This paper presents a novel approach for time-optimal path parameterization based on reachability analysis for robotic systems with viscous friction in the dynamics and jerk constraints. The main step of the method is the backward propagation of controllable sets through a linear second-order system. In order to avoid the unbounded growth of the number of constraints, the sets are approximated by a ray shooting algorithm. Using a convex relaxation, the required set expansion can be solved with second-order cone programming. Evaluation results for a 6-degree of freedom (DOF) robot arm highlight the advantages of the method for computing jerk-limited trajectories.

This paper presents a time-optimal path parameterization (TOPP) method for cooperative multi-arm robotic systems (MARS) manipulating heavy objects with third-order constraints that include jerk, torque rate and wrench rate limits. The method is based on a problem reformulation as a sequential linear program and provides a unified planning approach that is faster than previous convex optimization techniques. The equivalence to a reachability-based TOPP is shown and simulation results for a cooperative MARS consisting of two 7 degree of freedom (DOF) robots and a tightly grasped object with 6 DOFs are provided.

Recently, data-driven and hybrid control of hydraulic cylinders for excavator assistance functions have been in the focus of many research papers. To ensure an accurate behavior, data-driven controllers and models need a large amount of data to cover all relevant operation regions, which requires a time-consuming data generation process. In this work, we introduce two learning-based methods to enhance the efficiency of this procedure: a static learning method and an active learning method. Both methods reduce the amount of required data to learn a hydraulic inverse actuation model. Compared to previous collection methods, the required data was reduced by factor 7.5, while the information content of the dataset remains nearly the same.

This paper presents a new control scheme for cooperative dual-arm robots manipulating heavy objects. The proposed method uses the full dynamical model of the kinematically coupled robot system and builds on a hierarchical quadratic programming (HQP) formulation to enforce dynamical inequality constraints such as joint torques or internal loads. This ensures optimal tracking of an object trajectory, while additional objectives with lower priority are optimized on the prior solution space. Therefore, the redundancy of the inherent load distribution problem between the two arms can be eliminated. With this approach, higher object loads can be manipulated compared to non-optimized methods. Simulations with a 14 degree of freedom (dof) dual-arm robotic system demonstrate the effectiveness of the proposed control method. The real-time feasibility is guaranteed with an average computation time of less than 0.35 milliseconds at a control rate of 1 kilohertz.