A Distributed Processing Approach for Smooth Task Transitioning in Strict Hierarchical Control

To enhance robots’ applicability in real-world scenarios, it is essential to establish a complex and multi-tasking behaviour, inspired by human nature. To this purpose, from a hardware perspective, a high number of degrees of freedom is necessary, as is the case for humanoids and collaborative mobile manipulators. From a software standpoint instead, complex hierarchical strategies are often used to define a set of behaviours that the robot should reflect in strict hierarchical order. Their main issue however, is related to the lack of continuity when their stack of tasks is changed. Existing works that address this issue clearly present a trade-off between optimality assurance during transition and computational costs. Here, we employ a distributed processing approach that enables not only the minimization of computational costs, but also continuous optimality and constraints feasibility even under sharp transitions. The approach is tested during three task transitions, for different tasks such as constrained trajectory tracking, obstacle avoidance, and postural optimization. Two mobile manipulators are used, each having 10 DoF, and the results confirm the smoothness of the generated solutions.