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Philip J. Knoghtbridge

dblp:86/3309 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1995
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

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
Planning, search and constraint satisfaction · 61% Reinforcement learning · 30% Motion planning and robot control · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
hierarchical planning
0.011995
Task Planning and World Modelling for Supervisory Control of Robots in unstructured Environments · ICRA 1995
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
task planning
0.011995
Task Planning and World Modelling for Supervisory Control of Robots in unstructured Environments · ICRA 1995
Machine learning › Reinforcement learning › model-based reinforcement learning
world model
0.011995
Task Planning and World Modelling for Supervisory Control of Robots in unstructured Environments · ICRA 1995
Robotics › Motion planning and robot control › robot control › hierarchical control
supervisory control
0.011995
Task Planning and World Modelling for Supervisory Control of Robots in unstructured Environments · ICRA 1995

Methods — techniques the papers use, named apart from their topics

sensor fusion · 0.0
YearPublicationVenuePosition
1995 Task Planning and World Modelling for Supervisory Control of Robots in unstructured Environments
abstract
We describe the design of a task planning and world modelling mechanism as part of a supervisory or teleassistance control scheme for robots operating in unstructured environments. The mechanism addresses the key difficulties of an unstructured environment which lead to failures in task execution. Reactivity is achieved by mixing planning and execution. Complexity of task and error recovery is managed by hierarchical design of action sequences. The world model and task planner are intimately linked to allow parameterization of actions, and for the operator to focus on involved objects in the world. They work as part of a functional architecture with motion planning, dynamic control and sensor fusion processes, and have been implemented to specify the activity of a pair of underwater manipulators, sensed using force/torque loadcell, joint potentiometers, high frequency sonar and laser striping.
David M. Lane, Philip J. Knoghtbridge
ICRA2