63 research outputs found
Multi-Agent Persistent Task Performance
A method to control a system of robots to persistently perform a task while operating under a constraint such as battery life is presented. Persistently performing a task is defined as continuously executing the task without a break or stopping due to low battery constraints or lack of capabilities of a particular agent. If an agent is no longer able to execute the task it must be replaced by one that can continue the execution of the task. This is achieved through the utilization of two distinctions of agent roles: workers and helpers. This method is focused on addressing problems that require task handoffs where a second robot physically replaces a robot that has run low on battery. The worker agents are assigned the tasks, and perform the tasks until the constraint prevents further performance. Once a worker agent has reached a low battery threshold a task handoff is performed with a helper agent. This method utilizes a proactive approach in performing these handoffs by predicting the time and place that a worker will reach a low battery threshold and need to perform a handoff. This decreases the time necessary to respond to a low battery in these problems compared to prior developed reactive methods. As a result the total time needed by the multi agent team to complete a set of tasks is decreased. In this paper, the method is demonstrated utilizing a physics based simulator to model the behavior of the multi agent team. Experiments are run over three standard problems requiring agent task handoffs: sentry, inspection, and coverage. These demonstrate the effectiveness of the method when compared against the existing reactive methods
Multi-Agent Persistent Task Performance
A method to control a system of robots to persistently perform a task while operating under a constraint such as battery life is presented. Persistently performing a task is defined as continuously executing the task without a break or stopping due to low battery constraints or lack of capabilities of a particular agent. If an agent is no longer able to execute the task it must be replaced by one that can continue the execution of the task. This is achieved through the utilization of two distinctions of agent roles: workers and helpers. This method is focused on addressing problems that require task handoffs where a second robot physically replaces a robot that has run low on battery. The worker agents are assigned the tasks, and perform the tasks until the constraint prevents further performance. Once a worker agent has reached a low battery threshold a task handoff is performed with a helper agent. This method utilizes a proactive approach in performing these handoffs by predicting the time and place that a worker will reach a low battery threshold and need to perform a handoff. This decreases the time necessary to respond to a low battery in these problems compared to prior developed reactive methods. As a result the total time needed by the multi agent team to complete a set of tasks is decreased. In this paper, the method is demonstrated utilizing a physics based simulator to model the behavior of the multi agent team. Experiments are run over three standard problems requiring agent task handoffs: sentry, inspection, and coverage. These demonstrate the effectiveness of the method when compared against the existing reactive methods
Feedback-based Information Roadmap (FIRM): Graph-based Estimation and Control of Robotic Systems Under Uncertainty
This dissertation addresses the problem of stochastic optimal control with imperfect
measurements. The main application of interest is robot motion planning under
uncertainty. In the presence of process uncertainty and imperfect measurements, the
system's state is unknown and a state estimation module is required to provide the
information-state (belief), which is the probability distribution function (pdf) over
all possible states. Accordingly, successful robot operation in such a setting requires
reasoning about the evolution of information-state and its quality in future time
steps. In its most general form, this is modeled as a Partially-Observable Markov
Decision Process (POMDP) problem. Unfortunately, however, the exact solution of
this problem over continuous spaces in the presence of constraints is computationally
intractable. Correspondingly, state-of-the-art methods that provide approximate solutions
are limited to problems with short horizons and small domains. The main
challenge for these problems is the exponential growth of the search tree in the information
space, as well as the dependency of the entire search tree on the initial
belief.
Inspired by sampling-based (roadmap-based) methods, this dissertation proposes
a method to construct a "graph" in information space, called Feedback-based Information
RoadMap (FIRM). Each FIRM node is a probability distribution and each
FIRM edge is a local controller. The concept of belief stabilizers is introduced as a
way to steer the current belief toward FIRM nodes and induce belief reachability.
The solution provided by the FIRM framework is a feedback law over the information
space, which is obtained by switching among locally distributed feedback controllers.
Exploiting such a graph in planning, the intractable POMDP problem over continuous spaces is reduced to a tractable MDP (Markov Decision Process) problem
over the graph (FIRM) nodes. FIRM is the first graph generated in the information
space that preserves the principle of optimality, i.e., the costs associated with different
edges of FIRM are independent of each other. Unlike the forward search methods
on tree-structures, the plans produced by FIRM are independent of the initial belief
(i.e., plans are query-independent). As a result, they are robust and reliable. They
are robust in the sense that if the system's belief deviates from the planned belief,
then replanning is feasible in real-time, as the computed solution is a feedback over
the entire belief graph. Computed plans are reliable in the sense that the probability
of violating constraints (e.g., hitting obstacles) can be seamlessly incorporated into
the planning law. Moreover, FIRM is a scalable framework, as the computational
complexity of its construction is linear in the size of underlying graph as opposed to
state-of-the-art methods whose complexity is exponential in the size of underlying
graph.
In addition to the abstract framework, we present concrete FIRM instantiations
for three main classes of robotic systems: holonomic, nonholonomic, and non-pointstabilizable.
The abstract framework opens new avenues for extending FIRM to a
broader class of systems that are not considered in this dissertation. This includes
systems with discrete dynamics or in general systems that are not well-linearizable,
systems with non-Gaussian distributions, and systems with unobservable modes. In
addition to the abstract framework and concrete instantiations of it, we propose
a formal technique for replanning with FIRM based on a rollout-policy algorithm
to handle changes in the environment as well as discrepancies between actual and
computational models. We demonstrate the performance of the proposed motion
planning method on different robotic systems, both in simulation and on physical
systems. In the problems we consider, the system is subject to motion and sensing
noise. Our results demonstrate a significant advance over existing approaches for
motion planning in information space. We believe the proposed framework takes an
important step toward making information space planners applicable to real world
robotic applications
Adaptive tactical behaviour planner for autonomous ground vehicle
Success of autonomous vehicle to effectively
replace a human driver depends on its ability to plan safe,
efficient and usable paths in dynamically evolving traffic
scenarios. This challenge gets more difficult when the
autonomous vehicle has to drive through scenarios such as
intersections that demand interactive behavior for successful
navigation. The many autonomous vehicle demonstrations over
the last few decades have highlighted the limitations in the
current state of the art in path planning solutions. They have
been found to result in inefficient and sometime unsafe
behaviours when tackling interactively demanding scenarios. In
this paper we review the current state of the art of path planning
solutions, the individual planners and the associated methods
for each planner. We then establish a gap in the path planning
solutions by reviewing the methods against the objectives for
successful path planning. A new adaptive tactical behaviour
planner framework is then proposed to fill this gap. The
behaviour planning framework is motivated by how expert
human drivers plan their behaviours in interactive scenarios.
Individual modules of the behaviour planner is then described
with the description how it fits in the overall framework. Finally
we discuss how this planner is expected to generate safe and
efficient behaviors in complex dynamic traffic scenarios by
considering a case of an un-signalised roundabout
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