arXiv subjects

Satadal Ghosh

Publications and source records attributed to Satadal Ghosh.

13 recordsLinked to original sources

arXiv2026

Switched Turn-based Adaptive Source Seeking Strategy using Estimation and Information-driven Direction of Improvement

Source seeking arises in applications such as gas leak localization, radiation monitoring, and environmental surveillance, where the origin of an unknown signal field must be estimated from spatial measurements. In practice, the source location is not directly observable and must be inferred from noisy scalar measurements collected during motion.In robotic source seeking, estimation and motion are closely linked: measurements improve the source estimate, while the chosen trajectory affects the quality of future measurements.Existing loop-based geometric strategies generate feasible motion but do not explicitly use estimation uncertainty to regulate direction updates.This paper presents a loop-based source-seeking framework that combines Extended Kalman Filter (EKF) estimation with Fisher Information Matrix (FIM)-based direction selection. The source estimate is updated during motion, and the heading is changed at loop boundaries using both estimation uncertainty and predicted information gain. A measurement-based stopping condition is used to detect convergence without requiring prior knowledge of the source location.Simulation results under stationary and moving source scenarios demonstrate improved tracking performance and reduced estimation error compared to purely information-driven or estimate-driven strategies.

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arXiv2026

Surrogate Model-Based Near-Optimal Gain Selection for Approach-Angle-Constrained Two-Phase Pure Proportional Navigation

In guidance literature, Pure Proportional Navigation (PPN) guidance is widely used for aerodynamically driven vehicles. A two-phase extension of PPN (2pPPN), which uses different navigation gains for an orientation phase and a final phase, has been presented to achieve any desired approach angle within an angular half-space. Recent studies show that the orientation phase can be realized through multiple feasible trajectories, creating an opportunity to select navigation gains that minimize overall guidance effort. This paper addresses the problem of near-optimal gain selection for given initial and desired terminal engagement geometries. Two optimization problems are considered: i) determination of the optimal orientation-phase gain for a specified final-phase gain, and ii) simultaneously determining the optimal gain pair for both phases that minimizes the total guidance effort. Determining the optimal gains analytically for arbitrary engagement geometries is intractable. Numerical simulations further reveal that these optimal gains vary smoothly with respect to the engagement conditions. Exploiting this property, a neural network (NN)-based regression model is developed in this paper to learn the nonlinear mapping between optimal gains and initial and desired terminal engagement geometries. The trained NN serves as a computationally efficient surrogate for generating the optimal gains manifold, enabling near-optimal realization of 2pPPN guidance. Numerical simulation studies demonstrate that the developed NN-based architecture predicts optimal gains with high accuracy, achieving very high (close to 0.9) value of coefficient of determination.

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arXiv2025

A Corrector-aided Look-ahead Distance-based Guidance for Online Reference Path Following with an Efficient Mid-course Guidance Strategy

Efficient path-following is crucial in most of the applications of autonomous vehicles (UxV). Among various guidance strategies presented in literature, the look-ahead distance ($L_1$)-based nonlinear guidance has received significant attention due to its ease in implementation and ability to maintain a low cross-track error while following simpler reference paths and generating bounded lateral acceleration commands. However, the constant value of $L_1$ becomes problematic when the UxV is far away from the reference path and also produces higher cross-track error while following complex reference paths having high variation in radius of curvature. To address these challenges, the notion of look-ahead distance is leveraged in a novel way to develop a two-phase guidance strategy. Initially, when the UxV is far from the reference path, an optimized $L_1$ selection strategy is developed to guide the UxV towards the vicinity of the start point of the reference path, while maintaining minimal lateral acceleration command. Once the vehicle reaches a close neighborhood of the reference path, a novel notion of corrector point is incorporated in the constant $L_1$-based guidance scheme to generate the guidance command that effectively reduces the root mean square of the cross-track error and lateral acceleration requirement thereafter. Simulation results validate satisfactory performance of this proposed corrector point and look-ahead point pair-based guidance strategy, along with the developed mid-course guidance scheme. Also, its superiority over the conventional constant $L_1$ guidance scheme is established by simulation studies over different initial condition scenarios.

