Bradley Hamner

An autonomous mobile manipulator for assembly tasks

The fundamental difference between autonomous robotic assembly and traditional hard automation, currently utilized in large-scale manufacturing production, lies in the specific approaches used in locating, acquiring, manipulating, aligning, and assembling parts. An autonomous robotic assembly manipulator offers high flexibility and high capability to deal with the inherent system uncertainties, unknowns, and exceptions. This paper presents an autonomous mobile manipulator that effectively overcomes inherent system uncertainties and exceptions by utilizing control strategies that employ coordinated control, combine visual and force servoing, and incorporate sophisticated reactive task control. The mobile manipulation system has been demonstrated experimentally to achieve high reliability for a “peg-in-hole” type of insertion assembly task that is commonly encountered in automotive wiring harness assembly.

Learning Obstacle Avoidance Parameters from Operator Behavior

This paper concerns an outdoor mobile robot that learns to avoid collisions by observing a human driver operate a vehicle equipped with sensors that continuously produce a map of the local environment. We have implemented steering control that models human behavior in trying to avoid obstacles while trying to follow a desired path. Here we present the formulation for this control system and its independent parameters and then show how these parameters can be automatically estimated by observing a human driver. We also present results from operation on an autonomous robot as well as in simulation, and compare the results from our method to another commonly used learning method. We find that the proposed method generalizes well and is capable of learning from a small number of samples.

Comprehensive Automation for Specialty Crops: Year 1 results and lessons learned

Comprehensive Automation for Specialty Crops is a project focused on the needs of the specialty crops sector, with a focus on apples and nursery trees. The project’s main thrusts are the integration of robotics technology and plant science; understanding and overcoming socio-economic barriers to technology adoption; and making the results available to growers and stakeholders through a nationwide outreach program. In this article, we present the results obtained and lessons learned in the first year of the project with a reconfigurable mobility infrastructure for autonomous farm driving. We then present sensor systems developed to enable three real-world agricultural applications—insect monitoring, crop load scouting, and caliper measurement—and discuss how they can be deployed autonomously to yield increased production efficiency and reduced labor costs.

An efficient system for combined route traversal and collision avoidance

Abstract Here we consider the problem of a robot that must follow a previously designated path outdoors. While the nominal path, a series of closely spaced via points, is provided with an assurance that it will lead to the destination, we can’t be guaranteed that it will be obstacle free. We present an efficient system capable of both following the path as well as being perceptive and agile enough to avoid obstacles in its way. We present a system that detects obstacles using laser ranging, as well as a layered system that continuously tracks the path, avoiding obstacles and replanning the route when necessary. The distinction of this system is that compared to the state of the art, it is minimal in sensing and computation while achieving high speeds. In this paper, we present an algorithm that is based on models of obstacle avoidance by humans and show variations of the model to deal with practical considerations. We show how the parameters of this model are automatically learned from observation of human operation and discuss limitations of the model. We then show how these models can be extended by adding online route planning and a formulation that allows for operation at varying speeds. We present experimental results from an autonomous vehicle that has operated several hundred kilometers to validate the methodology.

Motion planning for a mobile manipulator with imprecise locomotion

This paper presents a motion planning method for mobile manipulators for which the base locomotion is less precise than the manipulator control. In such a case, it is advisable to move the base to discrete poses from which the manipulator can be deployed to cover a prescribed trajectory. The proposed method finds base poses that not only cover the trajectory but also meet constraints on a measure of manipulability. We propose a variant of the conventional manipulability measure that is suited to the trajectory control of the end effector of the mobile manipulator along an arbitrary curve in three space. Results with implementation on a mobile manipulator are discussed.

Results with autonomous vehicles operating in specialty crops

Specialty crops constitute a

Autonomous Orchard Vehicles for Specialty Crops Production

45 billion/year industry. As opposed to crops such as wheat, cotton, corn and soybean, they are characterized by the need for intensive cultivation. Specialty crops growers currently face serious labor cost and availability problems, and few technological solutions exist to increase their efficiency given the past history of abundant supply of low-cost labor. This leads to an opportunity to use recent technological advances to not only increase efficiency and reduce labor costs in specialty crops production but also to support a domestic engineering solutions industry for specialty crops. We envision a family of reconfigurable vehicles that can be rapidly tasked to automate or augment pruning, thinning, harvesting, mowing, spraying, etc. They would share a common sensing and computing infrastructure, allowing applications created for one to be easily transferable to others - much like software applications today are transferable from one computer to another. In this paper we describe our work over the last three years designing and deploying a family of such vehicles, the Autonomous Prime Movers (APMs). The five vehicles completed so far have traveled autonomously over 300 km in research and commercial tree fruit orchards; preliminary results in time trials conducted by extension educators indicate efficiency gains of up to 58%.

Improving Orchard Efficiency with Autonomous Utility Vehicles

Comprehensive Automation for Specialty Crops (CASC) is a four-year project to develop crop intelligence and agricultural automation technologies for the apple and nursery tree industries. Supported by the USDA Specialty Crop Research Initiative, CASC aims at impacting industry where it matters most: increasing production efficiency and reducing labor costs. In this paper we present our most recent results with the Autonomous Prime Movers, a family of reconfigurable vehicles designed to augment or automate a variety of orchard operations. We discuss the modifications made on the first APM to make it robust to uneven terrain and variance in canopy types, along with field tests that totaled 159 km in autonomous orchard driving. We also present the design and initial deployment of two new autonomous orchard platforms.

Results in Combined Route Traversal and Collision Avoidance

In modern orchards, many maintenance tasks call for a driver to steer a tractor through rows of trees at slow speeds over hundreds of acres as it mows or sprays. Similarly, manually-driven orchard platforms allow a crew of workers to perform tasks such as pruning, training, and thinning. In this paper we report on the development of vehicles capable of autonomous row following in orchards. Such vehicles increase efficiency and reduce production costs by moving a farm worker from an unproductive driving role to a productive one. In the past year, the technologies that enable such autonomous row following have been implemented on an electric utility vehicle capable of continuously driving orchard blocks; to date this vehicle has logged more than 130 km of driverless traversals. The vehicle uses laser range scanners to detect trees and other objects in its vicinity, builds a model of the row of trees, and uses this model to safely steer the vehicle along the row without GPS. It detects when it has reached the end of a row, turns, and enters the next row. This way the vehicle can drive entire orchard blocks autonomously, even if the rows are of varied lengths or trees are missing in the rows. In addition to the laser scanners, the only other sensors necessary are wheel encoders that continuously measure distance traveled and the steering angle. All computation is performed on a rugged laptop onboard the vehicle.

Mobile robotic dynamic tracking for assembly tasks

This paper presents an outdoor mobile robot capable of high-speed navigation in outdoor environments. Here we consider the problem of a robot that has to follow a designated path at high speeds over undulating terrain. It must also be perceptive and agile enough to avoid small obstacles. Collision avoidance is a key problem and it is necessary to use sensing modalities that are able to operate robustly in a wide variety of conditions. We report on the sensing and control necessary for this application and the results obtained to date.