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Vincenzo Giovanni Scordio

dblp:74/4128 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 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
2 papers
Motion planning and robot control · 43% Knowledge representation and reasoning · 37% Autonomous driving · 20%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › inconsistency handling
conflict resolution
0.122007
Decentralized Cooperative Policy for Conflict Resolution in Multivehicle Systems · IEEE Trans. Robotics 2007
Probabilistic Verification of a Decentralized Policy for Conflict Resolution in Multi-agent Systems · ICRA 2006
Robotics › Motion planning and robot control
collision avoidance
0.122007
Decentralized Cooperative Policy for Conflict Resolution in Multivehicle Systems · IEEE Trans. Robotics 2007
Probabilistic Verification of a Decentralized Policy for Conflict Resolution in Multi-agent Systems · ICRA 2006
Robotics › Autonomous driving
multi-vehicle coordination
0.112007
Decentralized Cooperative Policy for Conflict Resolution in Multivehicle Systems · IEEE Trans. Robotics 2007
Robotics › Motion planning and robot control › multi-robot control
decentralized control
0.112006
Probabilistic Verification of a Decentralized Policy for Conflict Resolution in Multi-agent Systems · ICRA 2006

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

probabilistic verification · 0.1decentralized control · 0.1randomized experiment · 0.1
YearPublicationVenuePosition
2007 Decentralized Cooperative Policy for Conflict Resolution in Multivehicle Systems
abstract
In this paper, we propose a novel policy for steering multiple vehicles between assigned start and goal configurations, ensuring collision avoidance. The policy rests on the assumption that all agents are cooperating by implementing the same traffic rules. However, the policy is completely decentralized, as each agent decides its own motion by applying those rules only on the locally available information, and scalable, in the sense that the amount of information processed by each agent and the computational complexity of the algorithms do not increase with the number of agents in the scenario. The proposed policy applies to systems in which new vehicles may enter the scene and start interacting with existing ones at any time, while others may leave. Under mild conditions on the initial configurations, the policy is shown to be safe, i.e., it guarantees collision avoidance throughout the system evolution. In the paper, conditions are discussed on the desired configurations of agents, under which the ultimate convergence of all vehicles to their goals can also be guaranteed. To show that such conditions are actually necessary and sufficient, which turns out to be a challenging liveness-verification problem for a complex hybrid automaton, we employ a probabilistic verification method. The paper finally presents and discusses simulations for systems of several tens of vehicles, and reports on some experimental implementation showing the practicality of the approach.
Lucia Pallottino, Vincenzo Giovanni Scordio, Antonio Bicchi, Emilio Frazzoli
IEEE Trans. Robotics2
2006 Probabilistic Verification of a Decentralized Policy for Conflict Resolution in Multi-agent Systems
abstract
In this paper, we consider a decentralized cooperative control policy proposed recently for steering multiple nonholonomic vehicles between assigned start and goal configurations while avoiding collisions. The policy is known to ensure safety (i.e., collision avoidance) for an arbitrarily large number of vehicles, if initial configurations satisfy certain conditions. The method is highly scalable, and effective solutions can be obtained for several tens of autonomous agents. On the other hand, the liveness properties of the policy, i.e. the capability of negotiating a solution in finite time, are not completely understood yet. In this paper, we introduce a condition on the final vehicle configurations, which we conjecture to be necessary and sufficient for guaranteeing liveness. We prove the necessity by a constructive method. Because of the overwhelming complexity of proving the sufficiency of such condition, we assess the correctness of the conjecture in probability through the analysis of the results of a large number of randomized experiments
Lucia Pallottino, Vincenzo Giovanni Scordio, Emilio Frazzoli, Antonio Bicchi
ICRA2