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Chris Niessl

dblp:123/6454 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 87% Motion planning and robot control · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
mobile manipulation
0.212015
Mobile manufacturing of large structures · ICRA 2015
Robotics › Robot manipulation › assembly
multi-robot assembly
0.212015
Mobile manufacturing of large structures · ICRA 2015
Robotics › Motion planning and robot control › robot control › motion control
precision control
0.112015
Mobile manufacturing of large structures · ICRA 2015
YearPublicationVenuePosition
2015 Mobile manufacturing of large structures
abstract
Assembly of large structures requires large fixtures, often referred to as monuments. Their cost and massive size limit flexibility and scalability of the manufacturing process. Numerous small mobile robots can replace these large structures and, therefore, replicate the efficiency of the assembly line with far more flexibility. An assembly line made up of mobile manipulators can easily and rapidly be reconfigured to support scalability and a varied product mix, while allowing for near optimal resource assignment. The challenge to using small robots in place of monuments is making their joint behavior precise enough to accomplish the task and efficient enough to execute subtasks in a reasonable period of time. In this paper, we describe a set of techniques that we combine to achieve the necessary precision and overall efficiency to build a large structure. We describe and demonstrate these techniques in the context of a testbed we implemented for assembling a wing ladder.
David A. Bourne, Howie Choset, Humphrey Hu, George Kantor, Chris Niessl, Zachary B. Rubinstein, Reid G. Simmons, Stephen F. Smith
ICRA5
2012 Sensor fusion for human safety in industrial workcells
abstract
Current manufacturing practices require complete physical separation between people and active industrial robots. These precautions ensure safety, but are inefficient in terms of time and resources, and place limits on the types of tasks that can be performed. In this paper, we present a real-time, sensor-based approach for ensuring the safety of people in close proximity to robots in an industrial workcell. Our approach fuses data from multiple 3D imaging sensors of different modalities into a volumetric evidence grid and segments the volume into regions corresponding to background, robots, and people. Surrounding each robot is a danger zone that dynamically updates according to the robot's position and trajectory. Similarly, surrounding each person is a dynamically updated safety zone. A collision between danger and safety zones indicates an impending actual collision, and the affected robot is stopped until the problem is resolved. We demonstrate and experimentally evaluate the concept in a prototype industrial workcell augmented with stereo and range cameras.
Paul E. Rybski, Peter Anderson-Sprecher, Daniel Huber, Chris Niessl, Reid G. Simmons
IROS4