arXiv subjects

Santosh Devasia

Publications and source records attributed to Santosh Devasia.

At least 19 recordsLinked to original sources

arXiv2025

Mitigating Dynamic Tip-Over during Mobile Crane Slewing using Input Shaping

Payload swing during rapid slewing of mobile cranes poses a safety risk, as it generates overturning moments that can lead to tip-over accidents of mobile cranes. Currently, to limit the risk of tip-over, mobile crane operators are forced to either reduce the slewing speed (which lowers productivity) or reduce the load being carried to reduce the induced moments. Both of these approaches reduce productivity. This paper seeks to enable rapid slewing without compromising safety by applying input shaping to the crane-slewing commands generated by the operator. A key advantage of this approach is that the input shaper requires only the information about the rope length, and does not require detailed mobile crane dynamics. Simulations and experiments show that the proposed method reduces residual payload swing and enables significantly higher slewing speeds without tip over, reducing slewing completion time by at least 38% compared to unshaped control. Human control with input shaping improves task completion time by 13%, reduces the peak swing by 18%, and reduces the potential of collisions by 82% when compared to unshaped control. Moreover, shaped control with a human had no tip-over, whereas large swing led to tip-over without input shaping. Thereby, the proposed method substantially recovers the operational-safety envelope of mobile cranes (designed to avoid tip-over using static analysis) that would otherwise be lost in dynamic conditions. Videos and demonstrations are available at https://youtu.be/dVy3bbIhrBU.

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arXiv2025

Simulated Annealing for Multi-Robot Ergodic Information Acquisition Using Graph-Based Discretization

One of the goals of active information acquisition using multi-robot teams is to keep the relative uncertainty in each region at the same level to maintain identical acquisition quality (e.g., consistent target detection) in all the regions. To achieve this goal, ergodic coverage can be used to assign the number of samples according to the quality of observation, i.e., sampling noise levels. However, the noise levels are unknown to the robots. Although this noise can be estimated from samples, the estimates are unreliable at first and can generate fluctuating values. The main contribution of this paper is to use simulated annealing to generate the target sampling distribution, starting from uniform and gradually shifting to an estimated optimal distribution, by varying the coldness parameter of a Boltzmann distribution with the estimated sampling entropy as energy. Simulation results show a substantial improvement of both transient and asymptotic entropy compared to both uniform and direct-ergodic searches. Finally, a demonstration is performed with a TurtleBot swarm system to validate the physical applicability of the algorithm.

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arXiv2025

Rapidly Converging Time-Discounted Ergodicity on Graphs for Active Inspection of Confined Spaces

Ergodic exploration has spawned a lot of interest in mobile robotics due to its ability to design time trajectories that match desired spatial coverage statistics. However, current ergodic approaches are for continuous spaces, which require detailed sensory information at each point and can lead to fractal-like trajectories that cannot be tracked easily. This paper presents a new ergodic approach for graph-based discretization of continuous spaces. It also introduces a new time-discounted ergodicity metric, wherein early visitations of information-rich nodes are weighted more than late visitations. A Markov chain synthesized using a convex program is shown to converge more rapidly to time-discounted ergodicity than the traditional fastest mixing Markov chain. The resultant ergodic traversal method is used within a hierarchical framework for active inspection of confined spaces with the goal of detecting anomalies robustly using SLAM-driven Bayesian hypothesis testing. Experiments on a ground robot show the advantages of this framework over three continuous space ergodic planners as well as greedy and random exploration methods for left-behind foreign object debris detection in a ballast tank.

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arXiv2024

Semi-autonomous Teleoperation using Differential Flatness of a Crane Robot for Aircraft In-Wing Inspection

Visual inspection of confined spaces such as aircraft wings is ergonomically challenging for human mechanics. This work presents a novel crane robot that can travel the entire span of the aircraft wing, enabling mechanics to perform inspection from outside of the confined space. However, teleoperation of the crane robot can still be a challenge due to the need to avoid obstacles in the workspace and potential oscillations of the camera payload. The main contribution of this work is to exploit the differential flatness of the crane-robot dynamics for designing reduced-oscillation, collision-free time trajectories of the camera payload for use in teleoperation. Autonomous experiments verify the efficacy of removing undesired oscillations by 89%. Furthermore, teleoperation experiments demonstrate that the controller eliminated collisions (from 33% to 0%) when 12 participants performed an inspection task with the use of proposed trajectory selection when compared to the case without it. Moreover, even discounting the failures due to collisions, the proposed approach improved task efficiency by 18.7% when compared to the case without it.

