gaussian mixture models(GMMs)are used to classify the MFCC features into different terrain type categories.The proposed framework was validated on a quadruped robot.Overall,our investigations achieved a classification time-resolution of 1 s when the robot trotted over three kinds of terrains,thus recording a comprehensive success rate of 92.7%.

Inverted Modelling:An Effective Way to Support Motion Planning of Legged Mobile Robots

This paper presents an effective way to support motion planning of legged mobile robots—Inverted Modelling,based on the equivalent metamorphic mechanism concept.The difference from the previous research is that we herein invert the equivalent parallel mechanism.Assuming the leg mechanisms are hybrid links,the body of robot being considered as fixed platform,and ground as moving platform.The motion performance is transformed and measured in the body frame.Terrain and joint limits are used as input parameters to the model,resulting in the representation which is independent of terrains and particular poses in Inverted Modelling.Hence,it can universally be applied to any kind of legged robots as global motion performance framework.Several performance measurements using Inverted Modelling are presented and used in motion performance evaluation.According to the requirements of actual work like motion continuity and stability,motion planning of legged robot can be achieved using different measurements on different terrains.Two cases studies present the simulations of quadruped and hexapod robots walking on rugged roads.The results verify the correctness and effectiveness of the proposed method.

Footholds optimization for legged robots walking on complex terrain

This paper proposes a novel continuous footholds optimization method for legged robots to expand their walking ability on complex terrains.The algorithm can efficiently run onboard and online by using terrain perception information to protect the robot against slipping or tripping on the edge of obstacles,and to improve its stability and safety when walking on complex terrain.By relying on the depth camera installed on the robot and obtaining the terrain heightmap,the algorithm converts the discrete grid heightmap into a continuous costmap.Then,it constructs an optimization function combined with the robot’s state information to select the next footholds and generate the motion trajectory to control the robot’s locomotion.Compared with most existing footholds selection algorithms that rely on discrete enumeration search,as far as we know,the proposed algorithm is the first to use a continuous optimization method.We successfully implemented the algorithm on a hexapod robot,and verified its feasibility in a walking experiment on a complex terrain.

Smart Gait:A Gait Optimization Framework for Hexapod Robots

The current gait planning for legged robots is mostly based on human presets,which cannot match the flexible characteristics of natural mammals.This paper proposes a gait optimization framework for hexapod robots called Smart Gait.Smart Gait contains three modules:swing leg trajectory optimization,gait period&duty optimization,and gait sequence optimization.The full dynamics of a single leg,and the centroid dynamics of the overall robot are considered in the respective modules.The Smart Gait not only helps the robot to decrease the energy consumption when in locomotion,mostly,it enables the hexapod robot to determine its gait pattern transitions based on its current state,instead of repeating the formalistic clock-set step cycles.Our Smart Gait framework allows the hexapod robot to behave nimbly as a living animal when in 3D movements for the first time.The Smart Gait framework combines offline and online optimizations without any fussy data-driven training procedures,and it can run efficiently on board in real-time after deployment.Various experiments are carried out on the hexapod robot LittleStrong.The results show that the energy consumption is reduced by 15.9%when in locomotion.Adaptive gait patterns can be generated spontaneously both in regular and challenge environments,and when facing external interferences.

