arXiv subjects

Leena Vachhani

Publications and source records attributed to Leena Vachhani.

16 recordsLinked to original sources

arXiv2026

Instantaneous Planning, Control and Safety for Navigation in Unknown Underwater Spaces

Navigating autonomous underwater vehicles (AUVs) in unknown environments is significantly challenging due to poor visibility, weak signal transmission, and dynamic water currents. These factors pose challenges in accurate global localization, reliable communication, and obstacle avoidance. Local sensing provides critical real time environmental data to enable online decision making. However, the inherent noise in underwater sensor measurements introduces uncertainty, complicating planning and control. To address these challenges, we propose an integrated planning and control framework that leverages real time sensor data to dynamically induce closed loop AUV trajectories, ensuring robust obstacle avoidance and enhanced maneuverability in tight spaces. By planning motion based on pre designed feedback controllers, the approach reduces the computational complexity needed for carrying out online optimizations and enhances operational safety in complex underwater spaces. The proposed method is validated through ROS Gazebo simulations on the RexRov AUV, demonstrating its efficacy. Its performance is evaluated by comparison against PID based tracking methods, and quantifying localization errors in dead reckoning as the AUV transitions into the target communication range.

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arXiv2026

A Survey on Sensor-based Planning and Control for Unmanned Underwater Vehicles

This survey examines recent sensor-based planning and control methods for Unmanned Underwater Vehicles (UUVs). In complex, uncertain underwater environments, UUVs require advanced planning and control strategies for effective navigation. These vehicles face significant challenges including drifting and noisy sensor measurements, absence of Global Navigation Satellite System (GNSS) signals, and low-bandwidth, high-latency underwater acoustic communications. The focus is on reactive local planning layers that adapt to real-time sensor inputs such as SONAR and Inertial Measurement Units (IMU) to improve localization accuracy and autonomy in dynamic ocean conditions, enabling dynamic obstacle avoidance and on-the-fly re-planning. The survey categorizes the existing literature into decoupled and coupled architectures for sensor-based planning and control. The decoupled architecture sequentially addresses planning and control stages, whereas coupled architectures offer tighter feedback loops for more immediate responsiveness. A comparative analysis of coupled planning and control methods reveals that while PID controllers are simple, they lack predictive capability for complex maneuvers. Model Predictive Control (MPC) offers superior path optimization but can be computationally intensive, and invariant-set controllers provide strong safety guarantees at the potential cost of agility in confined environments. Key contributions include a taxonomy of architectures combining planning and control, a focus on adaptive local planning, and an analysis of controller roles in integrated planning frameworks for autonomous navigation of UUVs.

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arXiv2025

$\epsilon$-Optimal Multi-Agent Patrol using Recurrent Strategy

The multi-agent patrol problem refers to repeatedly visiting different locations in an environment using multiple autonomous agents. For over two decades, researchers have studied this problem in various settings. While providing valuable insights into the problem, the works in existing literature have not commented on the nature of the optimal solutions to the problem. We first show that an $\epsilon$-approximate recurrent patrol strategy exists for every feasible patrol strategy. Then, we establish the existence of a recurrent patrol strategy that is an $\epsilon$-optimal solution to the General Patrol Problem. The factor $\epsilon$ is proportional to the discretisation constant $D$, which can be arbitrarily small and is independent of the number of patrol agents and the size of the environment. This result holds for a variety of problem formulations already studied. We also provide an algorithmic approach to determine an $\epsilon$-approximate recurrent patrol strategy for a patrol strategy created by any method from the literature. We perform extensive simulations in graphs based on real-life environments to validate the claims made in this work.

