Continuous tasks and constraints transitions for the control of robots

Large and sudden changes in the torques of the actuators of a robot are highly undesirable and should be avoided during robot control as they may result in unpredictable behaviours. Multi-objective control system for complex robots usually have to handle multiple prioritized tasks while satisfying constraints. Changes in tasks and/or constraints are inevitable for robots when adapting to the unstructured and dynamic environment, and they may lead to large sudden changes in torques. Within this work, the problem of task priority transitions and changing constraints is primarily considered to reduce large sudden changes in torques. This is achieved through two main contributions as follows. Firstly, based on quadratic programming (QP), a new controller called Generalized Hierarchical Control (GHC) is developed to deal with task priority transitions among arbitrary prioritized task. This projector can be used to achieve continuous task priority transitions, as well as insert or remove tasks among a set of tasks to be performed in an elegant way. The control input (e.g. joint torques) is computed by solving one quadratic programming problem, where generalized projectors are adopted to maintain a task hierarchy while satisfying equality and inequality constraints. Secondly, a predictive control primitive based on Model Predictive Control (MPC) is developed to handle presence of discontinuities in the constraints that the robot must satisfy, such as the breaking of contacts with the environment or the avoidance of an obstacle. The controller takes the advantages of predictive formulations to anticipate the evolutions of the constraints by means of control scenarios and/or sensor information, and thus generate new continuous constraints to replace the original discontinuous constraints in the QP reactive controller. As a result, the rate of change in joint torques is minimized compared with the original discontinuous constraints. This predictive control primitive does not directly modify the desired task objectives, but the constraints to ensure that the worst case of changes of torques is well-managed. The effectiveness of the proposed control framework is validated by a set of experiments in simulation on the Kuka LWR robot and the iCub humanoid robot. The results show that the proposed approach significantly decrease the rate of change in joint torques when task priorities switch or discontinuous constraints occur.