Aggregation of self-reconfigurable robotic modules can potentially offer many advantages for robotic locomotion and manipulation. The resulting system could be more reliable and fault-tolerant and provide the necessary flexibility for new tasks and environments. However, self-aggregation of modules is a challenging task, especially when the alignment of the docking parties in a 3D environment involves both position and orientation (6D), since the bases of docking may be non-stationary (e.g., floating in space, underwater, or moving along the ground), and the end-effectors may have accumulated uncertainties due to many dynamically-established connections between modules. This paper presents a new framework for docking in such a context and describes a solution for sensor-guided self-reconfiguration and manipulation with non-fixed bases. The main contributions of the paper include a realistic experiment setting for 6D docking where a modular manipulator is floating or rotating in space with a reaction wheel and searches and docks with a target module using vision. The movement of the docking parties is a combination of floating and manipulation, and the precision of the docking is guided by a sensor located at the tip of the docking interface. The docking itself is planned and executed by a real-time algorithm with a theoretical convergence boundary. This new framework has been tested in a high-fidelity physics-based simulator, as well as by real robotic modules based on SuperBot. Experimental results have shown an average success rate of more than 86.7 percent in a variety of different 6D-docking scenarios.

Operating and maneuvering in difficult terrains has remained a challenging problem in the field of legged robots. One of the major challenges arises from the high dimensionality inherent in planning foot placements coupled with center of mass motion along terrains that are multifaceted and highly diverse. Previous work has resolved these issues to an extent by constraining the center of mass to fixed trajectories or using predetermined foot placements. To deal with these challenges, this paper proposes a new set of strategies: (1) an optimized geometric Hermite curve with minimum curvature and length is used to plan the motion of the center of mass (2) single contact model dynamics for state-space approximations of center of mass behavior are used to resolve feet transitions between steps in planar environments and (3) vertical center of mass phase space trajectories are optimized to produce an overall plan with minimum energy. This framework allows us to synthesize complex maneuvers in rough terrains and to develop optimal contact transition and foot placement plans that consider the robot's configuration and constraints. Experimental results show that for any potential locations of foot contacts, our planner generates smooth and optimal trajectories for center of mass motion as well as a minimum energy plan for transitioning between foot placements.