7,888 research outputs found
Improving the Parallel Execution of Behavior Trees
Behavior Trees (BTs) have become a popular framework for designing
controllers of autonomous agents in the computer game and in the robotics
industry. One of the key advantages of BTs lies in their modularity, where
independent modules can be composed to create more complex ones. In the
classical formulation of BTs, modules can be composed using one of the three
operators: Sequence, Fallback, and Parallel. The Parallel operator is rarely
used despite its strong potential against other control architectures as Finite
State Machines. This is due to the fact that concurrent actions may lead to
unexpected problems similar to the ones experienced in concurrent programming.
In this paper, we introduce Concurrent BTs (CBTs) as a generalization of BTs in
which we introduce the notions of progress and resource usage. We show how CBTs
allow safe concurrent executions of actions and we analyze the approach from a
mathematical standpoint. To illustrate the use of CBTs, we provide a set of use
cases in robotics scenarios
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
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