BALANCE MOTION PLANNING FOR HUMANOID ROBOT BASED ON 3D INVERTED PENDULUM MODEL

Researches on humanoid robots are alway attractive to many researchers in robotics field. One of considerable challenges of humanoid robots is to keep balance and stability of their movement. Because a humanoid robot moves by two legs, most of time of the step period of the humanoid robot is be in one leg touching on the floor and the other leg swinging forward. This posture is similar to a three dimension (3D) inverted pendulum model. This papers presents the dynamic model of a 3D inverted pendulum model and applies to balanced motion planning for a humanoid robot. The obtained results show that the robot is able to keep balance during its movements

Head-Mounted and Hand-Held Displays Diminish the Effectiveness of Fall-Resisting Skills

Use of head-mounted displays (HMDs) and hand-held displays (HHDs) may affect the effectiveness of stability control mechanisms and impair resistance to falls. This study aimed to examine whether the ability to control stability during locomotion is diminished while using HMDs and HHDs. Fourteen healthy adults (21–46 years) were assessed under single-task (no display) and dual-task (spatial 2-n-back presented on the HMD or the HHD) conditions while performing various locomotor tasks. An optical motion capture system and two force plates were used to assess locomotor stability using an inverted pendulum model. For perturbed standing, 57% of the participants were not able to maintain stability by counter-rotation actions when using either display, compared to the single-task condition. Furthermore, around 80% of participants (dual-task) compared to 50% (single-task) showed a negative margin of stability (i.e., an unstable body configuration) during recovery for perturbed walking due to a diminished ability to increase their base of support effectively. However, no evidence was found for HMDs or HHDs affecting stability during unperturbed locomotion. In conclusion, additional cognitive resources required for dual-tasking, using either display, are suggested to result in delayed response execution for perturbed standing and walking, consequently diminishing participants’ ability to use stability control mechanisms effectively and increasing the risk of falls.

Stabilized Walking of Humanoid NAO using Enhanced Spring-Loaded Inverted Pendulum Model on Uneven Terrain

In the coming decades, humanoid robots will play a rising role in society. The present article discusses their walking control and obstacle avoidance on uneven terrain using enhanced spring-loaded inverted pendulum model (ESLIP). The SLIP model is enhanced by tuning it with an adaptive particle swarm optimization (APSO) approach. It helps the humanoid robot to reach closer to the obstacles in order to optimize the turning angle to optimize the path length. The desired trajectory, along with the sensory data, is provided to the SLIP model, which creates compatible COM (center of mass) dynamics for stable walking. This output is fed to APSO as input, which adjusts the placement of the foot during interaction with uneven surfaces and obstacles. It provides an optimum turning angle for shunning the obstacles and ensures the shortest path length. Simulation has been carried out in a 3D simulator based on the proposed controller and SLIP controller in uneven terrain.

A Model of Predictive Postural Control Against Floor Tilting in Rats

Humans and animals learn the internal model of bodies and environments from their experience and stabilize posture against disturbances based on the predicted future states according to the internal model. We evaluated the mechanism of predictive control during standing, by using rats to construct a novel experimental system and comparing their behaviors with a mathematical model. In the experiments, rats (n = 6) that were standing upright using their hindlimbs were given a sensory input of light, after a certain period, the floor under them tilted backward. Initially, this disturbance induced a large postural response, including backward rotation of the center-of-mass angle and hindlimb segments. However, the rats gradually adjusted to the disturbance after experiencing 70 sequential trials, and a reduction in the amplitude of postural response was noted. We simulated the postural control of the rats under disturbance using an inverted pendulum model and model predictive control (MPC). MPC is a control method for predicting the future state using an internal model of the control target. It provides control inputs that optimize the predicted future states. Identification of the predictive and physiological parameters so that the simulation corresponds to the experiment, resulted in a value of predictive horizon (0.96 s) close to the interval time in the experiment (0.9–1.15 s). These results suggest that the rats predict posture dynamics under disturbance based on the timing of the sensory input and that the central nervous system provides plasticity mechanisms to acquire the internal model for MPC.

Balance Recoverability and Control of Bipedal Walkers with Foot Slip

Low-friction foot/ground contacts present a particular challenge for stable bipedal walkers. The slippage of the stance foot introduces complexity in robot dynamics and the general locomotion stability results cannot be applied directly. We relax the commonly used assumption of non-slip contact between the walker foot and the ground and examine bipedal dynamics under foot slip. Using a two-mass linear inverted pendulum model, we introduce the concept of balance recoverability and use it to quantify the balanced or fall-prone walking gaits. Balance recoverability also serves as the basis for the design of the balance recovery controller. We design the within- or multi-step recovery controller to assist the walker to avoid fall. The controller performance is validated through simulation results and robustness is demonstrated in the presence of measurement noises as well as variations of foot/ground friction conditions. In addition, the proposed methods and models are used to analyze the data from human walking experiments. The multiple subject experiments validate and illustrate the balance recoverability concept and analyses.

Stable jump control for the wheel-legged robot based on TMS-DIP model

Purpose The purpose of this paper is to propose a novel jump control method based on Two Mass Spring Damp Inverted Pendulum (TMS-DIP) model, which makes the third generation of hydraulic driven wheel-legged robot prototype (WLR-3P) achieve stable jumping. Design/methodology/approach First, according to the configuration of the WLR, a TMS-DIP model is proposed to simplify the dynamic model of the robot. Then the jumping process is divided into four stages: thrust, ascent, descent and compression, and each stage is modeled and solved independently based on TMS-DIP model. Through WLR-3P kinematics, the trajectory of the upper and lower centroids of the TMS-DIP model can be mapped to the joint space of the robot. The corresponding control strategies are proposed for jumping height, landing buffer, jumping attitude and robotic balance, so as to realize the stable jump control of the WLR. Findings The TMS-DIP model proposed in this paper can simplify the WLR dynamic model and provide a simple and effective tool for the jumping trajectory planning of the robot. The proposed approach is suitable for hydraulic WLR jumping control. The performance of the proposed wheel-legged jump method was verified by experiments on WLR-3P. Originality/value This work provides an effective model (TMS-DIP) for the jump control of WLR-3P. The results showed that the number of landing shock (twice) and the pitch angle fluctuation range (0.44 rad) of center of mass of the jump control method based on TMS-DIP model are smaller than those based on spring-loaded inverted pendulum model. Therefore, the TMS-DIP model makes the jumping process of WLR more stable and gentler.