779 research outputs found
A Dynamical System Approach for Resource-Constrained Mobile Robotics
The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots.
First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment.
Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated.
Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios.
Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view.
In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system
Optimal Event-Driven Multi-Agent Persistent Monitoring of a Finite Set of Targets
We consider the problem of controlling the movement of multiple cooperating
agents so as to minimize an uncertainty metric associated with a finite number
of targets. In a one-dimensional mission space, we adopt an optimal control
framework and show that the solution is reduced to a simpler parametric
optimization problem: determining a sequence of locations where each agent may
dwell for a finite amount of time and then switch direction. This amounts to a
hybrid system which we analyze using Infinitesimal Perturbation Analysis (IPA)
to obtain a complete on-line solution through an event-driven gradient-based
algorithm which is also robust with respect to the uncertainty model used. The
resulting controller depends on observing the events required to excite the
gradient-based algorithm, which cannot be guaranteed. We solve this problem by
proposing a new metric for the objective function which creates a potential
field guaranteeing that gradient values are non-zero. This approach is compared
to an alternative graph-based task scheduling algorithm for determining an
optimal sequence of target visits. Simulation examples are included to
demonstrate the proposed methods.Comment: 12 pages full version, IEEE Conference on Decision and Control, 201
Information Acquisition with Sensing Robots: Algorithms and Error Bounds
Utilizing the capabilities of configurable sensing systems requires
addressing difficult information gathering problems. Near-optimal approaches
exist for sensing systems without internal states. However, when it comes to
optimizing the trajectories of mobile sensors the solutions are often greedy
and rarely provide performance guarantees. Notably, under linear Gaussian
assumptions, the problem becomes deterministic and can be solved off-line.
Approaches based on submodularity have been applied by ignoring the sensor
dynamics and greedily selecting informative locations in the environment. This
paper presents a non-greedy algorithm with suboptimality guarantees, which does
not rely on submodularity and takes the sensor dynamics into account. Our
method performs provably better than the widely used greedy one. Coupled with
linearization and model predictive control, it can be used to generate adaptive
policies for mobile sensors with non-linear sensing models. Applications in gas
concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014
IEEE International Conference on Robotics and Automatio
Cooperative Periodic Coverage With Collision Avoidance
In this paper, we propose a periodic solution to the problem of persistently covering a finite set of interest points with a group of autonomous mobile agents. These agents visit periodically the points and spend some time carrying out the coverage task, which we call coverage time. Since this periodic persistent coverage problem is NP-hard, we split it into three subproblems to counteract its complexity. In the first place, we plan individual closed paths for the agents to cover all the points. Second, we formulate a quadratically constrained linear program to find the optimal coverage times and actions that satisfy the coverage objective. Finally, we join together the individual plans of the agents in a periodic team plan by obtaining a schedule that guarantees collision avoidance. To this end, we solve a mixed-integer linear program that minimizes the time in which two or more agents move at the same time. Eventually, we apply the proposed solution to an induction hob with mobile inductors for a domestic heating application and show its performance with experiments on a real prototype. IEE
Information-Driven Path Planning for UAV with Limited Autonomy in Large-scale Field Monitoring
This paper presents a novel information-based mission planner for a drone
tasked to monitor a spatially distributed dynamical phenomenon. For the sake of
simplicity, the area to be monitored is discretized. The insight behind the
proposed approach is that, thanks to the spatio-temporal dependencies of the
observed phenomenon, one does not need to collect data on the entire area. In
fact, unmeasured states can be estimated using an estimator, such as a Kalman
filter. In this context the planning problem becomes the one of generating a
flight path that maximizes the quality of the state estimation while satisfying
the flight constraints (e.g. flight time). The first result of this paper is to
formulate this problem as a special Orienteering Problem where the cost
function is a measure of the quality of the estimation. This approach provides
a Mixed-Integer Semi-Definite formulation to the problem which can be optimally
solved for small instances. For larger instances, two heuristics are proposed
which provide good sub-optimal results. To conclude, numerical simulations are
shown to prove the capabilities and efficiency of the proposed path planning
strategy. We believe this approach has the potential to increase dramatically
the area that a drone can monitor, thus increasing the number of applications
where monitoring with drones can become economically convenient
Persistent Monitoring of Events with Stochastic Arrivals at Multiple Stations
This paper introduces a new mobile sensor scheduling problem, involving a
single robot tasked with monitoring several events of interest that occur at
different locations. Of particular interest is the monitoring of transient
events that can not be easily forecast. Application areas range from natural
phenomena ({\em e.g.}, monitoring abnormal seismic activity around a volcano
using a ground robot) to urban activities ({\em e.g.}, monitoring early
formations of traffic congestion using an aerial robot). Motivated by those and
many other examples, this paper focuses on problems in which the precise
occurrence times of the events are unknown {\em a priori}, but statistics for
their inter-arrival times are available. The robot's task is to monitor the
events to optimize the following two objectives: {\em (i)} maximize the number
of events observed and {\em (ii)} minimize the delay between two consecutive
observations of events occurring at the same location. The paper considers the
case when a robot is tasked with optimizing the event observations in a
balanced manner, following a cyclic patrolling route. First, assuming the
cyclic ordering of stations is known, we prove the existence and uniqueness of
the optimal solution, and show that the optimal solution has desirable
convergence and robustness properties. Our constructive proof also produces an
efficient algorithm for computing the unique optimal solution with time
complexity, in which is the number of stations, with time
complexity for incrementally adding or removing stations. Except for the
algorithm, most of the analysis remains valid when the cyclic order is unknown.
We then provide a polynomial-time approximation scheme that gives a
-optimal solution for this more general, NP-hard problem
Cooperative Periodic Coverage With Collision Avoidance
In this paper we propose a periodic solution to the problem of persistently
covering a finite set of interest points with a group of autonomous mobile
agents. These agents visit periodically the points and spend some time carrying
out the coverage task, which we call coverage time. Since this periodic
persistent coverage problem is NP-hard, we split it into three subproblems to
counteract its complexity. In the first place, we plan individual closed paths
for the agents to cover all the points. Second, we formulate a quadratically
constrained linear program to find the optimal coverage times and actions that
satisfy the coverage objective. Finally, we join together the individual plans
of the agents in a periodic team plan by obtaining a schedule that guarantees
collision avoidance. To this end, we solve a mixed integer linear program that
minimizes the time in which two or more agents move at the same time.
Eventually, we apply the proposed solution to an induction hob with mobile
inductors for a domestic heating application and show its performance with
experiments on a real prototype.Comment: This is the accepted version an already published manuscript. See
journal reference for detail
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