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R. Bhatt

dblp:17/4666 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1987
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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
Motion planning and robot control · 64% Robot navigation and mapping · 28% Autonomous driving · 8%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
motion planning
0.011987
A real-time guidance system for an autonomous vehicle · ICRA 1987
Robotics › Robot navigation and mapping
obstacle avoidance
0.011987
A real-time guidance system for an autonomous vehicle · ICRA 1987
Robotics › Motion planning and robot control › motion planning › real-time motion planning
real-time trajectory generation
0.011987
A real-time guidance system for an autonomous vehicle · ICRA 1987
Robotics › Autonomous driving › vehicle control
autonomous vehicle control
0.011987
A real-time guidance system for an autonomous vehicle · ICRA 1987
Robotics › Motion planning and robot control › robot control
hierarchical control
0.011987
A real-time guidance system for an autonomous vehicle · ICRA 1987

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

rule-based control · 0.0heuristic search · 0.0
YearPublicationVenuePosition
1987 A real-time guidance system for an autonomous vehicle
abstract
A real-time system for the control of an autonomous vehicle consisting of a nested hierarchy of control modules is described. The focus is on the PILOT module which provdies the real-time guidance of the system. It is responsible for the generation and tracking of dynamically feasible trajectories which follow the generally planned path and avoid local obstacles. The PILOT operation is based on a combination of simple rules and heuristic search. The state space consists of two parts: the lower level state space which represents the model of the world as a set of relationships among the units of vehicle state variables representing steering, acceleration, and velocity, as well as positions of obstacles, and the upper level state space where the relationships among generalized objects are represented as rules. Rules provide simple elementary maneuvers, and are applied when the upper level situation is easily recognized. Otherwise the situations are handled by the lower level search. Example runs of the vehicle are simulated to demonstrate the complexity of behavior. The algorithm has also been tested on a gas powered dune buggy developed at Drexel University.
R. Bhatt, D. Gaw, Alex Meystel
ICRA1