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arXiv2023

Active Anomaly Detection in Confined Spaces Using Ergodic Traversal of Directed Region Graphs

We provide the first step toward developing a hierarchical control-estimation framework to actively plan robot trajectories for anomaly detection in confined spaces. The space is represented globally using a directed region graph, where a region is a landmark that needs to be visited (inspected). We devise a fast mixing Markov chain to find an ergodic route that traverses this graph so that the region visitation frequency is proportional to its anomaly detection uncertainty, while satisfying the edge directionality (region transition) constraint(s). Preliminary simulation results show fast convergence to the ergodic solution and confident estimation of the presence of anomalies in the inspected regions.

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arXiv2023

Output-Sampled Model Predictive Path Integral Control (o-MPPI) for Increased Efficiency

The success of the model predictive path integral control (MPPI) approach depends on the appropriate selection of the input distribution used for sampling. However, it can be challenging to select inputs that satisfy output constraints in dynamic environments. The main contribution of this paper is to propose an output-sampling-based MPPI (o-MPPI), which improves the ability of samples to satisfy output constraints and thereby increases MPPI efficiency. Comparative simulations and experiments of dynamic autonomous driving of bots around a track are provided to show that the proposed o-MPPI is more efficient and requires substantially (20-times) less number of rollouts and (4-times) smaller prediction horizon when compared with the standard MPPI for similar success rates. The supporting video for the paper can be found at https://youtu.be/snhlZj3l5CE.

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arXiv2022

Precision Data-enabled Koopman-type Inverse Operators for Linear Systems

The advent of easy access to large amount of data has sparked interest in directly developing the relationships between input and output of dynamic systems. A challenge is that in addition to the applied input and the measured output, the dynamics can also depend on hidden states that are not directly measured. The main contribution of this work is to identify the information needed (in particular, the past history of the output) to remove the hidden state dependence in Koopman-type inverse operators for linear systems. Additionally, it is shown that the time history of the output should be augmented with the instantaneous time derivatives of the output to achieve precision of the inverse operator. This insight into the required output (history and instantaneous derivative) information, to remove the hidden-state dependence and improve the precision of data-enabled inverse operators, is illustrated with an example system.

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arXiv2021

Communication-free Cohesive Flexible-Object Transport using Decentralized Robot Networks

Decentralized network theories focus on achieving consensus and in speeding up the rate of convergence to consensus. However, network cohesion (i.e., maintaining consensus) during transitions between consensus values is also important when transporting flexible structures. Deviations in the robot positions due to loss of cohesion when moving flexible structures from one position to another, such as uncuredcomposite aircraft wings, can cause large deformations, which in turn, can result in potential damage. The major contribution of this work is to develop a decentralized approach to transport flexible objects in a cohesive manner using local force measurements, without the need for additional communication between the robots. Additionally, stability conditions are developed for discrete-time implementation of the proposed cohesive transition approach, and experimental results are presented, which show that the proposed cohesive transportation approach can reduce the relative deformations by 85% when compared to the case without it.

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arXiv2020

Rapid Transitions with Robust Accelerated Delayed Self Reinforcement for Consensus-Based Networks

