14,091 research outputs found
Quadrotor control for persistent surveillance of dynamic environments
Thesis (M.S.)--Boston UniversityThe last decade has witnessed many advances in the field of small scale unmanned aerial vehicles (UAVs). In particular, the quadrotor has attracted significant attention. Due to its ability to perform vertical takeoff and landing, and to operate in cluttered spaces, the quadrotor is utilized in numerous practical applications, such as reconnaissance and information gathering in unsafe or otherwise unreachable environments.
This work considers the application of aerial surveillance over a city-like environment. The thesis presents a framework for automatic deployment of quadrotors to monitor and react to dynamically changing events. The framework has a hierarchical structure. At the top level, the UAVs perform complex behaviors that satisfy high- level mission specifications. At the bottom level, low-level controllers drive actuators on vehicles to perform the desired maneuvers.
In parallel with the development of controllers, this work covers the implementation of the system into an experimental testbed. The testbed emulates a city using physical objects to represent static features and projectors to display dynamic events occurring on the ground as seen by an aerial vehicle. The experimental platform features a motion capture system that provides position data for UAVs and physical features of the environment, allowing for precise, closed-loop control of the vehicles. Experimental runs in the testbed are used to validate the effectiveness of the developed control strategies
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
CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments
In this paper we study a new reinforcement learning setting where the
environment is non-rewarding, contains several possibly related objects of
various controllability, and where an apt agent Bob acts independently, with
non-observable intentions. We argue that this setting defines a realistic
scenario and we present a generic discrete-state discrete-action model of such
environments. To learn in this environment, we propose an unsupervised
reinforcement learning agent called CLIC for Curriculum Learning and Imitation
for Control. CLIC learns to control individual objects in its environment, and
imitates Bob's interactions with these objects. It selects objects to focus on
when training and imitating by maximizing its learning progress. We show that
CLIC is an effective baseline in our new setting. It can effectively observe
Bob to gain control of objects faster, even if Bob is not explicitly teaching.
It can also follow Bob when he acts as a mentor and provides ordered
demonstrations. Finally, when Bob controls objects that the agent cannot, or in
presence of a hierarchy between objects in the environment, we show that CLIC
ignores non-reproducible and already mastered interactions with objects,
resulting in a greater benefit from imitation
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