3 research outputs found
Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators
This paper presents a solution based on dual quaternion algebra to the
general problem of pose (i.e., position and orientation) consensus for systems
composed of multiple rigid-bodies. The dual quaternion algebra is used to model
the agents' poses and also in the distributed control laws, making the proposed
technique easily applicable to time-varying formation control of general
robotic systems. The proposed pose consensus protocol has guaranteed
convergence when the interaction among the agents is represented by directed
graphs with directed spanning trees, which is a more general result when
compared to the literature on formation control. In order to illustrate the
proposed pose consensus protocol and its extension to the problem of formation
control, we present a numerical simulation with a large number of free-flying
agents and also an application of cooperative manipulation by using real mobile
manipulators
A Dynamic Programming Framework for Optimal Planning of Redundant Robots Along Prescribed Paths With Kineto-Dynamic Constraints
Off-line optimal planning of trajectories for redundant robots along
prescribed task space paths is usually broken down into two consecutive
processes: first, the task space path is inverted to obtain a joint-space path,
then, the latter is parametrized with a time law. If the two processes are
separated, they cannot optimize the same objective function, ultimately
providing sub-optimal results. In this paper, a unified approach is presented
where dynamic programming is the underlying optimization technique. Its
flexibility allows accommodating arbitrary constraints and objective functions,
thus providing a generic framework for optimal planning of real systems. To
demonstrate its applicability to a real world scenario, the framework is
instantiated for time-optimality. Compared to numerical solvers, the proposed
methodology provides visibility of the underlying resolution process, allowing
for further analyses beyond the computation of the optimal trajectory. The
effectiveness of the framework is demonstrated on a real 7-degrees-of-freedom
serial chain. The issues associated with the execution of optimal trajectories
on a real controller are also discussed and addressed. The experiments show
that the proposed framework is able to effectively exploit kinematic redundancy
to optimize the performance index defined at planning level and generate
feasible trajectories that can be executed on real hardware with satisfactory
results