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arXiv2024

Switched Vector Field-based Guidance for General Reference Path Following in Planar Environment

Reference path following is a key component in the functioning of almost all engineered autonomous agents. Among several path following guidance methods in existing literature, vector-field-based guidance approach has got wide attention because of its simplicity and guarantee of stability under a broad class of scenarios. However, the usage of same cross-track-error-dependent structure of desired vector field in most of the existing literature irrespective of instantaneous cross-track error and course angle of unmanned vehicle makes it quite restrictive in attaining faster convergence and also leads to infeasibly high turn rate command for many scenarios. To this end, this paper presents a novel switched vector field-based guidance for following a general reference path, in which the structure of the desired vector field depends on instantaneous cross-track-error and vehicle's course angle. While the developed method ensures faster convergence, it also ensures that the guidance command always stays within a realistic threshold satisfying its curvature constraint, thus making it more real-life implementable for autonomous vehicles with kino-dynamic constraints. Theoretical analysis for convergence of the developed guidance scheme is presented. Possibilities of undesirable chattering at phase transitions are also eliminated. Numerical simulation studies are presented to validate the satisfactory performance of the developed algorithm.

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arXiv2024

Robust Fuel-Optimal Landing Guidance for Hazardous Terrain using Multiple Sliding Surfaces

In any spacecraft landing mission, fuel-efficient precision soft landing while avoiding nearby hazardous terrain is of utmost importance. Very few existing literature have attempted addressing both the problems of precision soft landing and terrain avoidance simultaneously. To this end, an optimal terrain avoidance landing guidance (OTALG) was recently developed, which showed promising performance in avoiding the terrain while consuming near-minimum fuel. However, its performance significantly degrades in the face of external disturbances, indicating lack of robustness. To mitigate this problem, in this paper, a near fuel-optimal guidance law is developed to avoid terrain and achieve precision soft landing at the desired landing site. Expanding the OTALG formulation using sliding mode control with multiple sliding surfaces (MSS), the presented guidance law, named `MSS-OTALG', improves precision soft landing accuracy. Further, the sliding parameter is designed to allow the lander to avoid terrain by leaving the trajectory enforced by the sliding mode and eventually returning to it when the terrain avoidance phase is completed. And finally, the robustness of the MSS-OTALG is established by proving practical fixed-time stability. Extensive numerical simulations are also presented to showcase its performance in terms of terrain avoidance, low fuel consumption, and accuracy of precision soft landing under bounded atmospheric perturbations, thrust deviations, and constraints. Comparative studies against existing relevant literature validate a balanced trade-off of all these performance measures achieved by the developed MSS-OTALG.

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arXiv2023

Fuel-Optimal Powered Descent Guidance for Hazardous Terrain

Future interplanetary missions will carry more and more sensitive equipment critical for setting up bases for crewed missions. The ability to manoeuvre around hazardous terrain thus becomes a critical mission aspect. However, large diverts and manoeuvres consume a significant amount of fuel, leading to less fuel remaining for emergencies or return missions. Thus, requiring more fuel to be carried onboard. This work presents fuel-optimal guidance to avoid hazardous terrain and safely land at the desired location. We approximate the hazardous terrain as step-shaped polygons and define barriers around the terrain. Using an augmented cost functional, fuel-optimal guidance command, which avoids the terrain, is derived. The results are validated using computer simulations and tested against many initial conditions to prove their effectiveness.

