Robotics for engineers
Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Engineering foundations of robotics
Engineering foundations of robotics
A learning process for fuzzy control rules using genetic algorithms
Fuzzy Sets and Systems
Fuzzy logic controller design utilizing multiple contending software agents
Fuzzy Sets and Systems
A genetic-algorithm-based method for tuning fuzzy logic controllers
Fuzzy Sets and Systems
Learning fuzzy classifier systems for multi-agent coordination
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent embedded agents
Free gait generation with reinforcement learning for a six-legged robot
Robotics and Autonomous Systems
Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems
Information Sciences: an International Journal
Meet me where i'm gazing: how shared attention gaze affects human-robot handover timing
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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This paper presents an application of the multi-agent system approach to a service mobile manipulator robot that interacts with a human during an object delivery and hand-over task in two dimensions. The base, elbow and shoulder of the robot are identified as three different agents, and are controlled using fuzzy control. The control variables of the controllers are linear velocity of the base, angular velocity of the elbow, and angular velocity of the shoulder. Main inputs to the system are the horizontal and vertical distances between the human and robot hands. These are input to all three agents. In developing the fuzzy control rules, effective delivery and avoidance of contact with humans, not to cause physical damage, are considered. The membership functions of the fuzzy controllers are tuned by using genetic algorithms. In tuning, the performance is calculated considering the distance deviation from the direct path, time spent to reach the human hand and energy consumed by the actuators. The proposed multi-agent system structure based on fuzzy control for the object delivery task succeeded in both effective and safe delivery.