I have been working on a variety of research topics, ranging from stochastic
control and information (belief) space planning, to simultaneous localization
and mapping for mobile robots, robot vision, cooperative planning, and rescue
robotics.

Traditionally, the perception and estimation
modules serve the controller by providing an estimate of the system's state.
I am interested in methods where the controller serves back the perception and estimation
modules by taking the available information
in the environment into account. This often can be formulated as a planning problem
in the information space.
As a result, the controller takes the system's state toward goal while taking maneuvers that
steer the sensory
information to improve perception,
localization, and mapping accuracy, and hence achieve safer robot navigation.

Focused Mapping for Resource-Constrained Collision-Free Autonomous Navigation

Long-term operations of resource-constrained
robots typically require hard decisions be made about which
data to process and/or retain. The question then arises of how
to choose which data is most useful to keep to achieve the
task at hand. As spatial scale grows, the size of the map will
grow without bound, and as temporal scale grows, the number
of measurements will grow without bound. In this work, we
present the first known approach to tackle both of these issues.
The approach has two stages. First, a subset of the variables
(focused variables) is selected that are most useful for a particular
task. Second, a task-agnostic and principled method (focused
inference) is proposed to select a subset of the measurements
that maximizes the information over the focused variables. The
approach is then applied to the specific task of robot navigation in
an obstacle-laden environment. A landmark selection method is
proposed to minimize the probability of collision and then select
the set of measurements that best localizes those landmarks. It
is shown (in simulation and on physical systems) that the two-stage approach outperforms both only
selecting measurement and only selecting landmarks in terms
of minimizing the probability of collision.

Multi-robot Planning Under Uncertainty Using Decentralized Control of POMDPs via Macro-actions

The focus of this work is on solving multi-robot planning problems in continuous spaces with partial observability.
Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination
problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level
representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper
extends the Dec-POMDP model to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP).
The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains.
We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can
incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce
robust solutions. The proposed method’s performance is evaluated on a complex multi-robot package delivery problem under
uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for
large-scale problems.

Persistent Package Delivery Under Uncertainty Using Quad-copters

Currently there is a desire for the use of quadcopters in persistent missions. Unfortunately, these vehicles commonly face battery and fuel degradation issues. This work centers on development of a planner for long-endurance robotics missions that necessitate health (system’s correct functionality) management in partially observable Markov decision processes (POMDP). Specifically,
we focus on the computational problem involving the extended endurance of quadrotors implemented for package delivery. To handle uncertainties in the real world applications (such as wind, measurement noise, etc) we plan in the space of probability distributions over both the vehicle-level and mission-level states. The former consideration includes parameters like vehicle location while the latter involves vehicular health and capabilities. We reduce online planning for mission-level states, performed by forward tree searches in the space of probability distributions, by utilizing the information roadmaps. In doing so, we enable the quadrotors to take preventive measures (such as going to a recharge/repair station) during their missions to preserve their health. We have proposed novel algorithms and a corresponding framework, presenting simulation results for dynamic health and partial observability in a persistent payload delivery mission with limited fuel.

Window into Belief Space

Hardware-in-the-loop experiments are an essential step for transitioning implementation of planning
and learning algorithms from simulations to real systems.
During execution of algorithms, the
planner/learner manipulates numerous latent variables such as probability distributions over state
of the system and environment, predicted robot trajectories and transition probabilities. In this work, we
describe a new visualization system that consists of multiple projectors and a real-time animation
software that is tailored towards presenting the belief space of multi-agent planning and learning
algorithms. We have implemented the visualization system in Aerospace Controls Lab's RAVEN indoor
flight testbed at MIT. This system utilizes 18 Vicon motion-capture cameras tracking multiple heterogeneous
robotic vehicle platforms. State and latent information for the vehicles are published using ROS
(Robot Operating System), allowing a computer dedicated to visualization-rendering to package
this information in an intuitive format for designers and spectators. The
visualization is then projected onto the experiment area, with latent data and
physical systems being run synchronously. This allows designers and spectators to observe hardware while simultaneously gaining an intuitive understanding of underlying
decisions made by the algorithm.

Below are the some examples on how this system has been utilized for different projects in our lab.

Multi-robot (aerial & ground) coordination

RRT based path planning

Forest Fire Management

Multi-robot Path Planning

Robust Real-time Replanning in Belief Space in Changing Environments (2013)

We have devised a principled way of real-time replanning in
belief space via realization of the rollout policy based on the FIRM framework.
This video demonstrates the performance of the method on physical mobile robots
over a long (about 25 minutes) and complex run. In this run, we investigate how
such a real-time replanning method can generate a feedback plan that is robust
to discrepancies between real models and computational models as well as robust
to changes in the environment, failures in the sensory system, and large
deviations from the nominal plan. We believe these framework lay the ground work
for further advancing the theoretical POMDP framework toward practical
applications, and achieving long-term autonomy in robotic systems.

Dynamic replanning in belief space using Information RoadMaps (IRM) --
(2011-2012)

Information RoadMap (IRM) is the first/only multi-query roadmap (graph) in the belief space (information space), i.e., the space of all possible probability distribution over the system state. Since it is a multi-query graph, IRM makes it possible to dynamically re-plan in real-time, in case of large deviations. In this video each FIRM node (Gaussian pdf) is shown by a blue mean and black covariance. The system belief is shown by red mean and covariance. The true system (unknown) is shown in green. When the true state (green) is pushed away from its nominal plan, IRM can replan in
real-time by putting a new node and locally connecting it to the neighboring nodes, while the rest of graph remains unchanged.
[paper].

