Research On Key Technologies Of Motion Planning For Humanoid Robot Based On Similarity Locomotion Of Human Actor

The spatial mechanical structure of humanoid robot is similar to the skeletalstructure of body and limb of human being, which makes it walk with biped legsand deal with task by hands just like people. For this reason, the humanoid robothas been the important part of human-robot society. The motion planning ofhumanoid robot is the basis of both task executing and behavior deciding. Thereare two planning methods–based on the locomotion analytic equation and basedon the similarity locomotion of human actor. Although there are many advantagesin the method based on locomotion analytic equation such as its easily describ ingand analyzing characteristics of mathematics as well as smooth moving track, thenatural conversion of moving track, complicated motion design and energyconsumption optimization in this method are not as good as that one based on thesimilarity locomotion of human actor.The research is funded by the national863key project "Sports andentertainment multi-robot systems" and it mainly focuses on the key technologiesof motion planning for humanoid robot based on the similarity locomotion ofhuman actor, including keeping balance and following tracks in the biped walkingprocess, minimizing the collision from floor when falling down and recovering tothe similar pose of human actor after it, self adjusting the fixed tracks of humanactor on robot to fit the changeable environment. The main contents are as follow:Firstly, the basic model of similarity locomotion is built up, which isconstructed by three models---image capture and dealing, feature extraction,kinematics constraint and optimization. The forward kinematics and inversekinematics are analyzed as well as the simplfied locomotion model and motionretargeting. The similarity degree based on spatio-temporal control is defined. Themethods of key posture judging and extracting, movement coordinating andsynchronization, and the hierarchy tree structure of key posture are proposed. Thestability judging cristerias are compared. The collisions and causes in similaritylocomotion are analyzied and the corresponding methods such as the collision-freecontraints, landing leg compensation and joint angle control are also described aswell.Secondly, taking the7-link biped walking robot model as an example, thecontrol of keeping balance and following track are studied. The features ofkinametics and dynamics of biped walking are analyzied and the constraints ofkinematics, sub-phase connection and physical condition are set on the key poses and basic phases as well. The Lagrange dynamics equation based on joint torqueare established as well as the biped contacting-land equation through robotJacobian matrix. The dynamics model of humanoid robot is defined as the lineartime invariant system with continous time. The state feedback controller withobserver is used to follow the tracks of human actor. The balance control of bipedwalking is analyzied through the three dimensional inverted pendulum and thetarget function with minimum error of movement parameters is built through thelinear quadratic regulator with infinite time when following tracks.Thirdly, the protection and recovery of pose for humanoid robot are studedwhen the robot falls down. The dynamatics features of falling down for humanoidrobot are analyzed and the contraints of kinematics and physical conditions are setas well. When controlling the contacting position on ground, the parameterizedcontrol method is used to approach the optimal solution and the ehhancingtechnique is use to converse the differential quotient solving with original timepoint through index functional into the parameter acquirement through differentialquotient solving with new time point, which solves the optimizing problem offalling motion for humanoid robot and obtains the minimal floor collision, optimaltouching position and falling stability. After collision with floor, the geneticalgorithm with variable population is used to optimize the pose recovery ofsimilarity locomotion for humanoid robot.Finally, taking the stepping upstairs of humanoid robot as an example, theautonomously fixing tracks obtained from human actor on humanoid robot to fitthe variable target environment is studied. The constraints of kinematics andphysical conditions are set in the process of stepping upstairs for humanoid robot.The particle swarm optimization with hierarchical reinforcement learning isproposed, in which the essence particles are obtained through the crossingselection and the strategies of adjusting inertial weight are selected in the evolvingstrategies of particles. The parameters of stair width and height are obtainedthrough the embedded monocular vision. The tracks of knee and ankle joints onhumanoid robot are adjusted through the above method to fit the changeable targetenvironment.