Noriaki Hirose

Personal robot assisting transportation to support active human life - Reference generation based on model predictive control for robust quick turning.

Various robots are being developed to support active human lifestyles throughout the world. In particular, robots that can support a comfortable lifestyle for elderly people are needed for the coming super-aging society in many countries. The authors have already proposed the Personal Robot (PR) shown in Fig. 1, which can follow a human being and carry their baggage; the PR user does not need to carry heavy bags, even after shopping. The PR, therefore, can encourage not only elderly people but also young people to walk outside. This means that the PR can support a life of wellness in the true sense. In conventional research, a control approach for the roll angle is proposed to ensure the stability margin for steady turning because in order to follow a human being, the PR must realize high traveling performance. In this paper, a method of reference generation for the roll angle and turning angular velocity is proposed to take into account the stability margin during the transient state using model predictive control. According to the proposed approach, the quickest turning motion can be realized by keeping the upper and lower boundary constraints for the zero moment point (ZMP). The effectiveness of the proposed approach is verified by experiment using the prototype PR.

Mode switching control for a personal mobility robot based on initial value compensation

The present paper introduces a novel standing-up control strategy using Initial Value Compensation (IVC) for a Personal Mobility Robot (PMR). Personal mobility robots, which are wheeled inverted pendulum type mobility-assist devices, have the advantages of a small turn radius, maintaining a level seating posture on a slope, and a small footprint as compared to conventional electric wheelchairs with four wheels. On the other hand, the user must maintain the posture of the PMR by the use of additional assist wheels on the ground when mounting/dismounting and working from the PMR. In order to achieve these requirements, we propose the following approach. First, the PMR accelerates to lift its assist wheels off the ground and detects whether the assist wheels are in contact with the ground. The feedback controller is then switched to wheeled inverted pendulum mode using the proposed IVC. The proposed IVC is designed to improve the transient responses and suppress the amplitude of the control input and the jerk component for reducing the shock to the driver. The effectiveness of the proposed approach has been verified by numerical simulations and experiments using a prototype PMR.

Personal robot assisting transportation to support active human life — Posture stabilization based on feedback compensation of lateral acceleration

Recently, a super-aging society has developed in many countries around the world. The research and development of PRs (personal robots) that improve the quality of human life is needed in order to accommodate the aging society. Elderly people will be able to spend their lives happily and effortlessly with the aid of useful and convenient PRs. However, excessive or premature use of PRs may cause health deterioration or contribute to the quick aging phenomenon. In this paper, a new prototype PR is proposed that can follow human beings with their baggage. Elderly people, therefore, will be able to go outside empty handed to shop, enjoy the fresh air, and visit friends. This PR will encourage people to walk outside and can eventually support an active lifestyle in its true sense. For actual use, PRs should have both a small footprint for coexistence in human society and high traveling performance for following the human wherever they go. Active posture control for the roll and pitch angles is applied to the PR to realize these requirements. The proposed structure and control approach using lateral acceleration as a control variable is verified by experiment using the new prototype robot.

Personal robot assisting transportation to support active human life — Human-following method based on model predictive control for adjacency without collision

An aging society exists throughout the entire world. In order to support the aging society, service robots that are able to improve our comfortable lifestyle and increase longevity have been developed. However, excessive and premature robot support may cause not only the deterioration of our physical ability but also shortening of the period of well-being. In our research group, a personal robot that can carry baggage and automatically follow a human being was developed to overcome the above problem. The people that use this personal robot will be able to walk outside with empty hands, even after shopping, thus contributing to their health management. In this paper, the past prototypes of the personal robot are discussed, and the new robot is presented. Also, a human-following method based on model predictive control is proposed to realize the contradicting requirements of both shortening the relative distance between the owner and the robot and ensuring no collision. The effectiveness of the proposed approach is verified by numerical simulation of the prototype robot.

Posture stabilization for a personal mobility robot using feedback compensation with an unstable pole

The present paper introduces a novel posture control approach using feedback compensation with an unstable pole. A narrow and small personal mobility robot (PMR) requires control of its posture in order to achieve quick turning and high acceleration. However, in the conventional control approach that uses the posture angle as a controlled variable, the zero moment point (ZMP) cannot be set to the desired point if an unknown disturbance force acts on the PMR, if the center of gravity of the PMR fluctuates, or if the conditions between the tires and the road surface change. In the present paper, a novel control method using feedback compensation with an unstable pole is proposed in order to achieve the desired ZMP at the steady state. The proposed controller changes the control input for the actuator of the posture control to zero in order to achieve the desired posture angle. The effectiveness of the proposed approach is verified experimentally using a prototype PMR.

