Alireza Dirafzoon

Topological mapping of unknown environments using an unlocalized robotic swarm

Mapping and exploration are essential tasks for swarm robotic systems. These tasks become extremely challenging when localization information is not available. In this paper, we explore how stochastic motion models and weak encounter information can be exploited to learn topological information about an unknown environment. Our system behavior mimics a probabilistic motion model of cockroaches, as it is inspired by current biobotic (cyborg insect) systems. We employ tools from algebraic topology to extract spatial information of the environment based on neighbor to neighbor interactions among the biologically inspired agents with no need for localization data. This information is used to build a map of persistent topological features of the environment. We analyze the performance of our estimation and propose a switching control mechanism for the motion models to extract features of complex environments in an effective way.

Mapping of unknown environments using minimal sensing from a stochastic swarm

Swarms consisting of cyborg-insects or millirobots can be used for mapping and exploration of unstructured environments in emergency-response situations. Under extreme conditions, traditional localization techniques may fail to provide reliable position estimates. Instead, we propose a robust approach to obtain a topological map of an unknown environment using encounter information from a swarm of agents following a stochastic motion model via the use of tools from topological data analysis. A classification approach is introduced to determine the persistent topology features of the space. The approach is analyzed using simulation and experimental data using a swarm robotic platform called the WolfBot. For all experiments, the agents are programmed to follow a stochastic motion model and only rely on encounter information between agents to construct a map of the environment. The results indicate that the proposed approach can identify robust topological features with high accuracy.

Biobotic insect swarm based sensor networks for search and rescue

The potential benefits of distributed robotics systems in applications requiring situational awareness, such as search-and-rescue in emergency situations, are indisputable. The efficiency of such systems requires robotic agents capable of coping with uncertain and dynamic environmental conditions. For example, after an earthquake, a tremendous effort is spent for days to reach to surviving victims where robotic swarms or other distributed robotic systems might play a great role in achieving this faster. However, current technology falls short of offering centimeter scale mobile agents that can function effectively under such conditions. Insects, the inspiration of many robotic swarms, exhibit an unmatched ability to navigate through such environments while successfully maintaining control and stability. We have benefitted from recent developments in neural engineering and neuromuscular stimulation research to fuse the locomotory advantages of insects with the latest developments in wireless networking technologies to enable biobotic insect agents to function as search-and-rescue agents. Our research efforts towards this goal include development of biobot electronic backpack technologies, establishment of biobot tracking testbeds to evaluate locomotion control efficiency, investigation of biobotic control strategies with Gromphadorhina portentosa cockroaches and Manduca sexta moths, establishment of a localization and communication infrastructure, modeling and controlling collective motion by learning deterministic and stochastic motion models, topological motion modeling based on these models, and the development of a swarm robotic platform to be used as a testbed for our algorithms.

A framework for mapping with biobotic insect networks: From local to global maps

We present an approach for global exploration and mapping of unknown environments using a swarm of cyborg insects, known as biobots, for emergency response scenarios under minimal sensing and localization constraints. We exploit natural stochastic motion models and controlled locomotion of biobots in conjunction with an aerial leader to explore and map a domain of interest. A sliding window strategy is adopted to construct local maps from coordinate free encounter information of the agents by means of local metric estimation. Robust topological features from these local representations are extracted using topological data analysis and a classification scheme. These maps are then merged into a global map which can be visualized using a graphical representation, that integrates geometric as well as topological features of the environment. Simulation and experimental results with biologically inspired robotic platform are presented to illustrate and verify the correctness of our approach, which provides building blocks for SLAM with biobotic insects. A framework for mapping with biobotic insect networks is proposed.The approach consists of metric estimation from coordinate-free local encounters.Topological features of the unknown environment are extracted in a robust manner.A methodology for construction of global maps from local maps is proposed.Experimental validation with bio-inspired miniature robots is presented.

Action classification from motion capture data using topological data analysis

This paper proposes a novel framework for activity recognition from 3D motion capture data using topological data analysis (TDA). We extract point clouds describing the oscillatory patterns of body joints from the principal components of their time series using Takens delay embedding. Topological persistence from TDA is exploited to extract topological invariants of the constructed point clouds. We propose a feature extraction method from persistence diagrams in order to generate robust low dimensional features used for classification of different activities. Our experimental results demonstrate high separability of generated features, and as a result a high accuracy of classification.

Geometric Learning and Topological Inference With Biobotic Networks

In this study, we present and analyze a framework for geometric and topological estimation for mapping of unknown environments. We consider agents mimicking motion behaviors of cyborg insects, known as biobots, and exploit coordinate-free local interactions among them to infer geometric and topological information about the environment, under minimal sensing and localization constraints. A metric estimation procedure is presented over a graphical representation referred to as the encounter graph in order to construct a geometric point cloud using manifold learning techniques. Topological data analysis (TDA) along with the proposed classification method is used to infer robust topological features of the space (e.g., existence of obstacles). We examine the asymptotic behavior of the proposed metric in terms of the convergence to the geodesic distances in the underlying manifold of the domain, and provide stability analysis results for the topological persistence. The proposed framework and its convergences and stability analysis are demonstrated through numerical simulations and experiments with Hexbugs.

Biobotic motion and behavior analysis in response to directional neurostimulation

This paper presents preliminary results for motion behavior analysis of Madagascar hissing cockroach biobots subject to stochastic and periodic neurostimulation pulses corresponding to randomly applied right and left turn, and move forward commands. We present our experimental setup and propose an unguided search strategy based stimulation profile designed for exploration of unknown environments. We study a probabilistic motion model fitted to the trajectories of biobots, perturbed from their natural motion by the stimulation pulses. Furthermore, we provide a statistical assessment of the biobotic directional response to turn commands and its correlation with stimuli profile over time. This study paves the way towards reliable control for more realistic under-rubble search and rescue applications.

Exploration and topological mapping with Hexbugs

In this demonstration, we present a topological mapping system to be used with biobotic insects in order to sketch maps of unknown arenas using only neighbor to neighbor interactions among the agents. Biobotic insects fuse the locomotory advantages of insects with wireless sensing technology in form of electronic backpacks to function as search and rescue agents. Our mapping approach is designed for emergency response scenarios, where traditional mapping approaches may fail due to lack of localization information. We demonstrate the performance of our proposed approach instead using Hexbugs, which emulate the natural random motion of biobots. The Hexbugs are dispersed into a maze with unknown structure (Fig. 1), and their local interactions are captured through a visual tracking system. Such information is then exploited into a data analysis engine in order to robustly find the topological structure of the maze.

Poster Abstract: Cyborg-Insect Networks for Mapping of Unknown Environments

Cyborg-insect networks are systems that take advantage of existing biological platforms such as cockroaches [2] by attaching small instrumented payloads for sensing and motion control. These agents can be used in applications such as mapping and exploration of environment for emergency response (e.g., search and rescue operations after earthquakes, tsunamis, hurricanes, etc.) These agents can gain access to locations that may not be reachable otherwise by moving underground through smaller locations. The power limitations of such platforms place restrictions on sensing, communication, and motion control. Hence, traditional mapping and exploration techniques may not perform well under these adverse conditions. We propose a robust approach to obtain a topological map of an unknown environment using the coordinate free sensory data obtained from these cyborg-insect networks. In order to minimize control input, we take advantage of the natural behavior of insects in order to estimate a topological model of the environment based only on neighbor-to-neighbor interactions.