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Biologically Inspired Robotics
passive joint. For the hyper dynamic manipulator proposed in this chapter,
these methods cannot be applied directly. Herein, we use a control method
based on motion generation.
According to the foregoing statements, to improve the capability of
dynamic manipulation, dynamically coupled driving must be made use
of efficiently by adopting light and low-power actuators in a manipulator
and one special motion trajectory, and the joint stop must be made available in the manipulator and utilized correctly in the motion. Obviously,
the utilization of both dynamically coupled driving and the joint stop
depends on the motion planning. Unfortunately, to the best of our knowledge, there is no mature analytical method to generate such motion for a
nonlinear system. We solve this problem by adopting a constrained optimization method.
4.3.1 Motion Generation and Control Method
4.3.1.1 Motion Generation
Many related works have been reported regarding the motion generation
of a manipulator (Wang, Timoszyk, and Bobrow 2001). Most of these optimal motion generation methods were based on joint trajectory approximation with a performance index and some constraints. In addition, general
motion generation methods mainly generate the joint trajectory according
to the specifications for velocity, acceleration, etc., of different joints in their
joint space separately (Shimon 1999).
However, as mentioned previously, the features of this proposal are utilizing dynamically coupled driving and joint stops in a hyper dynamic manipulator to realize a smart structure like a human. Therefore, it is necessary to
generate hyper dynamic manipulation of a manipulator while considering
the constraints on maximum active torque and the power of the actuators,
the characteristics of joint stops, etc., in addition to the boundary conditions
such as motion specifications. If a motion trajectory satisfies both constraints
and boundary conditions, dynamically coupled driving and joint stops will
be utilized automatically. However, because the nonlinearity and coupling
are strengthened by those constraints, the problem of motion planning
becomes more difficult. Among the constraints, the hard constraint on active
torque is the most pivotal factor for motion generation. It was found that joint
trajectory-based methods are difficult to use to solve such kinds of motion
generation problems.
To deal with such motion generation problems, time-dependent active
torque functions of joint i(i = 1, … , n) during the whole period from initial
position to finish position are used as inputs. If the active torque functions
of joint i(i = 1, … , n) are known, the motions of these joints can be derived by
solving direct dynamics by satisfying the hard constraints on active torque
and actuator power and the characteristics of joint stops. We assign active
Biologically Inspired Robotics
passive joint. For the hyper dynamic manipulator proposed in this chapter,
these methods cannot be applied directly. Herein, we use a control method
based on motion generation.
According to the foregoing statements, to improve the capability of
dynamic manipulation, dynamically coupled driving must be made use
of efficiently by adopting light and low-power actuators in a manipulator
and one special motion trajectory, and the joint stop must be made available in the manipulator and utilized correctly in the motion. Obviously,
the utilization of both dynamically coupled driving and the joint stop
depends on the motion planning. Unfortunately, to the best of our knowledge, there is no mature analytical method to generate such motion for a
nonlinear system. We solve this problem by adopting a constrained optimization method.
4.3.1 Motion Generation and Control Method
4.3.1.1 Motion Generation
Many related works have been reported regarding the motion generation
of a manipulator (Wang, Timoszyk, and Bobrow 2001). Most of these optimal motion generation methods were based on joint trajectory approximation with a performance index and some constraints. In addition, general
motion generation methods mainly generate the joint trajectory according
to the specifications for velocity, acceleration, etc., of different joints in their
joint space separately (Shimon 1999).
However, as mentioned previously, the features of this proposal are utilizing dynamically coupled driving and joint stops in a hyper dynamic manipulator to realize a smart structure like a human. Therefore, it is necessary to
generate hyper dynamic manipulation of a manipulator while considering
the constraints on maximum active torque and the power of the actuators,
the characteristics of joint stops, etc., in addition to the boundary conditions
such as motion specifications. If a motion trajectory satisfies both constraints
and boundary conditions, dynamically coupled driving and joint stops will
be utilized automatically. However, because the nonlinearity and coupling
are strengthened by those constraints, the problem of motion planning
becomes more difficult. Among the constraints, the hard constraint on active
torque is the most pivotal factor for motion generation. It was found that joint
trajectory-based methods are difficult to use to solve such kinds of motion
generation problems.
To deal with such motion generation problems, time-dependent active
torque functions of joint i(i = 1, … , n) during the whole period from initial
position to finish position are used as inputs. If the active torque functions
of joint i(i = 1, … , n) are known, the motions of these joints can be derived by
solving direct dynamics by satisfying the hard constraints on active torque
and actuator power and the characteristics of joint stops. We assign active