Geunho Lee

Design and control of JAIST active robotic walker

This paper presents the design and control of a novel assistive robotic walker that we call “JAIST active robotic walker (JARoW)”. JARoW is developed to provide potential users with sufficient ambulatory capability in an efficient, cost-effective way. Specifically, our focus is placed on how to allow easier maneuverability by creating a natural interface between the user and JARoW. For the purpose, we develop a rotating infrared sensor to detect the user’s lower limb movement. The implementation details of the JARoW control algorithms based on the sensor measurements are explained, and the effectiveness of the proposed algorithms is verified through experiments. Our results confirmed that JARoW can autonomously adjust its motion direction and velocity according to the user’s walking behavior without requiring any additional user effort.

A geometric approach to deploying robot swarms

We discuss the fundamental problems and practical issues underlying the deployment of a swarm of autonomous mobile robots that can potentially be used to build mobile robotic sensor networks. For the purpose, a geometric approach is proposed that allows robots to configure themselves into a two-dimensional plane with uniform spatial density. Particular emphasis is paid to the hole repair capability for dynamic network reconfiguration. Specifically, each robot interacts selectively with two neighboring robots so that three robots can converge onto each vertex of the equilateral triangle configuration. Based on the local interaction, the self-configuration algorithm is presented to enable a swarm of robots to form a communication network arranged in equilateral triangular lattices by shuffling the neighbors. Convergence of the algorithms is mathematically proved using Lyapunov theory. Moreover, it is verified that the self-reparation algorithm enables robot swarms to reconfigure themselves when holes exist in the network or new robots are added to the network. Through extensive simulations, we validate the feasibility of applying the proposed algorithms to self-configuring a network of mobile robotic sensors. We describe in detail the features of the algorithm, including self-organization, self-stabilization, and robustness, with the results of the simulation.

Walking Intent-Based Movement Control for JAIST Active Robotic Walker

This paper presents a novel interactive control for our assistive robotic walker, the JAIST Active Robotic Walker (JARoW), developed for elderly people in need of assistance. The aim of our research is to recognize characteristics of the users gait and to generate the movement of JARoW accordingly. Specifically, the proposed control enables JARoW to accurately generate the direction and velocity of its movement in a way that corresponds to the users variable walking behaviors. The algorithm and implementation of the control are explained in detail, and the effectiveness and usability of JARoW are verified through extensive experiments in everyday environments.

Adaptive Flocking of Robot Swarms: Algorithms and Properties

This paper presents a distributed approach for adaptive flocking of swarms of mobile robots that enables to navigate autonomously in complex environments populated with obstacles. Based on the observation of the swimming behavior of a school of fish, we propose an integrated algorithm that allows a swarm of robots to navigate in a coordinated manner, split into multiple swarms, or merge with other swarms according to the environment conditions. We prove the convergence of the proposed algorithm using Lyapunov stability theory. We also verify the effectiveness of the algorithm through extensive simulations, where a swarm of robots repeats the process of splitting and merging while passing around multiple stationary and moving obstacles. The simulation results show that the proposed algorithm is scalable, and robust to variations in the sensing capability of individual robots.

Adaptive Flocking of a Swarm of Robots Based on Local Interactions

This paper presents a novel flocking strategy for a large-scale swarm of robots that enables the robots to navigate autonomously in an environment populated with obstacles. Robot swarms are often required to move toward a goal while adapting to changes in environmental conditions in many applications. Based on the observation of the swimming behavior of a school of tunas, we apply their unique patterns of behavior to the autonomous adaptation of the shape of robot swarms. Specifically, each robot dynamically selects two neighboring robots within its sensing range and maintains a uniform distance with them. This enables three neighboring robots to form a regular triangle and remain stable in the presence of obstacles. Therefore, the swarm can be split into multiple groups or re-united into one according to environmental conditions. More specifically, assuming that robots are not allowed to have individual identification numbers, a pre-determined leader, memories of previous perceptions and actions, and direct communications to each other, we verify the validity of the proposed algorithm using the in-house simulator. The results show that a swarm of robots repeats the process of partition and maintenance passing through multiple narrow passageways

JAIST Robotic Walker control based on a two-layered Kalman filter

This paper presents a new control scheme of JAIST Active Robotic Walker (JARoW) developed to provide elderly people with sufficient ambulatory capability. Toward its practical use, our focus is placed on how to allow easier and reliable maneuverability by creating a natural user interface. Specifically, our challenge lies in providing a well-functioning controller by detecting what the user wants to do or their intentions. A Kalman filter based tracking scheme is realized to estimate and predict the locations of the users legs and body in real time. The feedback control can then adjust the motions of JARoW corresponding to the actual users walking behaviors. Our experiments confirm that JARoW can autonomously adjust its motion direction and velocity without requiring any additional control inputs.

