Kosuke Sekiyama

Sliding-Mode Velocity Control of Mobile-Wheeled Inverted-Pendulum Systems

There has been increasing interest in a type of underactuated mechanical systems, mobile-wheeled inverted-pendulum (MWIP) models, which are widely used in the field of autonomous robotics and intelligent vehicles. Robust-velocity-tracking problem of the MWIP systems is investigated in this study. In the velocity-control problem, model uncertainties accompany uncertain equilibriums, which make the controller design become more difficult. Two sliding-mode-control (SMC) methods are proposed for the systems, both of which are capable of handling both parameter uncertainties and external disturbances. The asymptotical stabilities of the corresponding closed-loop systems are achieved through the selection of sliding-surface parameters, which are based on some rules. There is still a steady tracking error when the first SMC controller is used. By assuming a novel sliding surface, the second SMC controller is designed to solve this problem. The effectiveness of the proposed methods is finally confirmed by the numerical simulations.

Human-Walking-Intention-Based Motion Control of an Omnidirectional-Type Cane Robot

An intelligent cane robot is designed for aiding the elderly and handicapped peoples walking. The robot consists of a stick, a group of sensors, and an omnidirectional basis driven by three Swedish wheels. Recognizing the users walking intention plays an important role in the motion control of our cane robot. To quantitatively describe the users walking intention, a concept called “intentional direction (ITD)” is proposed. Both the state model and the observation model of ITD are obtained by enumerating the possible walking modes and analyzing the relationship between the human-robot interaction force and the walking intention. From these two models, the users walking intention can be online inferred using the Kalman filtering technique. Based on the estimated intention, a new admittance motion control scheme is proposed for the cane robot. Walking experiments aided by the cane robot on a flat ground and slope are carried out to validate the proposed control approach. The experimental results show that the user feels more natural and comfortable when our intention-based admittance control is applied.

Stabilizing and Direction Control of Efficient 3-D Biped Walking Based on PDAC

This paper proposes a 3-D biped dynamic walking algorithm based on passive dynamic autonomous control (PDAC). The robot dynamics is modeled as an autonomous system of a 3-D inverted pendulum by applying the PDAC concept that is based on the assumption of point contact of the robot foot and the virtual constraint as to robot joints. Due to autonomy, there are two conservative quantities named ldquoPDAC constant,rdquo which determine the velocity and direction of the biped walking. We also propose the convergence algorithm to make PDAC constants converge to arbitrary values, so that walking velocity and direction are controllable. Finally, experimental results validate the performance and the energy efficiency of the proposed algorithm.

A PSO-based Mobile Sensor Network for Odor Source Localization in Dynamic Environment: Theory, Simulation and Measurement

This paper presents a problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Most work on chemical sensing with mobile robots assume an experimental setup that minimizes the influence of turbulent transport by either minimizing the source-to-sensor distance in trail following or by assuming a strong unidirectional air stream in the environment, including our previous work. However, not much attention has been paid to the natural environment problem. Modification Particle Swarm Optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charged PSO, which is another extension of the PSO, has also been applied to solve dynamic problems. Odor source localization is an interesting application in dynamic problems. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles. Before applying the algorithm for real implementation, some important hardware conditions must be considered. Firstly, to reduce the possibility of robots leaving the search space, a limit to the value of velocity vector is needed. The value of vector velocity can be clamped to the range [-Vmax, Vmax]; in our case for the MK-01 Robot, the maximum velocity is 0.05 m/s. Secondly, in the standard PSO algorithm there is no collision avoidance mechanism. To avoid the collision among robot we add some collision avoidance functions. Finally, we also add some sensor noise, delay and threshold value to model the sensor response. Then we develop odor localization algorithm, and simulations to show that the new approach can solve such dynamic environment problems.