Kinematic Calibration of a Six-Legged Walking Machine Tool

This paper presents the kinematic calibration of a novel six-legged walking machine tool comprising a six-legged mobile robot integrated with a parallel manipulator on the body.Each leg of the robot is a 2-universal-prismatic-spherical(UPS)and UP parallel mechanism,and the manipulator is a 6-PSU parallel mechanism.The error models of both subsystems are derived according to their inverse kinematics.The objective function for each kinematic limb is formulated as the inverse kinematic residual,i.e.,the deviation between the actual and computed joint coordinates.The hip center of each leg is first identified via sphere fitting,and the other kinematic parameters are identified by solving the objective function for each limb individually using the least-squares method.Thus,the kinematic parameters are partially decoupled,and the complexities of the error models are reduced.A calibration method is proposed for the legged robot to overcome the lack of a fixed base on the ground.A calibration experiment is conducted to validate the proposed method,where a laser tracker is used as the measurement equipment.The kinematic parameters of the entire robot are identified,and the motion accuracy of each leg and that of the manipulator are significantly improved after calibration.Validation experiments are performed to evaluate the positioning and trajectory errors of the six-legged walking machine tool.The results indicate that the kinematic calibration of the legs and manipulator improves not only the motion accuracy of each individual subsystem but also the cooperative motion accuracy among the subsystems.

Posture Control of Legged Locomotion Based on Virtual Pivot Point Concept

This paper presents a novel control approach for achieving robust posture control in legged locomotion,specifically for SLIP-like bipedal running and quadrupedal bounding with trunk stabilization.The approach is based on the virtual pendulum concept observed in human and animal locomotion experiments,which redirects ground reaction forces to a virtual support point called the Virtual Pivot Point(VPP)during the stance phase.Using the hybrid averaging theorem,we prove the upright posture stability of bipedal running with a fixed VPP position and propose a VPP angle feedback controller for online VPP adjustment to improve performance and convergence speed.Additionally,we present the first application of the VPP concept to quadrupedal posture control and design a VPP position feedback control law to enhance robustness in quadrupedal bounding.We evaluate the effectiveness of the proposed VPP-based controllers through various simulations,demonstrating their effectiveness in posture control of both bipedal running and quadrupedal bounding.The performance of the VPP-based control approach is further validated through experimental validation on a quadruped robot,SCIT Dog,for stable bounding motion generation at different forward speeds.

Type Synthesis of Walking Robot Legs

Walking robots use leg structures to overcome obstacles or move on complicated terrains. Most robots of current researches are equipped with legs of simple structure. The specific design method of walking robot legs is seldom studied. Based on the generalized-function(GF) set theory, a systematic type synthesis process of designing robot legs is introduced. The specific mobility of robot legs is analyzed to obtain two main leg types as the goal of design.Number synthesis problem is decomposed into two stages, actuation and constraint synthesis by name,corresponding to the combinatorics results of linear Diophantine equations. Additional restrictions are discussed to narrow the search range to propose practical limb expressions and kinematic-pair designs. Finally, all the fifty-one leg structures of four subtypes are carried out, some of which are chosen to make up robot prototypes, demonstrating the validity of the method. This paper proposed a novel type synthesis methodology, which could be used to systematically design various practical robot legs and the derived robots.

A desired landing points walking method enablinga planner quadruped robot to walk on rough terrain

It is necessary for legged robots to walk stably and smoothly on rough terrain.In this paper,a desired landing points(DLP) walking method based on preview control was proposed in which an off-line foot motion trace and an on-line modification of the trace were used to enable the robot to walk on rough terrain.The on-line modification was composed of speed modification,foot lifting-off height modification,step length modification,and identification and avoidance of unsuitable landing terrain.A planner quadruped robot simulator was used to apply the DLP walking method.The correctness of the method was proven by a series of simulations using the Adams and Simulink.

Sampling visual SLAM with a wide-angle camera for legged mobile robots

Precise localisation and navigation are the two most important tasks for mobile robots.Visual simultaneous localisation and mapping(VSLAM)is useful in localisation systems of mobile robots.The wide-angle camera has a broad field of vision and more abundant information on images,so it is widely used in mobile robots,including legged robots.However,wide-angle cameras are more complicated than ordinary cameras in the design of visual localisation systems,and higher requirements and challenges are put forward for VSLAM technologies based on wide-angle cameras.In order to resolve the problem of distortion in wide-angle images and improve the accuracy of localisation,a sampling VSLAM based on a wide-angle camera model for legged mobile robots is proposed.For the predictability of the periodic motion of a legged robot,in the method,the images are sampled periodically,image blocks with clear texture are selected and the image details are enhanced to extract the feature points on the image.Then,the feature points of the blocks are extracted and by using the feature points of the blocks in the images,the feature points on the images are extracted.Finally,the points on the incident light through the normalised plane are selected as the template points;the relationship between the template points and the images is established through the wide-angle camera model,and the pixel coordinates of the template points in the images and the descriptors are calculated.Moreover,many experiments are conducted on the TUM datasets with a quadruped robot.The experimental results show that the trajectory error and translation error measured by the proposed method are reduced compared with the VINS-MONO,ORB-SLAM3 and Periodic SLAM systems.

