Akshaya Kesarimangalam Srinivasan

Carnegie Mellon University

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Howie Choset

Carnegie Mellon University

Bhaskar Vundurthy

Carnegie Mellon University

Geordan Gutow

Carnegie Mellon University

Zhongqiang Ren

Shanghai Jiao Tong University

Ian Abraham

Yale University

Shambhavi Singh

Carnegie Mellon University

Shwetha Sridharan

National Institute of Technology Tiruchirappalli

Rajeswari Sridhar

National Institute of Technology Tiruchirappalli

Howard Coffin

Carnegie Mellon University

Z. Huang

Carnegie Mellon University

Cooperative Institutions

Carnegie Mellon University

National Institute of Technology Tiruchirappalli

Texas A&M University

Anna University, Chennai

Georgia Institute of Technology

Government Hospital of Thoracic Medicine

SRM Dental College

Ramakrishna Mission Vidyalaya

Karpagam Academy of Higher Education

Stanley Medical College

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Multi-Agent Multi-Objective Ergodic Search Using Branch and Bound

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023)

Akshaya Kesarimangalam Srinivasan Geordan Gutow Zhongqiang Ren Ian Abraham Bhaskar Vundurthy

Search and rescue applications often need multiple agents to complete a set of conflicting tasks. This paper studies a Multi-Agent Multi-Objective Ergodic Search (MA-MO-ES) approach to this problem where each objective or task is to cover a domain subject to an information map. The goal is to allocate coverage tasks to agents so that all maps are explored ergodically. The combinatorial nature of task allocation makes it computationally expensive to solve for optimal allocation using brute force. Apart from a large number of possible allocations, computing the cost of a task allocation is itself an expensive planning problem. To mitigate the computational challenge, we present a branch and bound-based algorithm with pruning techniques that reduce the number of allocations to be searched to find optimal coverage task allocation. We also present an approach to leverage the similarity between information maps to further reduce computation. Extensive testing on 147 randomly generated test cases shows an order of magnitude improvement in runtime compared to an exhaustive brute force approach.

Leverage (statistics)

Pruning

10.1109/iros55552.2023.10341353

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Object Localization and Navigation Assistant for the Visually challenged

Akshaya Kesarimangalam Srinivasan Shwetha Sridharan Rajeswari Sridhar

Locating an item in a room or navigating to it is remaining as a significant bottleneck for visually impaired people. In this paper, we have devised a methodology to detect the presence of a target object and evaluate its relative depth from the user to return navigation directions. The user will be led to the target and issued warnings about the presence of obstacles using a text to speech engine. The proposed system employs a pipeline consisting of real-time multiple object detection, depth calculation, and text to speech components. For object detection, SSDLite MobilenetV2 model has been trained with the ms coco dataset using TensorFlow. The information is obtained using triangulation with stereo vision. Stereo vision is simulated using two Logitech c270 HD web cameras mounted on a T-shaped bracket. The position and size of the bounding boxes of object detection and depth data are used to obtain the 3D coordinates of both the target and obstacles. This prototype has been tested with objects belonging to the ms coco dataset and successfully leads the user to everyday items like cups and handbags. The current algorithm can be improved with respect to the number of object classes that can be detected and the accuracy can also be increased while detecting far away or fast-moving objects due to lighting issues and computational complexity.

Bounding overwatch

10.1109/iccmc48092.2020.iccmc-00061

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Citations (9)

Multi-agent Collective Construction using 3D Decomposition

arXiv (Cornell University) (2023)

Akshaya Kesarimangalam Srinivasan Shambhavi Singh Geordan Gutow Howie Choset Bhaskar Vundurthy

This paper addresses a Multi-Agent Collective Construction (MACC) problem that aims to build a three-dimensional structure comprised of cubic blocks. We use cube-shaped robots that can carry one cubic block at a time, and move forward, reverse, left, and right to an adjacent cell of the same height or climb up and down one cube height. To construct structures taller than one cube, the robots must build supporting stairs made of blocks and remove the stairs once the structure is built. Conventional techniques solve for the entire structure at once and quickly become intractable for larger workspaces and complex structures, especially in a multi-agent setting. To this end, we present a decomposition algorithm that computes valid substructures based on intrinsic structural dependencies. We use Mixed Integer Linear Programming (MILP) to solve for each of these substructures and then aggregate the solutions to construct the entire structure. Extensive testing on 200 randomly generated structures shows an order of magnitude improvement in the solution computation time compared to an MILP approach without decomposition. Additionally, compared to Reinforcement Learning (RL) based and heuristics-based approaches drawn from the literature, our solution indicates orders of magnitude improvement in the number of pick-up and drop-off actions required to construct a structure. Furthermore, we leverage the independence between substructures to detect which sub-structures can be built in parallel. With this parallelization technique, we illustrate a further improvement in the number of time steps required to complete building the structure. This work is a step towards applying multi-agent collective construction for real-world structures by significantly reducing solution computation time with a bounded increase in the number of time steps required to build the structure.

