Autonomous robot navigation in complex environments requires robust perception as well as high-level scene understanding due to perceptual challenges, such as occlusions, and uncertainty introduced by robot movement. For example, a robot climbing a cluttered staircase can misinterpret clutter as a step, misrepresenting the state and compromising safety. This requires robust state estimation methods capable of inferring the underlying structure of the environment even from incomplete sensor data. In this paper, we introduce a novel method for robust state estimation of staircases. To address the challenge of perceiving occluded staircases extending beyond the robot's field-of-view, our approach combines an infinite-width staircase representation with a finite endpoint state to capture the overall staircase structure. This representation is integrated into a Bayesian inference framework to fuse noisy measurements enabling accurate estimation of staircase location even with partial observations and occlusions. Additionally, we present a segmentation algorithm that works in conjunction with the staircase estimation pipeline to accurately identify clutter-free regions on a staircase. Our method is extensively evaluated on real robot across diverse staircases, demonstrating significant improvements in estimation accuracy and segmentation performance compared to baseline approaches.
@article{sriganesh2025bayesian,
title={A Bayesian Modeling Framework for Estimation and Ground Segmentation of Cluttered Staircases},
author={Sriganesh, Prasanna and Shirose, Burhanuddin and Travers, Matthew},
journal={IEEE Robotics and Automation Letters},
year={2025},
volume={10},
number={5},
pages={4164-4171},
publisher={IEEE}
}
CAP: A Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments using Coverage Guidance Graph
Zongyuan Shen, Burhanuddin Shirose, Prasanna Sriganesh and Matthew Travers
Efficient coverage of unknown environments requires robots to adapt their paths in real time based on on-board sensor data. In this paper, we introduce CAP, a connectivity-aware hierarchical coverage path planning algorithm for efficient coverage of unknown environments. During online operation, CAP incrementally constructs a coverage guidance graph to capture essential information about the environment. Based on the updated graph, the hierarchical planner determines an efficient path to maximize global coverage efficiency and minimize local coverage time. The performance of CAP is evaluated and compared with five baseline algorithms through high-fidelity simulations as well as robot experiments. Our results show that CAP yields significant improvements in coverage time, path length, and path overlap ratio
@article{shen2025cap,
title={CAP: A Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments using Coverage Guidance Graph},
author={Shen, Zongyuan and Shirose, Burhanuddin and Sriganesh, Prasanna and Travers, Matthew},
journal={arXiv preprint arXiv:2503.00647},
year={2025}
}
2024
Modular, Resilient, and Scalable System Design Approaches - Lessons learned in the years after DARPA Subterranean Challenge
Prasanna Sriganesh, James Maier, Adam Johnson, Burhanuddin Shirose, Rohan Chandrasekar, Charles Noren, Joshua Spisak, Ryan Darnley, Bhaskar Vundurthy and Matthew Travers
Field robotics applications, such as search and rescue, involve robots operating in large, unknown areas. These environments present unique challenges that compound the difficulties faced by a robot operator. The use of multi-robot teams, assisted by carefully designed autonomy, help reduce operator workload and allow the operator to effectively coordinate robot capabilities. In this work, we present a system architecture designed to optimize both robot autonomy and the operator experience in multi-robot scenarios. Drawing on lessons learned from our team's participation in the DARPA SubT Challenge, our architecture emphasizes modularity and interoperability. We empower the operator by allowing for adjustable levels of autonomy ("sliding mode autonomy"). We enhance the operator experience by using intuitive, adaptive interfaces that suggest context-aware actions to simplify control. Finally, we describe how the proposed architecture enables streamlined development of new capabilities for effective deployment of robot autonomy in the field.
