Abstract
Human jumping motion includes stance phase, flight phase and landing impact phase. Jumping robot belongs to a variable constraints system because every phase has different constraint conditions. An unified dynamics equation during stance phase and flight phase is established based on floated-basis space. Inertia matching is used to analyze actuator/gear systems and select the optimum gear ratio based on the transmission performance between the torque produced at the actuator and the torque applied to the load. Load matching is an important index which affects jumping performance and reflects the capability of supporting a weight or mass. It also affects the distributing of the center of gravity (COG). Regarding jumping robot as a redundant manipulator with a load at end-effector, inertia matching can be applied to optimize load matching for jumping robot. Inertia matching manipulability and directional manipulability are easy to analyze and optimize the load matching parameters. A 5th order polynomial function is defined to plan COG trajectory of jumping motion, taking into account the constraint conditions of both velocity and acceleration. Finally, the numerical simulation of vertical jumping and experimental results show inertia matching is in direct proportion to jumping height, and inertia matching manipulability is a valid method to load matching optimization and conceptual design of robot.
Highlights
Legged robots have better mobility, versatility and autonomous capability on non‐structural environment contrasting to wheeled and tracked vehicles among mobile robotics family
The unified dynamics of stance and flight phase for humanoid jumping robot is established based on various constraint conditions and floated‐basis space
The concept of inertia matching is introducing to optimization of humanoid jumping robot
Summary
Legged robots have better mobility, versatility and autonomous capability on non‐structural environment contrasting to wheeled and tracked vehicles among mobile robotics family. Trajectory planning and motion optimization are two crucial steps in the design and control for jumping robots. 2005) was introduced to evaluate the static performance of a robot manipulator as an index of the relationship between the angular velocities at each joint and the linear or angular velocity at the end‐effector of the manipulator. The unified dynamics for jumping motion is established, and the manipulability measure which combines inertia matching and directional manipulability is applied to the load matching optimization of humanoid jumping robot.
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