RESUMO
When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.
RESUMO
When humanoid robots work in human environments, falls are inevitable due to the complexity of such environments. Current research on humanoid robot falls has mainly focused on falls on the ground, with little research on humanoid robots falling from the air. In this paper, we employ an extended state variable formulation that directly maps from the high-level motion strategy space to the full-body joint space to optimize the falling trajectory in order to protect the robot when falling from the air. In order to mitigate the impact force generated by the robot's fall, during the aerial phase, we employ simple proportion differentiation (PD) control. In the landing phase, we optimize the optimal contact force at the contact point using the centroidal dynamics model. Based on the contact force, the changes to the end-effector positions are solved using a dual spring-damper model. In the simulation experiments, we conduct three comparative experiments, and the simulation results demonstrate that the robot can safely fall 1.5 m from the ground at a pitch angle of 45°. Finally, we experimentally validate the methods on an actual robot by performing a side-fall experiment. The experimental results show that the proposed trajectory optimization and motion control methods can provide excellent shock absorption for the impact generated when a robot falls.