FIRM (Feedback-based Information RoadMap) -- (2011-2012)

FIRM is a sampling-based framework for motion planning under
uncertainty. It takes into account both process (motion) uncertainty and sensing
uncertainty. It relies on "stabilizers" that drive the system belief (posterior
distribution of the filter) to predefined beliefs (FIRM nodes). This
stabilization leads to desirable features such as robustness, reliability, and
scalability. In this video the FIRM nodes (Gaussian distributions) are shown by
a blue mean and black covariance. The system belief is shown by red mean and
covariance. The true system (unknown) is shown in green. The closer the robot is
to information sources (black stars) the less sensing uncertainty. Based on the
FIRM plan, the robot detours from the shortest path to a more informative path
on which the filter and the controller work better and can minimize the
collision probability. We have designed different varaints of FIRM for linearly
stabilizable systems, [paper],
non-holonomic systems, [paper],
and non-stoppable systems, [paper].

Cooperative Multi-robot Motion Planning Under Uncertainty

Stochastic cooperative planning under uncertainty aims at exploiting capabilities of the different (possibly heterogeneous) agents in a team to increase the chance of successfully performing a given task under uncertainty. In this video the goal is to reach a final team configuration, while minimizing the collision probability. There are tree robots, whose Gaussian belief (state pdf) is shown. The red robot has access to a GPS and two other robots can only measure their relative positions with respect to the red robot. However, the closer the robots, the less the measurement uncertainty. Using information roadmaps for cooperative control, the emergent behavior is interesting: the red robot detours from its shortest path and helps the other robots to shrink their uncertainty and reach the goal configuration with a minimum collision probability.

Uncertainty Propagation in Nonlinear Dynamical Systems through Fokker-Planck-Kolmogorov PDE
(2010)

Fokker-Planck-Kolmogorov Equation (FPKE) is a parabolic partial differential equation that lies at the heart of the uncertainty propagation problem and nonlinear filtering because it captures the exact description of evolution of the state probability density function (pdf) through continuous dynamical systems. I worked on solving this equation numerically based on Finite Element Methods (FEM) using
dealii package. This video shows the evolution of the system pdf through the 2D stochastic nonlinear Duffing oscillator system. As expected, the pdf converges to its bimodal non-Gaussian stationary distribution.

Medical Needle Steering Using Information Roadmaps

Robot-assisted medical procedures, such as medical needle
steering, require safe and accurate motions. Information RoadMaps (IRM)
seamlessly incorporate the constraints into the control problem and thus the
planner can find a feedback that minimizes the probability of collision with
sensitive regions in tissue. The left figure shows a Gd-EOB-DTPA–enhanced 3D
gradient-echo MRI of a 64-y-old patient with liver metastases due to breast
cancer. The right figure shows results of two sample runs using IRM. Due to the
feedback nature of the solution, the path is determined at run-time based on the
tissue deformation and sensing noise. [poster]

Detecting and Modeling Planar Mirrors in The Environment for Mobile Robot Navigation Purposes
(2009)

We devised a method for the detection and recognition of a
large planar mirror based on the images captured by a monocular camera. We derive a mirror transformation matrix in a homogeneous coordinate and
geometric constraints for corresponding real and virtual feature points in the
image. We find that existing feature detection methods are not reflection
invariant. We introduce a secondary artificial reflection to virtual features to
generate secondary features which are proven to share a rigid body motion
relationship with the original feature set. Finally, we propose an iterative strategy to
adjust the secondary mirror configuration so that existing feature matching
methods can be used. The combined method yields a robust mirror detection
algorithm which has been verified in physical experiments. [paper]

Rescue Robotics (2006-2008)

I was a member of
Resquake robotics group,
at KN Toosi University of Technology, from 2006 to 2008. The core team members were Ehsan Mihankhah, Arash Kalantari, Ehsan Aboosaeidan, Hesam Semsarilar, and Ali Jazayeri, experts in rescue robotics.
The team won many prizes in national and international rescue robotics contests. Resquake built different tele-operated and autonomous robots, and my
responsibility in this group was designing algorithms for Autonomous Navigation
and SLAM in rescue environments, for the autonomous robot.

LV-SLAM (2007-2008)

Fusing the information of a monocular camera and range
scanners for SLAM in mobile robots.

Feature-based Range Scan Matching and LRF-based EKF-SLAM (2006-2007)

This project is concerned with the feature extraction and data
associations algorithms in dealing with laser range finders. The proposed
method uses global information of the whole scan data and local information
around feature points to perform data association.

Consistency Analyses of EKF-based SLAM

This work is concerned with the consistency analysis and aims to improve the
process consistency while reducing the computational cost.

Hierarchical Q-Learning (2005)

Hierarchical reinforcement learning attempts to cope with “curse of dimensionality” by decomposing the state space to the simpler parts. In this
work, we decompose the tasks to simpler behaviors, which are connected to each other in
a subsumption architecture. Methods for updating the value functions, credit assignment, and structure learning are studied in the proposed framework.

Path Planning using Hopfield ANN (2005)

In this project, we were using Hopfield Artificial Neural Networks (ANNs) to find a path from start point to destination, in the presence of obstacles. The method is based on energy propagation concepts through neurons of the recurrent Hopfield network to build a surface, whose maximum gradient
at every point guides the robot toward the goal point in an efficient way.

ACO for solving TSP (2004)

Solving Traveling Salesman Person problem (TSP) using Ant Colony System-based optimization (ACO) through adding a local optimizer.

RoboSoccer Project (2003-2004)

This cool project was my second serious project in robotics
in my undergraduate studies. We were a team of 8 junior or senior students, building small-size soccer robots for Robocup competitions. I was the
leader of this team during 2003-2005, before I leave Tabriz University for my
graduate studies.