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arXiv2024

Polyhedral study of a temporal rural postman problem: application in inspection of railway track without disturbing train schedules

The Rural Postman Problem with Temporal Unavailability (RPP-TU) is a variant of the Rural Postman Problem (RPP) specified for multi-agent planning over directed graphs with temporal constraints. These temporal constraints represent the unavailable time intervals for each arc during which agents cannot traverse the arc. Such arc unavailability scenarios occur in routing and scheduling of the instrumented wagons for inspection of railway tracks without disturbing the train schedules, i.e. the scheduled trains prohibit access to the signal blocks (sections of railway track separated by signals) for some finite interval of time. A three-index formulation for the RPP-TU is adopted from the literature. The three-index formulation has binary variables for describing the route information of the agents, and continuous non-negative variables to describe the schedules at pre-defined locations. A relaxation of the three-index formulation for RPP-TRU, referred to as Cascaded Graph Formulation (CGF), is investigated in this work. The CGF has attributes that simplify the polyhedral study of time-dependent arc routing problems like RPP-TRU. A novel branch-and-cut algorithm is proposed to solve the RPP-TU, where branching is performed over the service arcs. A family of facet-defining inequalities, derived from the polyhedral study, is used as cutting planes in the proposed branch-and-cut algorithm to reduce the computation time by up to $48\%$. Finally, an application of this work is showcased using a simulation case study of a railway inspection scheduling problem based on Kurla-Vashi-Thane suburban network in Mumbai, India. An improvement of $93\%$ is observed when compared to a Benders' decomposition based MILP solver from the literature.

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arXiv2024

Self-navigation in crowds: An invariant set-based approach

Self-navigation in non-coordinating crowded environments is formidably challenging within multi-agent systems consisting of non-holonomic robots operating through local sensing. Our primary objective is the development of a novel, rapid, sensor-driven, self-navigation controller that directly computes control commands to enable safe maneuvering while coexisting with other agents. We propose an input-constrained feedback controller meticulously crafted for non-holonomic mobile robots and the characterization of associated invariant sets. The invariant sets are the key to maintaining stability and safety amidst the non-cooperating agents. We then propose a planning strategy that strategically guides the generation of invariant sets toward the agent's intended target. This enables the agents to directly compute theoretically safe control inputs without explicitly requiring pre-planned paths/trajectories to reliably navigate through crowded multi-agent environments. The practicality of our technique is demonstrated through hardware experiments, and the ability to parallelize computations to shorten computational durations for synthesizing safe control commands. The proposed approach finds potential applications in crowded multi-agent scenarios that require rapid control computations based on perceived safety bounds during run-time.

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arXiv2024

Plug-in for visualizing 3D tool tracking from videos of Minimally Invasive Surgeries

This paper tackles instrument tracking and 3D visualization challenges in minimally invasive surgery (MIS), crucial for computer-assisted interventions. Conventional and robot-assisted MIS encounter issues with limited 2D camera projections and minimal hardware integration. The objective is to track and visualize the entire surgical instrument, including shaft and metallic clasper, enabling safe navigation within the surgical environment. The proposed method involves 2D tracking based on segmentation maps, facilitating creation of labeled dataset without extensive ground-truth knowledge. Geometric changes in 2D intervals express motion, and kinematics based algorithms process results into 3D tracking information. Synthesized and experimental results in 2D and 3D motion estimates demonstrate negligible errors, validating the method for labeling and motion tracking of instruments in MIS videos. The conclusion underscores the proposed 2D segmentation technique's simplicity and computational efficiency, emphasizing its potential as direct plug-in for 3D visualization in instrument tracking and MIS practices.

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arXiv2023

Balancing Priorities in Patrolling with Rabbit Walks

In an environment with certain locations of higher priority, it is required to patrol these locations as frequently as possible due to their importance. However, the Non-Priority locations are often neglected during the task. It is necessary to balance the patrols on both kinds of sites to avoid breaches in security. We present a distributed online algorithm that assigns the routes to agents that ensures a finite time visit to the Non-Priority locations along with Priority Patrolling. The proposed algorithm generates offline patrol routes (Rabbit Walks) with three segments (Hops) to explore non-priority locations. The generated number of offline walks depends exponentially on a parameter introduced in the proposed algorithm, thereby facilitating the scalable implementation based on the onboard resources available on each patrolling robot. A systematic performance evaluation through simulations and experimental results validates the proportionately balanced visits and suggests the proposed algorithm's versatile applicability in the implementation of deterministic and non-deterministic scenarios.