A novel six-legged walking machine tool for in-situ operations

The manufacture and maintenance of large parts in ships,trains,aircrafts,and so on create an increasing demand for mobile machine tools to perform in-situ operations.However,few mobile robots can accommodate the complex environment of industrial plants while performing machining tasks.This study proposes a novel six-legged walking machine tool consisting of a legged mobile robot and a portable parallel kinematic machine tool.The kinematic model of the entire system is presented,and the workspace of different components,including a leg,the body,and the head,is analyzed.A hierarchical motion planning scheme is proposed to take advantage of the large workspace of the legged mobile platform and the high precision of the parallel machine tool.The repeatability of the head motion,body motion,and walking distance is evaluated through experiments,which is 0.11,1.0,and 3.4 mm,respectively.Finally,an application scenario is shown in which the walking machine tool steps successfully over a 250 mmhigh obstacle and drills a hole in an aluminum plate.The experiments prove the rationality of the hierarchical motion planning scheme and demonstrate the extensive potential of the walking machine tool for in-situ operations on large parts.

A new control for the pneumatic muscle bionic legged robot based on neural network

The bionic joints composed of pneumatic muscles(PMs)can simulate the motion of biological joints.However,the PMs themselves have non-linear characteristics such as hysteresis and creep,which make it difficult to achieve high-precision trajectory tracking control of PM-driven robots.In order to effectively suppress the adverse effects of non-linearity on control performance and improve the dynamic performance of PM-driven legged robot,this study designs a double closed-loop control structure based on neural network.First,according to the motion model of the bionic joint,a mapping model between PM contraction force and joint torque is proposed.Second,a control strategy is designed for the inner loop of PM contraction force and the outer loop of bionic joint angle.In the inner control loop,a feedforward neuron Proportional-Integral-Derivative controller is designed based on the PM three-element model.In the control outer loop,a sliding mode robust controller with local model approximation is designed by using the radial basis function neural network approximation capability.Finally,it is verified by simulation and physical experiments that the designed control strategy is suitable for humanoid motion control of antagonistic PM joints,and it can satisfy the requirements of reliability,flexibility,and bionics during human–robot collaboration.

Limb Stiffness Improvement of the Robot WAREC-1R for a Faster and Stable New Ladder Climbing Gait

Ladder climbing is a relatively new but practical locomotion style for robots. Unfortunately, due to their size and weight, ladder climbing by human-sized robots developed so far is struggling with the speedup of ladder climbing motion itself. Therefore, in this paper, a new ladder climbing gait for the robot WAREC-1R is proposed by the authors, which is both faster than the former ones and stable. However, to realize such a gait, a point that has to be taken into consideration is the deformation caused by the self-weight of the robot. To deal with this issue, extra hardware (sensor) and software (position and force control) systems and extra time for sensing and calculation were required. For a complete solution without any complicated systems and time only for deformation compensation, limb stiffness improvement plan by the minimal design change of mechanical parts of the robot is also proposed by the authors, with a thorough study about deformation distribution in the robot. With redesigned parts, ladder climbing experiments by WAREC-1R proved that both the new ladder climbing gait and the limb stiffness improvement are successful, and the reduced deformation is very close to the estimated value as well.