@inproceedings{sriganesh2024systemdesign,
title={Modular, Resilient, and Scalable System Design Approaches - Lessons learned in the years after {DARPA} Subterranean Challenge},
author={Prasanna Sriganesh and James Maier and Adam Johnson and Burhanuddin Shirose and Rohan Chandrasekar and Charles Noren and Joshua Spisak and Ryan Darnley and Bhaskar Vundurthy and Matthew Travers},
booktitle="IEEE ICRA Workshop on Field Robotics",
year={2024}
}
Longitudinal Control Volumes: A Novel Centralized Estimation and Control Framework for Distributed Multi-Agent Sorting Systems
James Maier, Prasanna Sriganesh and Matthew Travers
2024 IEEE International Conference on Robotics and Automation (ICRA)
Centralized control of a multi-agent system improves upon distributed control especially when multiple agents share a common task e.g., sorting different materials in a recycling facility. Traditionally, each agent in a sorting facility is tuned individually which leads to suboptimal performance if one agent is less efficient than the others. Centralized control overcomes this bottleneck by leveraging global system state information, but it can be computationally expensive. In this work, we propose a novel framework called Longitudinal Control Volumes (LCV) to model the flow of material in a recycling facility. We then employ a Kalman Filter that incorporates local measurements of materials into a global estimation of the material flow in the system. We utilize a model predictive control algorithm that optimizes the rate of material flow using the global state estimate in real-time. We show that our proposed framework outperforms distributed control methods by 40-100% in simulation and physical experiments.
@inproceedings{maier2024longitudinal,
title={Longitudinal Control Volumes: A Novel Centralized Estimation and Control Framework for Distributed Multi-Agent Sorting System},
author={Maier, James and Sriganesh, Prasanna and Travers, Matthew},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
pages={4420--44279},
year={2024},
organization={IEEE},
}
2023
Fast Staircase Detection and Estimation using 3D Point Clouds with Multi-detection Merging for Heterogeneous Robots
Prasanna Sriganesh, Namya Bagree, Bhaskar Vundurthy and Matthew Travers
2023 IEEE International Conference on Robotics and Automation (ICRA)
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.
@inproceedings{sriganesh2023fast,
title={Fast Staircase Detection and Estimation using 3D Point Clouds with Multi-detection Merging for Heterogeneous Robots},
author={Sriganesh, Prasanna and Bagree, Namya and Vundurthy, Bhaskar and Travers, Matthew},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
pages={9253--9259},
year={2023},
organization={IEEE},
}
2021
Generating Curved Path Walking Gaits for Biped Robots with Deficient Degrees of Freedom
Prasanna Sriganesh and Prajwal Rajendra Mahendrakar
2021 IEEE/SICE International Symposium on System Integration (SII)
This paper presents a method for generating curved path sequences in a humanoid robot with a deficient degree of freedom (DOF).Typically robots not having the yaw DOF in their limbs cannot turn in a curved path. This paper aims at achieving that by combining the robot’s friction based slip turn mechanism with the simple straight walking gait of the 3-D linear inverted pendulum model into a smooth curved motion. The amount of turn caused by the slip model is estimated and multiple experiments are conducted to verify the prediction. We noticed that the co-efficient of friction does not affect the amount of turn for symmetric gaits. A novel mathematical model which uses the radius of the curved path to plan the gait for generating a smooth circular trajectory, has been developed. A 17-DOF robot named TONY is used to test the hypothesis. The results are successful and the robot is able to walk in a curved path.
@inproceedings{sriganesh2021generating,
title={Generating curved path walking gaits for biped robots with deficient degrees of freedom},
author={Sriganesh, Prasanna and Mahendrakar, Prajwal Rajendra},
booktitle={2021 IEEE/SICE International Symposium on System Integration (SII)},
pages={786--793},
year={2021},
organization={IEEE}
}
2020
Solving Inverse Kinematics using Geometric Analysis for Gait Generation in Small-Sized Humanoid Robots
Prasanna Sriganesh, Prajwal Rajendra Mahendrakar and Rajasekar Mohan
2020 IEEE/SICE International Symposium on System Integration (SII)
Humanoid Robots can play a major role in domestic automation. It is a platform which does not require a specific work environment and can be made adaptable. The goal of this paper is to generate stable gaits for humanoid robots using geometric methods for kinematic analysis. A 3D linear inverted pendulum model is used to find the trajectory of the center of mass. This trajectory in turn coupled with foot positions are sufficient to find a solution to the D-H matrix. A small-sized humanoid robot with 17-DOF is built and the generated gaits are tested on the robot. The robot was successfully able to walk forward, turn left and right in a stable manner.
@inproceedings{sriganesh2020solving,
title={Solving inverse kinematics using geometric analysis for gait generation in small-sized humanoid robots},
author={Sriganesh, Prasanna and Mahendrakar, Prajwal Rajendra and Mohan, Rajasekar},
booktitle={2020 IEEE/SICE International Symposium on System Integration (SII)},
pages={384--389},
year={2020},
organization={IEEE}
}