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arXiv2023

Swarm Self Clustering for Communication denied Environments without Global Positioning

In this work, we investigate swarm self-clustering, where robots autonomously organize into spatially coherent groups using only local sensing and decision-making, without external commands, global positioning, or inter-robot communication. Each robot forms and maintains clusters by responding to relative distances from nearby neighbors detected through onboard range sensors with limited fields of view. The method is suited for GPS-denied and communication-constrained environments and requires no prior knowledge of cluster size, number, or membership. A mechanism enables robots to alternate between consensus-based and random goal assignment based on local neighborhood size, ensuring robustness, scalability, and untraceable clustering independent of initial conditions. Extensive simulations and real-robot experiments demonstrate empirical convergence, adaptability to dynamic additions, and improved performance over local-only baselines across standard cluster quality metrics.

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arXiv2023

Wobble control of a pendulum actuated spherical robot

Spherical robots can conduct surveillance in hostile, cluttered environments without being damaged, as their protective shell can safely house sensors such as cameras. However, lateral oscillations, also known as wobble, occur when these sphere-shaped robots operate at low speeds, leading to shaky camera feedback. These oscillations in a pendulum-actuated spherical robot are caused by the coupling between the forward and steering motions due to nonholonomic constraints. Designing a controller to limit wobbling in these robots is challenging due to their underactuated nature. We propose a model-based controller to navigate a pendulum-actuated spherical robot using wobble-free turning maneuvers consisting of circular arcs and straight lines. The model is developed using Lagrange-D'Alembert equations and accounts for the coupled forward and steering motions. The model is further analyzed to derive expressions for radius of curvature, precession rate, wobble amplitude, and wobble frequency during circular motions. Finally, we design an input-output feedback linearization-based controller to control the robot's heading direction and wobble. Overall, the proposed controller enables a teleoperator to command a specific forward velocity and pendulum angle as per the desired turning radius while limiting the robot's lateral oscillations to enhance the quality of camera feedback.

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arXiv2023

Pendulum Actuated Spherical Robot: Dynamic Modeling & Analysis for Wobble & Precession

A spherical robot has many practical advantages as the entire electronics are protected within a hull and can be carried easily by any Unmanned Aerial Vehicle (UAV). However, its use is limited due to finding mounts for sensors. Pendulum actuated spherical robot provides space for mounting sensors at the yoke. We study the non-linear dynamics of a pendulum-actuated spherical robot to analyze the dynamics of internal assembly (yoke) for mounting sensors. For such robots, we provide a coupled dynamic model that takes care of the relationship between forward and sideways motion. We further demonstrate the effects of wobbling and precession captured by our model when the bot is controlled to execute a turning maneuver while moving with a moderate forward velocity, a practical situation encountered by spherical robots moving in an indoor setting. A simulation setup based on the developed model provides visualization of the spherical robot motion.

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arXiv2021

Temporal Recurring Unavailabilities in Multi-agent Rural Postman Problem: Navigating railway tracks during availability time intervals

Time-dependent (or temporal) properties may arise in many network-based planning problems, particularly in the routing and scheduling of railway track inspection problems. The availability of tracks depends on the train schedules, maintenance possessions, etc. In the absence of side constraints, this routing and scheduling problem is formulated as a multi-agent rural postman problem on a temporal-directed network; where a given set of rail track sections must be visited while respecting the temporal attributes due to railway track unavailabilities. In this work, we adopt a three-index formulation for the multi-agent Rural Postman Problem with Temporal Recurring Unavailabilities (RPP-TRU) and frame it as a Mixed Integer Linear Programming (MILP) problem. In addition, we propose relevant theoretical studies for RPP-TRU to ensure the feasibility of the proposed optimization problem. Two approaches of an exact algorithm are proposed, based on Benders' decomposition framework, to address the disjunctive unavailability constraints occurring in its scheduling sub-problems, alongside the NP-Hard routing (master) problem. A polynomial-time algorithm is designed to address the scheduling sub-problem, while the NP-Hard master problem is solved using MILP toolbox. Comparison results with RPP (without temporal constraints) show a minor compromise with the spatial cost solution with significantly less delay, hence suitable for real-world routing and scheduling applications occurring in a shared network like railways. A simulation study on a part of the Mumbai suburban railway network demonstrates the working of the proposed methodology under a realistic setting.