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arXiv2021

3D-OGSE: Online Safe and Smooth Trajectory Generation using Generalized Shape Expansion in Unknown 3-D Environments

In this paper, we present an online motion planning algorithm (3D-OGSE) for generating smooth, collision-free trajectories over multiple planning iterations for 3-D agents operating in an unknown obstacle-cluttered 3-D environment. Our approach constructs a safe-region, termed 'generalized shape', at each planning iteration, which represents the obstacle-free region based on locally-sensed environment information. A collision-free path is computed by sampling points in the generalized shape and is used to generate a smooth, time-parametrized trajectory by minimizing snap. The generated trajectories are constrained to lie within the generalized shape, which ensures the agent maneuvers in the locally obstacle-free space. As the agent reaches boundary of 'sensing shape' in a planning iteration, a re-plan is triggered by receding horizon planning mechanism that also enables initialization of the next planning iteration. Theoretical guarantee of probabilistic completeness over the entire environment and of completely collision-free trajectory generation is provided. We evaluate the proposed method in simulation on complex 3-D environments with varied obstacle-densities. We observe that each re-planing computation takes $\sim$1.4 milliseconds on a single thread of an Intel Core i5-8500 3.0 GHz CPU. In addition, our method is found to perform 4-10 times faster than several existing algorithms. In simulation over complex scenarios such as narrow passages also we observe less conservative behavior.

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arXiv2021

Mathematical Properties of Generalized Shape Expansion-Based Motion Planning Algorithms

Motion planning is an essential aspect of autonomous systems and robotics and is an active area of research. A recently-proposed sampling-based motion planning algorithm, termed 'Generalized Shape Expansion' (GSE), has been shown to possess significant improvement in computational time over several existing well-established algorithms. The GSE has also been shown to be probabilistically complete. However, asymptotic optimality of the GSE is yet to be studied. To this end, in this paper we show that the GSE algorithm is not asymptotically optimal by studying its behaviour for the promenade problem. In order to obtain a probabilistically complete and asymptotically optimal generalized shape-based algorithm, a modified version of the GSE, namely 'GSE*' algorithm, is subsequently presented. The forementioned desired mathematical properties of the GSE* algorithm are justified by its detailed analysis. Numerical simulations are found to be in line with the theoretical results on the GSE* algorithm.

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arXiv2020

Terminal-Angle-Constrained Guidance based on Sliding Mode Control for UAV Soft Landing on Ground Vehicles

In this paper the problem of guidance formulation for autonomous soft landing of unmanned aerial vehicles on stationary, moving, or accelerating / maneuvering ground vehicles at desired approach angles in both azimuth and elevation is considered. Nonlinear engagement kinematics have been used. While integrated nonlinear controllers have been developed in the literature for this purpose, in practical implementations the controller inputs often need modification of the existing autopilot structure, which is challenging. In order to avoid that a higher-level guidance algorithm is designed in this paper leveraging sliding mode control-based approach. In the presented guidance formulation, target-state-dependent singularity can be avoided in the guidance command. The effectiveness of the presented guidance law is verified with numerical simulation studies. However, since the algorithm in its basic form is found to demand high guidance command at large distances from maneuvering ground targets, a two-phase guidance is presented next to avoid this problem and validated with numerical simulations. Finally, the efficacy of the modified guidance algorithm is validated by Software-In-The-Loop simulations for a realistic testbed.

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arXiv2020

Variable Gain Gradient Descent-based Reinforcement Learning for Robust Optimal Tracking Control of Uncertain Nonlinear System with Input-Constraints

In recent times, a variety of Reinforcement Learning (RL) algorithms have been proposed for optimal tracking problem of continuous time nonlinear systems with input constraints. Most of these algorithms are based on the notion of uniform ultimate boundedness (UUB) stability, in which normally higher learning rates are avoided in order to restrict oscillations in state error to smaller values. However, this comes at the cost of higher convergence time of critic neural network weights. This paper addresses that problem by proposing a novel tuning law containing a variable gain gradient descent for critic neural network that can adjust the learning rate based on Hamilton-Jacobi-Bellman (HJB) error. By allowing high learning rate the proposed variable gain gradient descent tuning law could improve the convergence time of critic neural network weights. Simultaneously, it also results in tighter residual set, on which trajectories of augmented system converge to, leading to smaller oscillations in state error. A tighter bound for UUB stability of the proposed update mechanism is proved. Numerical studies are then presented to validate the robust Reinforcement Learning control scheme in controlling a continuous time nonlinear system.

