Adaptive dynamic walking of a quadruped robot using a neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex and extensor/flexor reflexes, into a central pattern generator (CPG). We try to realize adaptive walking up and down a slope of 12°, walking over an obstacle 3 cm in height, and walking on terrain undulation consisting of bumps 3 cm in height with fixed parameters of CPGs and reflexes. The success in walking on such irregular terrain in spite of stumbling and landing on obstacles shows that the control method using a neural system model proposed in this study has the ability for autonomous adaptation to unknown irregular terrain. In order to clarify the role of a CPG, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. CPGs can generate stable walking suitable for the mechanical system by receiving inhibitory input as sensory feedback and generate adaptive walking on irregular terrain by receiving excitatory input as sensory feedback. MPEG footage of these experiments can be seen at: http://www.kimura.is.uec.ac.jp.     

Online Walking Pattern Generation and Its Application to a Biped Humanoid Robot — KHR-3 (HUBO)

The authors propose a simple on-line method for generating a walking pattern for the biped humanoid robot KHR-3 (HUBO). The problem of realizing a walking action in humanoid robots involves two components: generation of the basic walking pattern and the compensation required to maintain the robot's balance. Dynamic walking can be realized by incorporating the real-time stabilizing control algorithm developed for KHR-1, KHR-2 and KHR-3. The walking pattern of KHR-3 has four modes: forward/backward, left/right, curved walking and turning around. In the previous pattern generation of the KHR series, the step time and stride of the robot were fixed, and the walking modes, step time and action of stride without stopping could not be changed. Hence, the flexibility of the walking pattern of the robot needed to be upgraded. The walking pattern in this paper allows variation in the walking mode, step time and stride for each step. The pattern uses a simple mathematical form of trajectory curves, specifically the sine, cosine, linear and third-order polynomial curves, and the superposition of these curves is used to minimize the complexity and burden of the computation. The authors used a third-order polynomial to generate the trajectory of the robot's pelvis. With the aid of a simplified zero-moment point (ZMP) equation, the pelvis trajectories have a direct relationship with the ZMP trajectories. An effective means of generating the trajectories is introduced, and the scheme is verified experimentally under various walking conditions that take into account the step time and stride. The experimental platform, which has human-like features and movement, is briefly introduced here. With a simple kinematical structure and distributed control hardware architecture, the platform was designed to consume relatively low levels of energy. Moreover, the scheme for generating the trajectory is realized for variations to flexible walking.     

Scalable Symmetry Detection for Urban Scenes

In this paper, we present a novel method for detecting partial symmetries in very large point clouds of 3D city scans. Unlike previous work, which has only been demonstrated on data sets of a few hundred megabytes maximum, our method scales to very large scenes: We map the detection problem to a nearest‐neighbour problem in a low‐dimensional feature space, and follow this with a cascade of tests for geometric clustering of potential matches. Our algorithm robustly handles noisy real‐world scanner data, obtaining a recognition performance comparable to that of state‐of‐the‐art methods. In practice, it scales linearly with scene size and achieves a high absolute throughput, processing half a terabyte of scanner data overnight on a dual socket commodity PC.     

Incorporation of new technique for processing of Al/SiCp composites based on evaporative pattern casting (EPC) method

Abstract

A336 Al matrix composites containing different volume fraction and mean mass particle size of SiC particles as the reinforcing phase were synthesised by evaporative pattern casting (EPC) route. The process consisted of fabricating of EPS/SiC_p composite pattern followed by EPC of A336 Al alloy. The EPS/SiC_p pattern was made by blending SiC particles with expandable polystyrene (EPS) beads and placing them in expanding mould heating with steam until EPS beads expand completely. Uniform distributed SiC particles around the EPS beads and locally movement of them during pouring and degradation leads to homogenous distribution of particles in final Al/SiC_p composite. Higher modulus, strength and hardness were observed in the composites than the unreinforced Al alloy part. The fracture surfaces of the composite samples exhibited dimple surfaces and fracture in SiC particles.     