Design and Experiments of a Human-Leg-Inspired Omnidirectional Robotic Leg

Bionic-based robotic legs enable the legged robots with elegant and agile mobility in multi-terrain environment,just like natural living beings.And the smart design could efficiently improve the performance of a robotic leg.Inspired by the simplified human leg structure,we present a 3-DOF robotic leg—OmniLeg,that is capable of making omnidirectional legged locomotion while keeping constant posture of the foot.Additionally,the concentrated drive mode,in which all the motor actuators are installed in the torso and do not move with the leg,minimizes the inertia of the robotic leg.In this paper,the modular design,the kinematics model,the structural analysis,the workspace,and the performance evaluation of the OmniLeg are discussed.Furthermore,we build a prototype based on the proposed design,and the precision of it is verified by the error calibration experiment which is conducted by tracking the trajectory of the prototype’s endpoint.Then,we present an OmniLeg-based single legged mobile robot.The capability of omnidirectional legged locomotion of the OmniLeg is demonstrated by the experiments.

Biomimetic Quadruped Robot with a Spinal Joint and Optimal Spinal Motion via Reinforcement Learning

Feline animals can run quickly using spinal joints as well as the joints that make up their four legs.This paper describes the development of a quadruped robot including a spinal joint that biomimics feline animals.The developed robot platform consists of four legs with a double 4-bar linkage type and one simplified rotary joint.In addition,Q-learning,a type of machine learning,was used to find the optimal motion profile of the spinal joint.The bounding gait was implemented on the robot system using the motion profile of the spinal joint,and it was confirmed that using the spinal joint can achieve a faster Center of Mass(CoM)forward speed than not using the spinal joint.Although the motion profile obtained through Q-learning did not exactly match the spinal angle of a feline animal,which is more multiarticular than that of the developed robot,the tendency of the actual feline animal spinal motion profile,which is sinusoidal,was similar.

Sagittal SLIP-anchored task space control for a monopode robot traversing irregular terrain

As a well-explored template that captures the essential dynamical behaviors of legged locomotion on sagittal plane,the spring-loaded inverted pendulum(SLIP)model has been extensively employed in both biomechanical study and robotics research.Aiming at fully leveraging the merits of the SLIP model to generate the adaptive trajectories of the center of mass(CoM)with maneuverability,this study presents a novel two-layered sagittal SLIP-anchored(SSA)task space control for a monopode robot to deal with terrain irregularity.This work begins with an analytical investigation of sagittal SLIP dynamics by deriving an approximate solution with satisfactory apex prediction accuracy,and a two-layered SSA task space controller is subsequently developed for the monopode robot.The higher layer employs an analytical approximate representation of the sagittal SLIP model to form a deadbeat controller,which generates an adaptive reference trajectory for the CoM.The lower layer enforces the monopode robot to reproduce a generated CoM movement by using a task space controller to transfer the reference CoM commands into joint torques of the multi-degree of freedom monopode robot.Consequently,an adaptive hopping behavior is exhibited by the robot when traversing irregular terrain.Simulation results have demonstrated the effectiveness of the proposed method.

Design and control of a robotic system with legs,wheels,and a reconfigurable arm

Unmanned robotic systems are expected to liberate people from heavy,monotonous,and dangerous work.However,it is still difficult for robots to work in complicated environments and handle diverse tasks.To this end,a robotic system with four legs,four wheels,and a reconfigurable arm is designed.Special attention has been given to making the robot compact,agile,and versatile.Firstly,by using separate wheels and legs,it removes the coupling in the traditional wheeled–legged system and guarantees highly efficient locomotion in both the wheeled and legged modes.Secondly,a leg–arm reconfiguration design is adopted to extend the manipulation capability of the system,which not only reduces the total weight but also allows for dexterous manipulation and multi-limb cooperation.Thirdly,a multi-task control strategy based on variable configurations is proposed for the system,which greatly enhances the adaptability of the robot to complicated environments.Experimental results are given,which validate the effectiveness of the system in mobility and operation capability.

Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.