Personal Robot Assisting Transportation to Support Active Human Life - Following Control based on Model Predictive Control with Multiple Future Predictions -

Advanced countries are currently experiencing an aging society, and many people could benefit from a personal robot that can support a comfortable lifestyle. However, ex- cessive and premature robot assistance may deteriorate the user's physical abilities and can accelerate their aging process. The authors have already proposed a personal robot that can follow the user, even on uneven road surfaces. Therefore, people utilizing this personal robot do not need to carry heavy baggage, even after shopping. It is reported that a few brisk walks per week can be advantageous to our health management and can improve our quality of life, so this means that the personal robot can encourage the user to maintain an active lifestyle. For this personal robot, close following is required to ensure the safety of the baggage and the personal robot. However, it is difficult for the conventional approach to avoid collisions when the user stops suddenly. In this paper, a control approach based on model predictive control is proposed to achieve two conflicting requirements: close following and no collision. The proposed approach predicts multiple future outcomes, in which the user either stops or accelerates in the next step, in order to realize an appropriate relative distance and to consider the upper and lower boundaries for posture stabilization. The effectiveness of the proposed approach can be verified in numerical simulations using a multibody dynamics model consisting of actual 3D representations.