Stabilization control for biped follow walking

This paper proposes a follow-walking motion for biped humanoid robots based on a stabilization control and a complete walking-motion pattern. To follow human motion, the unit patterns of the trunk, the waist and the lower limbs are generated and synthesized. During the follow motion, the biped robots are balanced by the stabilization control that calculates the combined motion of the trunk and the waist that compensates for the moments produced by the motion of the lower limbs. For confirmation of the follow-walking motion, we have developed a life-sized humanoid robot, WABIAN-RII (WAseda BIped humANoid robot-Revised II). It has a total of 43 mechanical d.o.f.; two 6-d.o.f. legs, two 10-d.o.f. arms, a 4-d.o.f. neck, 4-d.o.f. in the eyes and a torso with a 3-d.o.f. waist.     

Location‐dependent generalization of road networks based on equivalent destinations

Suppose a user located at a certain vertex in a road network wants to plan a route using a wayfinding map. The user's exact destination may be irrelevant for planning most of the route, because many destinations will be equivalent in the sense that they allow the user to choose almost the same paths. We propose a method to find such groups of destinations automatically and to contract the resulting clusters in a detailed map to achieve a simplified visualization. We model the problem as a clustering problem in rooted, edge‐weighted trees. Two vertices are allowed to be in the same cluster if and only if they share at least a given fraction of their path to the root. We analyze some properties of these clusterings and give a linear‐time algorithm to compute the minimum‐cardinality clustering. This algorithm may have various other applications in network visualization and graph drawing, but in this paper we apply it specifically to focus‐and‐context map generalization. When contracting shortest‐path trees in a geographic network, the computed clustering additionally provides a constant‐factor bound on the detour that results from routing using the generalized network instead of the full network. This is a desirable property for wayfinding maps.     

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis–patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis–patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle–foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern.     

Neural network face detection

Abstract

Analysing a video sequence is a challenge, because faces are constantly in dynamic motion, presenting many different possible rotational and illumination conditions. While solutions to the task of face detection have been presented, the detection performances of many systems are heavily dependent upon a strictly constrained environment. This paper presents the results of an image-based neural network face detection system which seeks to address the problem of detecting faces under gross variations.     

Simulating Adaptive Human Bipedal Locomotion Based on Phase Resetting Using Foot-Contact Information

Humans generate bipedal walking by cooperatively manipulating their complicated and redundant musculoskeletal systems to produce adaptive behaviors in diverse environments. To elucidate the mechanisms that generate adaptive human bipedal locomotion, we conduct numerical simulations based on a musculoskeletal model and a locomotor controller constructed from anatomical and physiological findings. In particular, we focus on the adaptive mechanism using phase resetting based on the foot-contact information that modulates the walking behavior. For that purpose, we first reconstruct walking behavior from the measured kinematic data. Next, we examine the roles of phase resetting on the generation of stable locomotion by disturbing the walking model. Our results indicate that phase resetting increases the robustness of the walking behavior against perturbations, suggesting that this mechanism contributes to the generation of adaptive human bipedal locomotion.     

Experimental realization of dynamic walking of the biped humanoid robot KHR-2 using zero moment point feedback and inertial measurement

This paper describes a novel control algorithm for dynamic walking of biped humanoid robots. For the test platform, we developed KHR-2 (KAIST Humanoid Robot-2) according to our design philosophy. KHR-2 has many sensory devices analogous to human sensory organs which are particularly useful for biped walking control. First, for the biped walking motion, the motion control architecture is built and then an appropriate standard walking pattern is designed for the humanoid robots by observing the human walking process. Second, we define walking stages by dividing the walking cycle according to the characteristics of motions. Third, as a walking control strategy, three kinds of control schemes are established. The first scheme is a walking pattern control that modifies the walking pattern periodically based on the sensory information during each walking cycle. The second scheme is a real-time balance control using the sensory feedback. The third scheme is a predicted motion control based on a fast decision from the previous experimental data. In each control scheme, we design online controllers that are capable of maintaining the walking stability with the control objective by using force/torque sensors and an inertial sensor. Finally, we plan the application schedule of online controllers during a walking cycle according to the walking stages, accomplish the walking control algorithm and prove its effectiveness through experiments with KHR-2.