Demo Data from EDBT 2026 / reviewers in the wild / expert
Jordan H. Boyle
dblp:92/5336
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-4219-9383ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › continuum robot
continuum robot design |
0.4 | 1 | 2019 | Design of a Modular Continuum Robot Segment for use in a General Purpose Manipulator* · ICRA 2019 |
Robotics › Robot manipulation
grasping |
0.1 | 1 | 2019 | Design of a Modular Continuum Robot Segment for use in a General Purpose Manipulator* · ICRA 2019 |
Methods — techniques the papers use, named apart from their topics
multi-material 3d printing · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Swarm Foraging Under Communication and Vision UncertaintiesabstractSwarm foraging is a common test case application for multi-robot systems. In this paper RepAtt algorithm is used for improving coordination of a robot swarm by selectively broadcasting repulsion and attraction signals. This is a chemotaxis-inspired search behaviour where robots use the temporal gradients of these signals to navigate towards more advantageous areas. Hardware experiments were used to model and validate realistic, noisy sound communication and vision system. We then show through extensive simulation studies that RepAtt significantly improves swarm foraging time and robot efficiency under realistic communication and vision models.Note to Practitioners—This research developed a swarm foraging algorithm that takes into consideration the vision and communication sensing noise levels faced by robots in real world applications. The algorithm, known as RepAtt, was developed with the aim of emphasizing algorithmic simplicity and limiting the hardware requirements for the robots in the swarm. In this paper, we have focused on the problem of deploying swarm robots to forage litter in an environment such as a park. The communication model of the robots was based on the physics of sound, while their vision system was modelled using experiments with deep neural networks based object detectors. The results show that the RepAtt algorithm is robust to different distributions of targets (or litter) in the search space, exhibits good swarm efficiency with changes in swarm population and is robust to noise in its communication and vision systems. Apart from the RepAtt algorithm, other contributions made by this research include modelling of robot vision system to aid extensive study of the impact of communication and vision noise on swarm coordination. This will be relevant for extensive testing and validation before deployment to swarm robots hardware. The sound communication used in this research limits the kinds of environment the robots can be deployed in. Echoes within an enclosed environment and bandwidth limitation for communication frequency and public disturbance due to sound emitted by the robots can all contribute to this limitation. Thus, this research can be improved by investing in the development of a communication technology with similar physics. Other areas of improvement include adopting better obstacle avoidance algorithms and implementing suitable manipulators for handling litter objects. The algorithm can be extended to make it applicable for solving other problems such as search and rescue operations where foraging targets could be disaster survivors; demining and hazardous waste cleanup, where targets are the mines or waste material; and planetary exploration, where targets could be interesting features of the planets are the targets searched for by the robots. Simon O. Obute, Philip Kilby, Mehmet Remzi Dogar, Jordan H. Boyle |
IEEE Trans Autom. Sci. Eng. | 4 |
| 2019 | Design of a Modular Continuum Robot Segment for use in a General Purpose Manipulator*abstractThis paper presents the development of a tendon-driven continuum robot segment with a modular design, simple construction and significant lifting capabilities. The segment features a continuous flexible core combined with rigid interlocking vertebrae evenly distributed along its length. This design allows bending in two degrees of freedom while minimising torsional movement. The segment is actuated by two antagonistic tendon pairs, each of which is driven by a single geared DC motor. Modularity is achieved by embedding these motors in one end of the segment, avoiding the need for a bulky actuation unit and allowing variable numbers of segments to be connected. The design features a large hollow central bore which could be used as a vacuum channel for suction-assisted gripping or to allow ingress and egress of fluids. The design process goes through four iterations, the final two of which are subjected to quantitative experiments to evaluate workspace, lifting capabilities and torsional rigidity. All iterations are fabricated using multi-material 3D printing, which allows the entire structure to be printed as a pre-assembled unit with the rigid vertebrae fused to the flexible core. Assembly is then a simple case of inserting the motors and connecting the tendons. This unconventional manufacturing approach is found to be efficient, effective and relatively cheap. Nicholas P. Castledine, Jordan H. Boyle, Jongrae Kim |
ICRA | 2 |
| 2007 | An Integrated Neuro-mechanical Model of C. elegansForward Locomotion
Jordan H. Boyle, John Bryden, Netta Cohen |
ICONIP (1) | 1 |