Research

Research Overview

Adaptive Multi-Robot Planning

Multi-Robot Planning: To adapt to dynamic environments effectively and maximize the utility of the group, robots need to cooperate, share their information and make a suitable plan according to the specific scenario. Inspired by Maslow’s hierarchy of needs and systems theory, we form Robot’s Need Hierarchy and propose a new framework combining multi-robot task allocation, communication, planning, and execution through a newly designed negotiation and agreement protocols based on a novel priority queue mechanism.

Cooperative Herding in Multi-Robot Systems

Multi-Robot Herding: In this work, we introduce a coordinate-free multi-point rendezvous control strategy to enable multiple robots to gather at different locations by tracking their hierarchy in a connected interaction graph. A key novelty in this strategy is the gathering of robots in different groups rather than at a single consensus point, motivated by autonomous multi-point recharging and flocking control problems.

Heterogeneity in MRS

Heterogeneity in MRS – In
this work, we validate our hypothesis on the benefits of heterogeneity
through a search and rescue problem with different agent
behaviors and heterogeneous group compositions. The heterogeneity
informatics obtained from our simulations show a positive
correlation between the heterogeneity measure and the collection
speeds demonstrating benefits in most of the scenarios.

Gesture Classification using Wi-Fi

Wisture – A Solution for Gesture detection in smartphones using WiFi signals (with Mohamed Haseeb): Wisture is a new online machine learning solution for recognizing touch-less dynamic hand gestures on a smartphone. Wisture relies on the standard Wi-Fi Received Signal Strength (RSS) using a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), thresholding filters and an artificial traffic induction approach. Look at the Publications page for more information on the Wisture.

Multi-Robot Rendezvous

Multi-Robot Hierarchical Rendezvous Control: Using a hierarchical network topology tree, we devised a rendezvous control algorithm that can work on distributed fashion using the local sensing information at the robots, to collect all the robots rendezvous at a desired location in a hierarchical movement aided by the bearing-based source tracking of radio signals.

Learning Behavior Trees

Learning of Behavior Trees using Genetic Programming (with Michele Collendanchise): Taking advantage of modularity and reactiveness of Behavior Trees, we propose a model-free Automated Planned framework using Genetic Programming to derive an action plan for an autonomous agent to achieve a given goal in unknown environments. The advantages of the proposed approach are based on the advantages of BT over Finite State Machines. In particular, our approach avoids the problem of logic violation during the learning process.

Communication-Aware Motion Planing

Resilient Communication-Aware Motion Planner (RCAMP): We developed a resilient motion planner for mobile robots to find an optimal path that guarantees traversability and wireless connectivity. The RCAMP can also be used to re-establish the wireless connection in case of a communication loss with a mobile robot.

Robot Interface with Network Awareness

Human-Robot Interface (HRI) for Wireless Network Connectivity Situation Awareness in UGV Teleoperation: We extended a UGV Teleoperation Interface called Free Look Control (FLC) with the Wireless Network Connectivity Information. We used directional antennas to obtain the Direction of Arrival (DOA) (or gradients) of the Wi-Fi Received Signal Strength. Using this DoA we devised a Human-Robot Interface (HRI) that can support a teleoperator with Situation Awareness (SA) on wireless connectivity.