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arXiv2020

Simultaneous Identification and Optimal Tracking Control of Unknown Continuous Time Nonlinear System With Actuator Constraints Using Critic-Only Integral Reinforcement Learning

In order to obviate the requirement of drift dynamics in adaptive dynamic programming (ADP), integral reinforcement learning (IRL) has been proposed as an alternate formulation of Bellman equation.However control coupling dynamics is still needed to obtain closed form expression of optimal control effort. In addition to this, initial stabilizing controller and two sets of neural networks (NN) (known as Actor-Critic) are required to implement IRL scheme. In this paper, a stabilizing term in the critic update law is leveraged to avoid the requirement of an initial stabilizing controller in IRL framework to solve optimal tracking problem with actuator constraints. With such a term, only one NN is needed to generate optimal control policies in IRL framework. This critic network is coupled with an experience replay (ER) enhanced identifier to obviate the necessity of control coupling dynamics in IRL algorithm. The weights of both identifier and critic NNs are simultaneously updated and it is shown that the ER-enhanced identifier is able to handle parametric variations better than without ER enhancement. The most salient feature of the novel update law is its variable learning rate, which scales the pace of learning based on instantaneous Hamilton-Jacobi-Bellman (HJB) error. Variable learning rate in critic NN coupled with ER technique in identifier NN help in achieving tighter residual set for state error and error in NN weights as shown in uniform ultimate boundedness (UUB) stability proof. The simulation results validate the presented "identifier-critic" NN on a nonlinear system.

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arXiv2020

Critic-Only Integral Reinforcement Learning Driven by Variable Gain Gradient Descent for Optimal Tracking Control

Integral reinforcement learning (IRL) was proposed in literature to obviate the requirement of drift dynamics in adaptive dynamic programming framework. Most of the online IRL schemes in literature require two sets of neural network (NNs), known as actor-critic NN and an initial stabilizing controller. Recently, for RL-based robust tracking this requirement of initial stabilizing controller and dual-approximator structure could be obviated by using a modified gradient descent-based update law containing a stabilizing term with critic-only structure. To the best of the authors' knowledge, there has been no study on leveraging such stabilizing term in IRL algorithm framework to solve optimal trajectory tracking problems for continuous time nonlinear systems with actuator constraints. To this end a novel update law leveraging the stabilizing term along with variable gain gradient descent in IRL framework is presented in this paper. With these modifications, the IRL tracking controller can be implemented using only critic NN, while no initial stabilizing controller is required. Another salient feature of the presented update law is its variable learning rate, which scales the pace of learning based on instantaneous Hamilton-Jacobi-Bellman error and rate of variation of Lyapunov function along the system trajectories. The augmented system states and NN weight errors are shown to possess uniform ultimate boundedness (UUB) stability under the presented update law and achieve a tighter residual set. This update law is validated on a full 6-DoF nonlinear model of UAV for attitude control.

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arXiv2020

$\text{H}_{\infty}$ Tracking Control via Variable Gain Gradient Descent-Based Integral Reinforcement Learning for Unknown Continuous Time Nonlinear System

Optimal tracking of continuous time nonlinear systems has been extensively studied in literature. However, in several applications, absence of knowledge about system dynamics poses a severe challenge to solving the optimal tracking problem. This has found growing attention among researchers recently, and integral reinforcement learning (IRL)-based method augmented with actor neural network (NN) have been deployed to this end. However, very few studies have been directed to model-free $H_{\infty}$ optimal tracking control that helps in attenuating the effect of disturbances on the system performance without any prior knowledge about system dynamics. To this end a recursive least square-based parameter update was recently proposed. However, gradient descent-based parameter update scheme is more sensitive to real-time variation in plant dynamics. And experience replay (ER) technique has been shown to improve the convergence of NN weights by utilizing past observations iteratively. Motivated by these, this paper presents a novel parameter update law based on variable gain gradient descent and experience replay technique for tuning the weights of critic, actor and disturbance NNs.

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