Vertical ladder climbing motion with posture control for multi-locomotion robot

This paper introduces a vertical ladder climbing of the humanoid robot only by the posture control without any external sensors. The humanoid robot does not have any special structure for fixing the body to the ladder. The robot maintains the body on the ladder by its grippers like human does. As a problem of this locomotion, a free gripper position of the climbing robot is not controllable because a yawing of the robot body around the axis connecting a supporting gripper and foot on the ladder is not fixed. To solve this problem, the momentum around AOY caused by the gravity is used to control the yaw motion of the body so that the various gait such as pace gait and trot gait could be realized in a ladder climbing maneuver. The algorithm of ladder climbing with recovery motion is experimentally verified by using ldquomulti-locomotion robot(MLR)rdquo which is developed to achieve various types of locomotion such as biped, quadruped walking and brachiation.

Multi-Locomotion Robotic Systems

Nowadays, multiple attention have been paid on a robot working in the human living environment, such as in the field of medical, welfare, entertainment and so on. Various types of researches are being conducted actively in a variety of fields such as artificial intelligence, cognitive engineering, sensor- technology, interfaces and motion control. In the future, it is expected to realize super high functional human-like robot by integrating technologies in various fields including these types of researches. The book represents new developments and advances in the field of bio-inspired robotics research introducing the state of the art, the idea of multi-locomotion robotic system to implement the diversity of animal motion. It covers theoretical and computational aspects of Passive Dynamic Autonomous Control (PDAC), robot motion control, multi legged walking and climbing as well as brachiation focusing concrete robot systems, components and applications. In addition, gorilla type robot systems are described as hardware of Multi-Locomotion Robotic system. It is useful for students and researchers in the field of robotics in general, bio-inspired robots, multi-modal locomotion, legged walking, motion control, and humanoid robots. Furthermore, it is also of interest for lecturers and engineers in practice building systems cooperating with humans.

Distributed odor source localization in dynamic environment

This paper addresses the problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Modification particle swarm optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charge PSO, which is another extension of the PSO has also been applied to solve dynamic problem. Odor source localization is an interesting application in dynamic problem. We adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles. Then we develop odor localization algorithm, and simulations to show that the new approach can solve such a kind of dynamic environment problem

Communication Timing Control and Topology Reconfiguration of a Sink-Free Meshed Sensor Network With Mobile Robots

This paper deals with a unified system of fully distributed meshed sensor network and mobile robot cooperation that serves as a sink node. The meshed sensor network in this paper is composed of static wireless nodes, and is capable of fully distributed peer-to-peer (P2P) ad hoc communication with ZigBee-based protocol. A novel communication timing control employing coupled-oscillator dynamics, named phase-diffusion time-division method (PDTD), has been proposed so far, aiming at realization of an ad hoc collision-free wireless communication network. In this paper, we extend the basic PDTD so that it can exhibit flexible topological reconfiguration according to the moving sink node (robot). Unlike conventional sensor network, no static sink node is supposed inside the network; however, a mobile robot will function as a sink node and access the mesh network from an arbitrary position. A large-scale experiment was conducted, and its results show that satisfactory collaboration between the mesh sensor network and the mobile robot is achieved, and the proposed system outperformed the carrier-sense-multiple-access-based sensor system.

Study of Fall Detection Using Intelligent Cane Based on Sensor Fusion

A three-wheeled omni-directional cane robot is designed for aiding the elderly walking. A new human fall detection method is proposed based on fusing sensory information from a vision system and a laser ranger finder (LRF). This method plays an important role in the fall-prevention for the cane robot. The human fall model is represented in a 2D space, where the distance between the head and the average leg position is a significant feature to detect the fall. The possibility distribution of this distance is estimated by using Dubois possibility theory. Fall detection is implemented by using a simple rule based on the possibility distribution. The proposed method is confirmed through experiments.

A Mobile Robots PSO-based for Odor Source Localization in Dynamic Advection-Diffusion Environment

This paper presents a problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Odor source localization is an interesting application in dynamic problems. Modified particle swarm optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charged PSO which is another extension of the PSO has also been applied to solve dynamic problems. We adopted two types of modified concepts of PSO for a new algorithm in order to control autonomous vehicles in more realistic environment where a speed limitation of the robot behavior and collision avoidance mechanism should be taken into consideration as well as the effect of noise and threshold value for the odor sensor response, also positioning error of GPS sensor of robot. Simulations illustrate that the new approach can solve such dynamic problems in advection-diffusion odor model environment