Cube (algebra)

Leverage (statistics)

Heuristics

10.48550/arxiv.2309.00985

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Hierarchical Planning for Long-Horizon Multi-Agent Collective Construction

Shambhavi Singh Z. Huang Akshaya Kesarimangalam Srinivasan Geordan Gutow Bhaskar Vundurthy

10.1109/icra57147.2024.10611496

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Adaptive learning management expert system with evolving knowledge base and enhanced learnability

Education and Information Technologies (2021)

Shwetha Sridharan Deepti Saravanan Akshaya Kesarimangalam Srinivasan B. S. Murugan

Learnability

Learning Management

Relevance

Adaptive Learning

10.1007/s10639-021-10560-w

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Citations (16)

A Pareto-Optimal Local Optimization Framework for Multiobjective Ergodic Search

IEEE Transactions on Robotics (2023)

Zhongqiang Ren Akshaya Kesarimangalam Srinivasan Bhaskar Vundurthy Ian Abraham Howie Choset

Our work is motivated by humanitarian assistant and disaster relief (HADR) where often it is critical to find signs of life in the presence of conflicting criteria, objectives, and information. We believe ergodic search can provide a framework for exploiting available information as well as exploring for new information in applications, such as HADR. Existing ergodic search methods typically consider search using only a single information map. However, one can readily envision many scenarios where multiple information maps that encode different types of relevant information are used. Ergodic search methods currently do not possess the ability to simultaneously search multiple information maps, nor do they have a way to balance which information gets priority. This leads us to formulate a multiobjective ergodic search (MO-ES) problem, which aims to find the so-called Pareto-optimal solutions, for the purpose of providing human decision makers various solutions that trade off among conflicting criteria. To efficiently solve MO-ES, we develop a framework called sequential local ergodic search (SL-ES), which leverages the recent advances in ergodic search methods as well as the idea of local optimization to efficiently compute Pareto-optimal solutions. Our numerical results show that SL-ES computes solutions of better quality and runs faster than the baselines.

Pareto optimal

10.1109/tro.2023.3284358

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Citations (3)

A Local Optimization Framework for Multi-Objective Ergodic Search

Zhongqiang Ren Akshaya Kesarimangalam Srinivasan Howard Coffin Ian Abraham Howie Choset

Robots have the potential to perform search for a variety of applications under different scenarios.Our work is motivated by humanitarian assistant and disaster relief (HADR) where often it is critical to find signs of life in the presence of conflicting criteria, objectives, and information.We believe ergodic search can provide a framework for exploiting available information as well as exploring for new information for applications such as HADR, especially when time is of the essence.Ergodic search algorithms plan trajectories such that the time spent in a region is proportional to the amount of information in that region, and is able to naturally balance exploitation (myopically searching high-information areas) and exploration (visiting all locations in the search space for new information).Existing ergodic search algorithms, as well as other information-based approaches, typically consider search using only a single information map.However, in many scenarios, the use of multiple information maps that encode different types of relevant information is common.Ergodic search methods currently do not possess the ability for simultaneous nor do they have a way to balance which information gets priority.This leads us to formulate a Multi-Objective Ergodic Search (MOES) problem, which aims at finding the so-called Pareto-optimal solutions, for the purpose of providing human decision makers various solutions that trade off between conflicting criteria.To efficiently solve MOES, we develop a framework called Sequential Local Ergodic Search (SLES) that converts a MOES problem into a "weight space coverage" problem.It leverages the recent advances in ergodic search methods as well as the idea of local optimization to efficiently approximate the Pareto-optimal front.Our numerical results show that SLES computes solutions of better quality than the popular multi-objective genetic algorithms and runs distinctly faster than a naive scalarization method on a commercial laptop.

10.15607/rss.2022.xviii.052

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Citations (5)

Multi-Agent Collective Construction Using 3D Decomposition

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023)

Akshaya Kesarimangalam Srinivasan Shambhavi Singh Geordan Gutow Howie Choset Bhaskar Vundurthy

Consider a Multi-Agent Collective Construction (MACC) problem that aims to generate a plan for fictitious cubic robots to build a three-dimensional structure comprised of cubic blocks. These cubic robots can carry one cubic block at a time; robots may move left, right, forwards, backward, or climb up or down one block. To construct structures taller than one cube, the robots must build supporting scaffolding made of blocks and remove the scaffolding once the structure is built. Prior works sought to create a planner that considered the structure as one monolithic assembly, which becomes intractable for larger workspaces and complex structures. To this end, we present a decomposition algorithm that breaks the structure into substructures that can be planned for independently. We use Mixed Integer Linear Programming (MILP) to plan for each of these substructures and then aggregate the solutions to construct the entire structure. Extensive testing on 200 randomly generated structures shows an order of magnitude improvement in the solution computation time compared to an MILP approach without decomposition. Finally, we leverage the independence between substructures to detect which substructures can be built in parallel.

Leverage (statistics)

Cube (algebra)

10.1109/iros55552.2023.10341964

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Citations (1)