We have been studying multiple mobile robot systems since 1989. We consider intelligent systems as consisting of three factors: (a) multiple robots or intelligent machines (multiple agents), (b) human-beings who operate or cooperate with multiple agents, and (c) working environments. Now we deal with “Multi-agent system and robot system design“, “Human support robot system, human demonstration-based robot system, and manufacturing system design“, and “Human analysis and embodied-brain system science” based on motion planning methodology, evolutionary computation, control theory, and so on.
Our final target is to establish design methodology of multi-agent systems including artificial agents, humans and working environments through clarifying the underlying structure and function in the intelligence and mobility (mobiligence) of these agents.
Current research areas
Introduction [Japanese PDF] [English PDF] [Japanese YouTube] [English YouTube]
Multi-agent system and robot system design
- Fast motion planning algorithm considering the dynamic characteristics of swarm AGVs
- Proposal of a general-purpose algorithm of split delivery vehicle routing problems for multiple agricultural machines
- Robot system arrangement using experience-based hierarchical optimization methods
- Measurement pose optimization for joint offset calibration with a hand-eye camera
- Stepwise large-scale multi-agent task planning using neighborhood search
Human support robot system, human demonstration-based robot system, and manufacturing system design
- Learning difficult robot motion from human demonstration collected via a single RGB camera
- Automatic action recognition algorithm for industrial manual workers with human skeleton and object information
- Development of a nursing skill training system based on manipulator variable admittance control [Movie]
- Learning from human hand demonstration for wire harness grasping
- Development of virtual reality system for identification of specific expert skills in refinery inspection task with explainable AI
- Description method and failure ontology for utilizing maintenance logs with FMEA in failure cause inference of manufacturing systems
- A framework to support failure cause identification in manufacturing systems through generalization of past FMEAs
Human analysis and embodied-brain system science
- Modeling standing postural control in Parkinson’s Disease patients
- Estimation of foot center of pressure information using smartphone sensors
※ These links are PDF files