Rapid transitions are important for quick response of consensus-based, multi-agent networks to external stimuli. While high-gain can increase response speed, potential instability tends to limit the maximum possible gain, and therefore, limits the maximum convergence rate to consensus during transitions. Since the update law for multi-agent networks with symmetric graphs can be considered as the gradient of its Laplacian-potential function, Nesterov-type accelerated-gradient approaches from optimization theory, can further improve the convergence rate of such networks. An advantage of the accelerated-gradient approach is that it can be implemented using accelerated delayed-self-reinforcement (A-DSR), which does not require new information from the network nor modifications in the network connectivity. However, the accelerated-gradient approach is not directly applicable to general directed graphs since the update law is not the gradient of the Laplacian-potential function. The main contribution of this work is to extend the accelerated-gradient approach to general directed graph networks, without requiring the graph to be strongly connected. Additionally, while both the momentum term and outdated-feedback term in the accelerated-gradient approach are important in general, it is shown that the momentum term alone is sufficient to achieve balanced robustness and rapid transitions without oscillations in the dominant mode, for networks whose graph Laplacians have real spectrum. Simulation results are presented to illustrate the performance improvement with the proposed Robust A-DSR of 40% in structural robustness and 50% in convergence rate to consensus, when compared to the case without the A-DSR. Moreover, experimental results are presented that show a similar 37% faster convergence with the Robust A-DSR when compared to the case without the A-DSR.

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arXiv2020

MIMO ILC for Precision SEA robots using Input-weighted Complex-Kernel Regression

This work improves the positioning precision of lightweight robots with series elastic actuators (SEAs). Lightweight SEA robots, along with low-impedance control, can maneuver without causing damage in uncertain, confined spaces such as inside an aircraft wing during aircraft assembly. Nevertheless, substantial modeling uncertainties in SEA robots reduce the precision achieved by model-based approaches such as inversion-based feedforward. Therefore, this article improves the precision of SEA robots around specified operating points, through a multi-input multi-output (MIMO), iterative learning control (ILC) approach. The main contributions of this article are to (i) introduce an input-weighted complex kernel to estimate local MIMO models using complex Gaussian process regression (c-GPR) (ii) develop Geršgorin-theorem-based conditions on the iteration gains for ensuring ILC convergence to precision within noise-related limits, even with errors in the estimated model; and (iii) demonstrate precision positioning with an experimental SEA robot. Comparative experimental results, with and without ILC, show around 90% improvement in the positioning precision (close to the repeatability limit of the robot) and a 10-times increase in the SEA robot's operating speed with the use of the MIMO ILC.

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arXiv2020

Cohesive Networks using Delayed Self Reinforcement

How a network gets to the goal (a consensus value) can be as important as reaching the consensus value. While prior methods focus on rapidly getting to a new consensus value, maintaining cohesion, during the transition between consensus values or during tracking, remains challenging and has not been addressed. The main contributions of this work are to address the problem of maintaining cohesion by: (i) proposing a new delayed self-reinforcement (DSR) approach; (ii) extending it for use with agents that have higher-order, heterogeneous dynamics, and (iii) developing stability conditions for the DSR-based method. With DSR, each agent uses current and past information from neighbors to infer the overall goal and modifies the update law to improve cohesion. The advantages of the proposed DSR approach are that it only requires already-available information from a given network to improve the cohesion and does not require network-connectivity modifications (which might not be always feasible) nor increases in the system's overall response speed (which can require larger input). Moreover, illustrative simulation examples are used to comparatively evaluate the performance with and without DSR. The simulation results show substantial improvement in cohesion with DSR.

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arXiv2019

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

Efficient utilization of cooperating robots in the assembly of aircraft structures relies on balancing the workload of the robots and ensuring collision-free scheduling. We cast this problem as that of allocating a large number of repetitive assembly tasks, such as drilling holes and installing fasteners, among multiple robots. Such task allocation is often formulated as a Traveling Salesman Problem (TSP), which is NP-hard, implying that computing an exactly optimal solution is computationally prohibitive for real-world applications. The problem complexity is further exacerbated by intermittent robot failures necessitating real-time task reallocation. In this letter, we present an efficient method that exploits workpart geometry and problem structure to initially generate balanced and conflict-free robot schedules under nominal conditions. Subsequently, we deal with the failures by allowing the robots to first complete their nominal schedules and then employing a market-based optimizer to allocate the leftover tasks. Results show an improvement of 11.5\% in schedule efficiency as compared to an optimized greedy multi-agent scheduler on a four robot system, which is especially promising for aircraft assembly processes that take many hours to complete. Moreover, the computation times are similar and small, typically hundreds of milliseconds.

