Abstract

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data; 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning; 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes; and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to: a) datasets, publications, models, benchmarks, and simulation tools; b) open competitions for designs and systems; and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: http://goldberg.berkeley.edu/cloud-robotics/ Note to Practitioners-Most robots and automation systems still operate independently using onboard computation, memory, and programming. Emerging advances and the increasing availability of networking in the "Cloud" suggests new approaches where processing is performed remotely with access to dynamic global datasets to support a range of functions. This paper surveys research to date.

This paper presents LQG-MP (linear-quadratic Gaussian motion planning), a new approach to robot motion planning that takes into account the sensors and the controller that will be used during execution of the robot's path. LQG-MP is based on the linear-quadratic controller with Gaussian models of uncertainty, and explicitly characterizes in advance (i.e., before execution) the a-priori probability distributions of the state of the robot along its path. These distributions can be used to assess the quality of the path, for instance by computing the probability of avoiding collisions. Many methods can be used to generate the needed ensemble of candidate paths from which the best path is selected; in this paper we report results using Rapidly-Exploring Random Trees (RRT). We study the performance of LQG-MP with simulation experiments in three scenarios: A) a kinodynamic car-like robot, B) multi-robot planning with differential-drive robots, and C) a 6-DOF serial manipulator. We also apply Kalman Smoothing to make paths C k -continuous while avoiding obstacles and apply LQG-MP to precomputed roadmaps using a variant of Dijkstra's algorithm to efficiently find near-optimal paths.

In manufacturing it is often necessary to orient parts prior to packing or assembly. We say that a planar part is polygonal if its convex hull is a polygon. We consider the following problem: given a list of n vertices describing a polygonal part whose initial orientation is unknown, find the shortest sequence of mechanical gripper actions that is guaranteed to orient the part up to symmetry in its convex hull. We show that such a sequence exists for any polygonal part by giving an O(n 2 log n) algorithm for finding the sequence. Since the gripper actions do not require feedback, this result implies that any polygonal part can be oriented without sensors.

Abstract-Medical procedures such as seed implantation, biopsies, and treatment injections require inserting a needle to a specific target location inside the human body. Flexible needles with bevel tips are known to bend when inserted into soft tissues and can be inserted to targets unreachable by rigid symmetric-tip needles. Planning for such procedures is difficult because needle insertion causes soft tissues to displace and deform. In this paper, we develop a 2D planning algorithm for insertion of highly flexible bevel-tip needles into tissues with obstacles. Given an initial needle insertion plan specifying location, orientation, bevel rotation, and insertion distance, the planner combines soft tissue modeling and numerical optimization to generate a needle insertion plan that compensates for simulated tissue deformations, locally avoids polygonal obstacles, and minimizes needle insertion distance. Soft tissue deformations are simulated using a finite element formulation that models the effects of needle tip and frictional forces using a 2D mesh. The planning problem is formulated as a constrained nonlinear optimization problem which is locally minimized using a penalty method that converts the formulation to a sequence of unconstrained optimization problems. We apply the planner to bevel-right and bevel-left needles and generate plans for targets that are unreachable by rigid needles.