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arXiv2019

3D Sensing of a Moving Object with a Nodding 2D LIDAR and Reconfigurable Mirrors

Perception in 3D has become standard practice for a large part of robotics applications. High quality 3D perception is costly. Our previous work on a nodding 2D Lidar provides high quality 3D depth information with low cost, but the sparse data generated by this sensor poses challenges in understanding the characteristics of moving objects within an uncertain environment. This paper proposes a novel design of the nodding Lidar but provides dynamic reconfigurability in terms of limiting the field of view of the sensor using a set of optical mirrors. It not only provides denser scans, but it also achieves a three times higher scan update rate. Additionally, we propose a novel calibration mechanism for this sensor and prove its effectiveness for dynamic object detection and tracking.

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arXiv2019

Anytime Planning: A Motion Planner for Dynamic Environment

Motion planning in the presence of multiple dynamic obstacles is an important research problem from the perspective of autonomous vehicles as well as space-constrained multi-robot work environment. In this paper, we address the motion planning problem for multiple dynamic obstacle rich environment and propose a probabilistically, complete novel motion planning algorithm. Our claim is that given a fixed path cost i.e. the Euclidean path length, the proposed algorithm plans a path with the least computational time as compared to the state-of-the-art techniques. At the same time, given the time duration for planning, it plans the minimum cost path. Dynamic constraints have been taken into consideration while designing the planner such that the optimal planned path is feasible for implementation. The results of extensive simulation experiments show that the proposed sequential BIT* outperforms the DOVS both in the planned path length as well as the path generation time.

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arXiv2019

State Estimation for Vision-based Localization under Uncertain Conditions

Vision based localization is a popular approach to carry out manoeuvres particularly in GPS-restricted indoor environments, because vision can complement other activities performed by the robot. The objective is to estimate the current location with respect to a known location by matching the bearings. The problem is challenging as the known location information is in terms of the bearings of landmarks extracted from an image. We address the problem under more challenging scenario when landmarks are semi-static. In this work, an observer formulation is presented which enables to incorporate the effect of change in landmark position as parameters. The efficacy of two estimators: Augmented Extended Kalman Filter (A-EKF) and a Proportional-Integral EKF (PI-EKF) is tested under the cases where there are changes in some of the landmark positions. Morever, it is likely that not all landmarks are visible to the robot at all instants of time. A multi-rate estimation framework is proposed to mitigate this issue. Observability analysis is carried out to arrive upon a minimum number of landmarks required for such a formulation. Simulation studies are presented to test the efficacy of the formulations.

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arXiv2018

Reconfigurable formations of quadrotors on Lissajous curves for surveillance applications

This paper proposes trajectory planning strategies for online reconfiguration of a multi-agent formation on a Lissajous curve. In our earlier work, a multi-agent formation with constant parametric speed was proposed in order to address multiple objectives such as repeated collision-free surveillance and guaranteed sensor coverage of the area with ability for rogue target detection and trapping. This work addresses the issue of formation reconfiguration within this context. In particular, smooth parametric trajectories are designed for the purpose using calculus of variations. These trajectories have been employed in conjunction with a simple local cooperation scheme so as to achieve collision-free reconfiguration between different Lissajous curves. A detailed theoretical analysis of the proposed scheme is provided. These surveillance and reconfiguration strategies have also been validated through simulations in MATLAB\reg for agents performing parametric motion along the curves, and by Software-In-The-Loop simulation for quadrotors. In addition, they are validated experimentally with a team of quadrotors flying in a motion capture environment.

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arXiv2017

Hilbert's Space-filling Curve for Regions with Holes

The paper presents a systematic strategy for implementing Hilbert's space filling curve for use in online exploration tasks and addresses its application in scenarios wherein the space to be searched obstacles (or holes) whose locations are not known a priori. Using the self-similarity and locality preserving properties of Hilbert's space filling curve, a set of evasive maneuvers are prescribed and characterized for online implementation. Application of these maneuvers in the case of non-uniform coverage of spaces and for obstacles of varying sizes is also presented. The results are validated with representative simulations demonstrating the deployment of the approach.

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