Managing Temporal Robot Constraints using Reachable Volumes

This project focuses on planning the motion for high degree of freedom manipulator robots under dynamic (or temporal) constraints. Manipulator robots are widely used in industry and are important because they can do jobs that are either too tedious or too dangerous for humans. An example would be picking up toxic waste or exploring underwater archeological sites. Motion planning for high degree of freedom (DOF) manipulators under task constraints is challenging because it gives rise to high dimensional configuration spaces (C-space) that are complex in structure. Our approach reduces the complexity by re-parameterizing the manipulator robots DOFs into a space that contains the valid regions that the end effector of the robot can reach, known as the Reachable Volume space (RV-space). In this way, we can sample valid configurations in Cspace in linear time with the number of DOFs of the manipulator. Current Reachable Volume theory only handles permanent constraints and cannot adapt to scenarios that require constraints that are enabled at certain times in the problem and disabled at other times. For example, when a manipulator grabs an object, closure constraints on the grasper must be satisfied, but when the object is to be dropped, these constraints must be ignored. Additionally, certain scenarios require the cooperation of multiple robots. This is obvious if we consider problems that involve objects that are too large for a single robot to handle. In this work, we produce a working computational framework for efficient motion planning of high degree of freedom manipulator arms under dynamic constraints through the extension of existing work in Reachable Volume spaces

Autonomous Mechanical Assembly on the Space Shuttle: An Overview

The space shuttle will be equipped with a pair of 50 ft. manipulators used to handle payloads and to perform mechanical assembly operations. Although current plans call for these manipulators to be operated by a human teleoperator. The possibility of using results from robotics and machine intelligence to automate this shuttle assembly system was investigated. The major components of an autonomous mechanical assembly system are examined, along with the technology base upon which they depend. The state of the art in advanced automation is also assessed

Learning to Navigate Cloth using Haptics

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A. Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm

Contact aware robust semi-autonomous teleoperation of mobile manipulators

In the context of human-robot collaboration, cooperation and teaming, the use of mobile manipulators is widespread on applications involving unpredictable or hazardous environments for humans operators, like space operations, waste management and search and rescue on disaster scenarios. Applications where the manipulator's motion is controlled remotely by specialized operators. Teleoperation of manipulators is not a straightforward task, and in many practical cases represent a common source of failures. Common issues during the remote control of manipulators are: increasing control complexity with respect the mechanical degrees of freedom; inadequate or incomplete feedback to the user (i.e. limited visualization or knowledge of the environment); predefined motion directives may be incompatible with constraints or obstacles imposed by the environment. In the latter case, part of the manipulator may get trapped or blocked by some obstacle in the environment, failure that cannot be easily detected, isolated nor counteracted remotely. While control complexity can be reduced by the introduction of motion directives or by abstraction of the robot motion, the real-time constraint of the teleoperation task requires the transfer of the least possible amount of data over the system's network, thus limiting the number of physical sensors that can be used to model the environment. Therefore, it is of fundamental to define alternative perceptive strategies to accurately characterize different interaction with the environment without relying on specific sensory technologies. In this work, we present a novel approach for safe teleoperation, that takes advantage of model based proprioceptive measurement of the robot dynamics to robustly identify unexpected collisions or contact events with the environment. Each identified collision is translated on-the-fly into a set of local motion constraints, allowing the exploitation of the system redundancies for the computation of intelligent control laws for automatic reaction, without requiring human intervention and minimizing the disturbance of the task execution (or, equivalently, the operator efforts). More precisely, the described system consist in two different building blocks. The first, for detecting unexpected interactions with the environment (perceptive block). The second, for intelligent and autonomous reaction after the stimulus (control block). The perceptive block is responsible of the contact event identification. In short, the approach is based on the claim that a sensorless collision detection method for robot manipulators can be extended to the field of mobile manipulators, by embedding it within a statistical learning framework. The control deals with the intelligent and autonomous reaction after the contact or impact with the environment occurs, and consist on an motion abstraction controller with a prioritized set of constrains, where the highest priority correspond to the robot reconfiguration after a collision is detected; when all related dynamical effects have been compensated, the controller switch again to the basic control mode

Path planning and obstacle avoidance for a robot with large degree of redundancy

An algorithm to allow a redundant robot to avoid obstacles in its workspace is proposed. The task of path planning is formulated as a sequence of nonlinear programming problems. For each problem, the objective is to minimize the distance between the current location of the end-effector and some intermediate point along a desired path. Two penalties are added to the objective function to ensure that the robot is not colliding with an obstacle and that its links are intersecting one another. Inequality constraints describing the mechanical stops and limiting values for joint movements are incorporated. Obstacles are represented as polygons, which are composed of series of connecting line segments. Successive quadratic programming algorithm is used to solve the path planning problem. To save computation time, the algorithm activates the joints that are closer to the end effector. If activations of those joints cannot satisfactory complete the task, other joints will be sequentially mobilized until the desired path is reached. The proposed method is demonstrated especially efficient when the degrees of freedom are large