Author name cluster

Bhaskar Vundurthy

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

ICRA Conference 2025 Conference Paper

A Complete and Bounded-Suboptimal Algorithm for a Moving Target Traveling Salesman Problem with Obstacles in 3D

Anoop Bhat
Geordan Gutow
Bhaskar Vundurthy
Zhongqiang Ren
Sivakumar Rathinam
Howie Choset

The moving target traveling salesman problem with obstacles (MT-TSP-O) seeks an obstacle-free trajectory for an agent that intercepts a given set of moving targets, each within specified time windows, and returns to the agent's starting position. Each target moves with a constant velocity within its time windows, and the agent has a speed limit no smaller than any target's speed. We present FMC*-TSP, the first complete and bounded-suboptimal algorithm for the MT-TSP-O, and results for an agent whose configuration space is $\mathbb{R}^{3}$. Our algorithm interleaves a high-level search and a lowlevel search, where the high-level search solves a generalized traveling salesman problem with time windows (GTSP-TW) to find a sequence of targets and corresponding time windows for the agent to visit. Given such a sequence, the low-level search then finds an associated agent trajectory. To solve the low-level planning problem, we develop a new algorithm called FMC*, which finds a shortest path on a graph of convex sets (GCS) via implicit graph search and pruning techniques specialized for problems with moving targets. We test FMC*-TSP on 280 problem instances with up to 40 targets and demonstrate its smaller median runtime than a baseline based on prior work.

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IROS Conference 2024 Conference Paper

GESCE: Graph-based Ergodic Search in Cluttered Environments

Burhanuddin Shirose
Adam Johnson
Bhaskar Vundurthy
Howie Choset
Matthew Travers

In this paper, we present a novel motion planning algorithm that inherits the strengths of both optimization and search-based planners. Optimization-based planners use the gradient of an objective function to generate a desired path, whereas search-based planners operate on a graph capturing the salient topology of a robot’s free space. A class of optimization-based planners leverages prior information, modeled as a probability distribution of target locations in an environment, to guide path generation. We embrace one specific measure, referred to as ergodicity, which encourages a robot to spend a proportion of its time, weighted by the distribution, where it is likely to find targets of interest. Methods that minimize ergodicity were not designed to handle obstacles in the environment, and augmented approaches that add "soft" constraints for obstacles to the cost function may still yield a path that collides with an obstacle. In this work, we present a hybrid approach that first generates a graph of the environment’s free space, followed by searching the graph with ergodicity as a heuristic. Our approach not only restricts the search to the free space, thereby avoiding obstacles by design, but also generates trajectories with low ergodicity values. Extensive testing on 125 test scenarios with varying degrees of clutter, information distribution, and robot start locations illustrate the efficacy of our algorithm.

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ICRA Conference 2024 Conference Paper

Hierarchical Planning for Long-Horizon Multi-Agent Collective Construction

Shambhavi Singh
Zejian Huang
Akshaya Kesarimangalam Srinivasan
Geordan Gutow
Bhaskar Vundurthy
Howie Choset

We develop a planner that directs robots to construct a 3D target structure composed of blocks. The robots themselves are cubes of the same size as the blocks, and they may place, carry, or remove one block at a time. When moving, robots are also allowed to climb or descend a block. A construction plan may thus build a staircase-like scaffolding of blocks to reach other blocks at higher levels. The order of block placement is important; for example, a block that sits atop other blocks must be placed after the blocks below it, and a block that needs scaffolding cannot be placed until after the scaffolding is. Prior works focus on end-to-end approaches that simultaneously plan for block placement order and inter-robot collisions. Larger structures are either intractable or yield high-cost solutions. A prior approach mitigates this by decomposing the structure into smaller components that can be planned for independently, but the computational challenge remains. We present a hierarchical approach that first 1) uses A* to determine a sequence of block placements and removals while ignoring inter-robot collision, then 2) identifies ordering constraints between block placement and removal actions, and finally (3) computes collision-free paths for multiple robots to perform said actions. Compared to an optimization approach that minimizes the number of timesteps to complete the structure, we observe a 100x reduction in computation time for comparable solutions.

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ICLR Conference 2024 Conference Paper

Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem

Albert Xu
Jhih-Yi Hsieh
Bhaskar Vundurthy
Nithya Kemp
Eliana Cohen
Lu Li
Howie Choset

In deep metric learning, the triplet loss has emerged as a popular method to learn many computer vision and natural language processing tasks such as facial recognition, object detection, and visual-semantic embeddings. One issue that plagues the triplet loss is network collapse, an undesirable phenomenon where the network projects the embeddings of all data onto a single point. Researchers predominately solve this problem by using triplet mining strategies. While hard negative mining is the most effective of these strategies, existing formulations lack strong theoretical justification for their empirical success. In this paper, we utilize the mathematical theory of isometric approximation to show an equivalence between the triplet loss sampled by hard negative mining and an optimization problem that minimizes a Hausdorff-like distance between the neural network and its ideal counterpart function. This provides the theoretical justifications for hard negative mining's empirical efficacy. Experiments performed on the Market-1501 and Stanford Online Products datasets with various network architectures corroborate our theoretical findings, indicating that network collapse tends to happen when batch size is too large or embedding dimension is too small. In addition, our novel application of the isometric approximation theorem provides the groundwork for future forms of hard negative mining that avoid network collapse.

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ICRA Conference 2023 Conference Paper

Fast Staircase Detection and Estimation using 3D Point Clouds with Multi-detection Merging for Heterogeneous Robots

Prasanna Sriganesh
Namya Bagree
Bhaskar Vundurthy
Matthew Travers

Robotic systems need advanced mobility capabili-ties to operate in complex, three-dimensional environments designed for human use, e. g. , multi-level buildings. Incorporating some level of autonomy enables robots to operate robustly, reliably, and efficiently in such complex environments, e. g. , automatically “returning home” if communication between an operator and robot is lost during deployment. This work presents a novel method that enables mobile robots to robustly operate in multi-level environments by making it possible to autonomously locate and climb a range of different staircases. We present results wherein a wheeled robot works together with a quadrupedal system to quickly detect different staircases and reliably climb them. The performance of this novel staircase detection algorithm that is able to run on the heterogeneous platforms is compared to the current state-of-the-art detection algorithm. We show that our approach significantly increases the accuracy and speed at which detections occur.

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IROS Conference 2023 Conference Paper

Multi-Agent Collective Construction Using 3D Decomposition

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.

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IROS Conference 2023 Conference Paper

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

Akshaya Kesarimangalam Srinivasan
Geordan Gutow
Zhongqiang Ren
Ian Abraham
Bhaskar Vundurthy
Howie Choset

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.

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