The generation of complex movement patterns, in particular, in cases where one needs to smoothly and accurately join trajectories in a dynamic way, is an important problem in robotics. This paper presents a novel joining method that is based on the modification of the original dynamic movement primitive formulation. The new method can reproduce the target trajectory with high accuracy regarding both the position and the velocity profile and produces smooth and natural transitions in position space, as well as in velocity space. The properties of the method are demonstrated by its application to simulated handwriting generation, which are also shown on a robot, where an adaptive algorithm is used to learn trajectories from human demonstration. These results demonstrate that the new method is a feasible alternative for joining of movement sequences, which has a high potential for all robotics applications where trajectory joining is required.

The generation of complex movement patterns, in particular in cases where one needs to smoothly and accurately join trajectories, is still a difficult problem in robotics. This paper presents a novel approach for joining of several dynamic movement primitives (DMPs) based on a modification of the original formulation for DMPs. The new method produces smooth and natural transitions in position as well as velocity space. The properties of the method are demonstrated by applying it to simulated handwriting generation implemented on a robot, where an adaptive algorithm is used to learn trajectories from human demonstration. These results demonstrate that the new method is a feasible alternative for trajectory learning and generation and its accuracy and modular character has potential for various robotics applications.

This paper presents a novel method for trajectory generation based on dynamic movement primitives (DMPs) treated from a control theoretical perspective. We extended the key ideas from the original DMP formalism by introducing a velocity convergence mechanism in the reformulated system. Theoretical proof is given to guarantee its validity. The new method can deal with complex paths as a whole. Based on this, we can generate smooth trajectories with automatically generated transition zones, satisfy position- and velocity boundary conditions at start and endpoint with high precision, and support multiple via-point applications. Theoretic proof of this method and experiments are presented.

This paper puts forward a novel design solution for building a 3D hyper-redundant chain robot (HRCR) system, which consists of linked, identical modules and one base module. All the joints of this HRCR are passive and state controllable, and share common inputs introduced by wire-driven control, no matter how many degrees of freedom (DOF) are implemented using different numbers of modules. The prototype developed here, named 3D-Trunk, is used as a proof of concept. We will present here its concept, mechanical and embedded controller design and the implementation.

This paper puts forward a novel design concept for building a 3-D hyper-redundant chain robot (HRCR) system, consisting of linked, identical modules and one base module. All the joints of this HRCR are passive and state controllable and share common inputs introduced by wire-driven control. The original prototype developed here, named 3D-Trunk, is used as a proof of concept. We will present its whole mechanical design and controller architecture. The key components of 3D-Trunk, its operational principles, and all implementation issues are exhibited and described in detail. Basic robotics analyses, dynamics simulations, and some experiments are also shown. This novel design concept is highly modular and scalable, no matter how many degrees of freedom are implemented and, thus, provides an affordable solution for constructing an HRCR.