Flocking Controls for Swarms of Mobile Robots Inspired by Fish Schools

Self-organizing and adaptive behaviors can be easily seen in flocks of birds or schools of fish. It is surprising that each individual member follows a small number of simple behavioral rules, resulting in sophisticated group behaviors (Wilson, 2000). For instance, when a school of fish is faced with an obstacle, they can avoid collision by being split into a plurality of smaller groups that can be merged after they pass around the obstacle. Based on the observation of such habits of schooling fishes, we propose collective navigation behavior rules that enable a large swarm of autonomous mobile robots to flock toward a stationary or moving goal in an unknown environment. Recently, robot swarms are expected to be deployed in a wide variety of applications such as odor localization, mobile sensor networking, medical operations, surveillance, and search-and-rescue (Sahin, 2005). In order to perform those tasks successfully, the behaviors of individual robots need to be controlled in a simple manner to support coordinated group behavior. Reynolds presented a distributed behavioral model of coordinated animal motion based on fish schools and bird flocks (Reynolds, 1987). His work demonstrated that navigation is an example of emergent behavior arising from simple rules. Many navigation strategies reported in the field of swarm robotics can be classified into centralized and decentralized strategies. Centralized strategies (Egerstedt & Hu, 2001) (Burgard et al, 2005) employ a central unit that organizes the behaviors of the whole swarm. This strategy usually lacks scalability and becomes technically unfeasible when a large swarm is considered. On the other hand, decentralized strategies are based on interactions between individual robots mostly inspired by evidence from biological systems or natural phenomena. Decentralized strategies can be further divided into biological emergence (Baldassarre et al, 2007) (Shimizu et al, 2006) (Folino & Spezzano, 2002), behavior-based (Ogren & Leonard, 2005) (Balch & Hybinette, 2000), and virtual physics-based (Spears et al, 2006) (Esposito & Dunbar, 2006) (Zarzhitsky et al, 2005) approaches. Specifically, the behavior-based and virtual physicsbased approaches are related to the use of such physical phenomena as crystallization (Balch & Hybinette, 2000) gravitational forces (Spears et al, 2005) (Zarzhitsky et al, 2005) (Spears et al, 2004) and potential fields (Esposito & Dunbar, 2006). Those works mostly use a force balance between inter-individual interactions exerting an attractive or repulsive force within the influence range, which might over-constrain the swarm and frequently lead to deadlocks. Moreover, the computations of relative velocities or accelerations between robots O pe n A cc es s D at ab as e w w w .in te hw eb .c om

Particle filter based feedback control of JAIST Active Robotic Walker

We present a new control scheme of JAIST Active Robotic Walker (JARoW) developed to provide potential users such as the elderly with sufficient ambulatory capability. Toward its practical use, we tackle JARoWs easy and reliable maneuverability by creating a natural user interface between a user and JARoW. Specifically, our focus is placed on how to realize the natural and smooth movement of JARoW despite different gait parameters of users. For this purpose, a particle filtered interface function (PFIF) is proposed to estimate and predict the locations of the users legs and body. Then, the simple feedback motion control function adjusts the motions of JARoW corresponding to the estimation and prediction. Experimental results show that the proposed control scheme can be quite satisfactory for practical use without requiring any additional user effort.

Self-configurable mobile robot swarms with hole repair capability

We address the problem of deploying a swarm of autonomous mobile robots toward building an ad hoc network of robotic sensors with spatial uniform density. For the purpose, each of the robots configures themselves into an area with geographical constraints through local interactions with two adjacent neighboring robots. The basic idea underlying this work is that robots can be thought of as liquid particles that change their positions conforming to the shape of the container they occupy. The main challenge is how to cope with the accuracy limitations of sensors and possible holes in the configuration. Considering such realistic conditions, the convergence of the proposed method is proved using Lyapunovpsilas theorem. The proposed method is verified to be effective through the simulation for the secure deployments of robotic sensor network.

Three dimensional deployment of robot swarms

This paper addresses the deployment problem for a swarm of autonomous mobile robots initially randomly distributed in 3 dimensional space. A fully decentralized geometric self-configuration approach is proposed to deploy individual robots at a given spatial density. Specifically, each robot interacts with three neighboring robots in a selective and dynamic fashion without using any explicit communication so that four robots eventually form a regular tetrahedron. Using such local interactions, the proposed algorithms enable a swarm of robots to span a network of regular tetrahedrons in a designated space. The convergence of the algorithms is theoretically proved using Lyapunov theory. Through extensive simulations, we validate the effectiveness and scalability of the proposed algorithms.