Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

Motion Planning for the On-orbit Grasping of a Non-cooperative Target Satellite with Collision Avoidance

A method for grasping a tumbling noncooperative target is presented, which is based on nonlinear optimization and collision avoidance. Motion constraints on the robot joints as well as on the end-effector forces are considered. Cost functions of interest address the robustness of the planned solutions during the tracking phase as well as actuation energy. The method is applied in simulation to different operational scenarios

Feedback MPC for Torque-Controlled Legged Robots

The computational power of mobile robots is currently insufficient to achieve torque level whole-body Model Predictive Control (MPC) at the update rates required for complex dynamic systems such as legged robots. This problem is commonly circumvented by using a fast tracking controller to compensate for model errors between updates. In this work, we show that the feedback policy from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable alternative to bridge the gap between the low MPC update rate and the actuation command rate. We propose to augment the DDP approach with a relaxed barrier function to address inequality constraints arising from the friction cone. A frequency-dependent cost function is used to reduce the sensitivity to high-frequency model errors and actuator bandwidth limits. We demonstrate that our approach can find stable locomotion policies for the torque-controlled quadruped, ANYmal, both in simulation and on hardware.Comment: Paper accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

Correct-by-Construction Approach for Self-Evolvable Robots

The paper presents a new formal way of modeling and designing reconfigurable robots, in which case the robots are allowed to reconfigure not only structurally but also functionally. We call such kind of robots "self-evolvable", which have the potential to be more flexible to be used in a wider range of tasks, in a wider range of environments, and with a wider range of users. To accommodate such a concept, i.e., allowing a self-evovable robot to be configured and reconfigured, we present a series of formal constructs, e.g., structural reconfigurable grammar and functional reconfigurable grammar. Furthermore, we present a correct-by-construction strategy, which, given the description of a workspace, the formula specifying a task, and a set of available modules, is capable of constructing during the design phase a robot that is guaranteed to perform the task satisfactorily. We use a planar multi-link manipulator as an example throughout the paper to demonstrate the proposed modeling and designing procedures.Comment: The paper has 17 pages and 4 figure

Motion Planning from Demonstrations and Polynomial Optimization for Visual Servoing Applications

Vision feedback control techniques are desirable for a wide range of robotics applications due to their robustness to image noise and modeling errors. However in the case of a robot-mounted camera, they encounter difficulties when the camera traverses large displacements. This scenario necessitates continuous visual target feedback during the robot motion, while simultaneously considering the robot's self- and external-constraints. Herein, we propose to combine workspace (Cartesian space) path-planning with robot teach-by-demonstration to address the visibility constraint, joint limits and “whole arm” collision avoidance for vision-based control of a robot manipulator. User demonstration data generates safe regions for robot motion with respect to joint limits and potential “whole arm” collisions. Our algorithm uses these safe regions to generate new feasible trajectories under a visibility constraint that achieves the desired view of the target (e.g., a pre-grasping location) in new, undemonstrated locations. Experiments with a 7-DOF articulated arm validate the proposed method.published_or_final_versio