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arXiv2019

Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks

Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonomically safe, require monitoring, or need immediate attention. For performance evaluation, in addition to an open-source kitchen dataset, we collected a new dataset comprising twenty individuals picking up and placing objects of varying weights to and from cabinet and table locations at various heights. Results show very high (87-94)\% F1 overlap scores among the ground truth and predicted frame labels for videos lasting over two minutes and consisting of a large number of actions.

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arXiv2018

Accelerated Consensus for Multi-Agent Networks through Delayed Self Reinforcement

This article aims to improve the performance of networked multi-agent systems, which are common representations of cyber-physical systems. The rate of convergence to consensus of multi-agent networks is critical to ensure cohesive, rapid response to external stimuli. The challenge is that increasing the rate of convergence can require changes in the network connectivity, which might not be always feasible. Note that current consensus-seeking control laws can be considered as a gradient-based search over the graph's Laplacian potential. The main contribution of this article is to improve the convergence to consensus, by using an accelerated gradient-based search approach. Additionally, this work shows that the accelerated-consensus approach can be implemented in a distributed manner, where each agent applies a delayed self reinforcement, without the need for additional network information or changes to the network connectivity. Simulation results of an example networked system are presented in this work to show that the proposed accelerated-consensus approach with DSR can substantially improve synchronization during the transition by about ten times, in addition to decreasing the transition time by about half, when compared to the case without the DSR approach. This is shown to improve formation control during transitions in networked multi-agent systems.

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arXiv2018

Faster Response in Bounded-Update-Rate, Discrete-time Networks using Delayed Self-Reinforcement

The response speed of a network impacts the efficacy of its actions to external stimuli. However, for a given bound on the update rate, the network-response speed is limited by the need to maintain stability. This work increases the network-response speed without having to increase the update rate by using delayed self-reinforcement (DSR), where each agent uses its already available information from the network to strengthen its individual update law. Example simulation results are presented that show more than an order of magnitude improvement in the response speed (quantified using the settling time) with the proposed DSR approach.

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arXiv2018

Rapid Information Transfer in Networks with Delayed Self Reinforcement

The cohesiveness of response to external stimuli depends on rapid distortion-free information transfer across the network. Aligning with the information from the network has been used to model such information transfer. Nevertheless, the rate of such diffusion-type, neighbor-based information transfer is limited by the update rate at which each individual can sense and process information. Moreover, models of the diffusion-type information transfer do not predict the superfluid-like information transfer observed in nature. The contribution of this article is to show that self reinforcement, where each individual augments its neighbor-averaged information update using its previous update, can (i) increase the information-transfer rate without requiring an increased, individual update-rate; and (ii) capture the observed superfluid-like information transfer. This improvement in the information-transfer rate without modification of the network structure or increase of the bandwidth of each agent can lead to better understanding and design of networks with fast response.

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arXiv2017

Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators

When robots operate in unknown environments small errors in postions can lead to large variations in the contact forces, especially with typical high-impedance designs. This can potentially damage the surroundings and/or the robot. Series elastic actuators (SEAs) are a popular way to reduce the output impedance of a robotic arm to improve control authority over the force exerted on the environment. However this increased control over forces with lower impedance comes at the cost of lower positioning precision and bandwidth. This article examines the use of an iteratively-learned feedforward command to improve position tracking when using SEAs. Over each iteration, the output responses of the system to the quantized inputs are used to estimate a linearized local system models. These estimated models are obtained using a complex-valued Gaussian Process Regression (cGPR) technique and then, used to generate a new feedforward input command based on the previous iteration's error. This article illustrates this iterative machine learning (IML) technique for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful convergence of the IML approach to reduce the tracking error.

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arXiv2017

Iterative Machine Learning for Output Tracking

This article develops iterative machine learning (IML) for output tracking. The input-output data generated during iterations to develop the model used in the iterative update. The main contribution of this article to propose the use of kernel-based machine learning to iteratively update both the model and the model-inversion-based input simultaneously. Additionally, augmented inputs with persistency of excitation are proposed to promote learning of the model during the iteration process. The proposed approach is illustrated with a simulation example.

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