Following control approach based on model predictive control for wheeled inverted pendulum robot

Personal robots, which are seen as tools that will be needed to support our aging society, will be expected to support the comfortable lifestyles of healthy young people as well as the elderly. However, excessive and premature robot support may adversely impact the physical abilities of their human owner/operators. In this paper, the authors propose a personal robot equipped with wheeled inverted pendulum control that can carry baggage and follow the human being. Since such robots could remove the drudgery associated with carrying luggage, their use could also encourage people to go outside and walk briskly, which could contribute to improved health management. This paper proposes a novel control approach for a robot following the human being. The proposed approach employs a model predictive control that facilitates consideration of several types of upper and lower level constraints a personal robot would require. The effectiveness of our proposed approach was then verified in experiments using a prototype personal robot. Graphical Abstract

MPC policy learning using DNN for human following control without collision

Abstract Model predictive control has recently been applied to a wide variety of motion control systems. Model predictive control can be used to generate optimized control inputs with excellent performance considering inequality constraints to the control inputs, control outputs, and state variables. However, the computational load for this method is too heavy for implementation in most actual systems because the quadratic programming problem must be solved within the sampling period. As the number of inequality constraints, control variables, and state variables in the control system increases, more calculation time is required. In this study, a deep neural network designed to learn the model predictive control policy was developed to reduce the computational load. It is expected that a relatively small neural network can be used to learn the model predictive control policy. In the proposed system, the motion controller calculates the learned neural network in real time instead of solving the quadratic programming problem, realizing almost the same control performance as the original model predictive control approach. The effectiveness of the proposed approach was verified by applying it to the control of a personal robot designed to follow the user, which can provide daily support to the elderly. In Matlab simulations, the calculation time for the proposed approach was approximately times faster than that of the conventional method of solving the quadratic programming problem. In addition, an experiment using an actual personal robot was conducted to confirm the control performance.

Modeling of rolling friction by recurrent neural network using LSTM

The modeling and identification of a mechanical system is the most important issue for many control systems in order to realize the desired control specifications. In particular, the friction characteristics often deteriorate the control performance, such as in the fast and precise positioning performance in industrial robots, the force estimation accuracy based on a disturbance observer, and the posture control performance of an inverted pendulum robot. Rolling friction tends to cause overshoot, undershoot, or limit cycles of the target value in positioning systems. In previous research, some model structures for rolling friction have been proposed to express the hysteresis characteristics in order to overcome these control issues. However, it is difficult to identify the correct parameters for precise modeling. In this paper, the modeling of rolling friction based on a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) is proposed to precisely express the rolling friction characteristics. The initial value design of the RNN during supervised learning is also presented to achieve a better model. The effectiveness of the proposed approach is verified by comparison with conventional friction models using an actual experimental setup.

IR tag detection and tracking with omnidirectional camera using track-before-detect particle filter

Robust user detection and tracking is one of the key issues for a personal robot to follow the target person. In this paper, a novel tracking system using an omnidirectional camera and IR LED tags is proposed. The users wear the tags on their ankles, and the tags emit a light pattern as its ID. The camera on the robot is used to detect and track their positions individually. A novel approach based on a track-before-detect particle filter is proposed. It detects and tracks the tags simultaneously, even if the tags are not synchronized with the camera sampling or are not fully observable. The effectiveness of the proposed system is evaluated by experiments using a prototype personal robot. Graphical Abstract

Personal robot assisting transportation to support active human life

An aging society exists throughout the entire world. In order to support the aging society, service robots that are able to improve our comfortable lifestyle and increase longevity have been developed. However, excessive and premature robot support may cause not only the deterioration of our physical ability but also shortening of the period of well-being. In our research group, a personal robot that can carry baggage and automatically follow a human being was developed to overcome the above problem. The people that use this personal robot will be able to walk outside with empty hands, even after shopping, thus contributing to their health management. In this paper, the past prototypes of the personal robot are discussed, and the new robot is presented. Also, a human-following method based on model predictive control is proposed to realize the contradicting requirements of both shortening the relative distance between the owner and the robot and ensuring no collision. The effectiveness of the proposed approach is verified by numerical simulation of the prototype robot.