[ SYSTEM: READY ]

[ GRAD: MAY 2026 ]

[ CORE: V4.0.2-STABLE ]

[ LATENCY: 12ms ]

MS Robotics & Autonomous Systems · ASU

Robotics
Engineer

Saiteja Dasari

M.S. Robotics graduate with 6+ projects across autonomy, controls, perception, and UAV systems. Experience deploying real-time robotic stacks from MATLAB and Simulink digital twins to ROS2 hardware, including multi-robot coordination, fault-tolerant flight, and vision-based landing. Strong in Python, C/C++, MATLAB/Simulink, and Linux.

Target Roles

Robotics EngineerControls EngineerUAV SystemsSystems EngineerRobotics Software EngineerAutonomy EngineerTest & Validation Engineer

bash : 80x24

01saiteja@asu:~$ros2 launch autonomy bringup.launch.py

02[INFO] [launch]: Starting /fdi_controller

03[INFO] [launch]: Starting /path_planner_node

04saiteja@asu:~$ros2 topic echo /system_status

05{

06 "identity": "Saiteja Dasari",

07 "gpa": "3.93 / 4.0",

08 "focus": ["Controls", "Vision", "Autonomy"],

09 "status": "NOMINAL"

10}

11saiteja@asu:~$

[ SYSTEM: READY ][ MODE: BIO ]

ORIGIN_LOG

Origin Log

Operator

Saiteja
Dasari

Base of OperationsTempe, AZ

StatusActive

Bio

M.S. Robotics & Autonomous Systems graduate from Arizona State (May 2026), with work spanning autonomy, controls, perception, fault-tolerant flight, and UAV systems engineering. I build robotic systems that move from digital twin to flight hardware, and I am targeting full-time robotics, controls, autonomy, and UAV roles.

Recent work includes deriving and hardware-validating fault-tolerant control for the Crazyflie 2.1 (max roll cut from 145° to 7.2°), centralized multi-robot task allocation across 5 TurtleBots in ROS2, vision-based autonomous landing on a moving platform at 8 cm accuracy, real-time YOLOv8 crop and weed detection at 12 FPS on ROSMaster X3, and the voice-analytics pipeline for an LLM-agent ATC simulator at Honeywell Aerospace.

I work across MATLAB and Simulink, Python, C and C++, ROS1 and ROS2, Gazebo, MuJoCo, CrazySim SITL, ArduPilot, and Linux. I care about clean abstractions, repeatable tests, and control loops that hold up when sensors lie.

Primary Focus

Robot AutonomyControlsMulti-Robot CoordinationFault-Tolerant FlightReal-Time PerceptionSim-to-Real Validation

[ SYSTEM: READY ][ LATENCY: 12ms ][ MODE: SPECS ]

v4.0.2_CORE

Technical Specification

01code

Programming & Tools

Day-to-day stack for robotics software, simulation, and analysis. Python and C/C++ across embedded, ROS2 nodes, and tooling. MATLAB/Simulink for control design and digital twins.

LanguagesPython, C/C++

PythonC/C++MATLAB/SimulinkBashLinuxGitSolidWorks

02auto_videocam

Robotics & Simulation

Centralized ROS2 autonomy and Gazebo multi-robot environments. MuJoCo and CrazySim SITL for higher-fidelity quadrotor dynamics, with ArduPilot and Mission Planner on production UAVs.

Sim StackGazebo + MuJoCo

ROS1/ROS2GazeboMuJoCoCrazySim SITLRViztf2URDF/xacroArduPilotMission Plannercflib

03settings_input_component

Controls, GNC & Test

Fault detection and isolation, control allocation, and controller reconfiguration on Crazyflie 2.1. SITL workflows, log replay, and flight-log analysis on hardware.

Roll Reduction145° → 7.2°

PIDClosed-Loop ControlState EstimationSensor FusionFDIControl AllocationController ReconfigurationSITLLog ReplayFlight-Log Analysis

04visibility

Autonomy & Perception

Hungarian task assignment and A* planning over costmaps for multi-robot fleets. YOLOv8 and OpenCV pipelines for weed detection; camera-calibrated vision and inverse kinematics for the maze-solver arm task.

Detection97% @ 12 FPS

A*Hungarian AlgorithmPath PlanningCostmapsOccupancy GridsCollision AvoidanceOpenCVYOLOv8Inverse Kinematics

05flight

Flight Platforms

Hands-on with Crazyflie 2.1 (fault-tolerant control), Parrot Mambo (vision-based landing), and P80 heavy-payload multirotor (ArduPilot PID tuning across 3 variants).

Landing CEP8 cm

Crazyflie 2.1Parrot MamboP80 MultirotorArduPilotMission Planner

06engineering

Sim-to-Hardware

Digital twin in Simulink for autonomous landing. MuJoCo rigid-body sim for gyroscopic coupling on Crazyflie, with CrazySim SITL bridging to firmware-in-the-loop testing.

Sim Conditions24 validated

Digital TwinMuJoCoCrazySim SITLSimulinkGazebo

Engineering Meta Data

Revision: STABLE-4.0

Parameter	Value	Variance	Status
Max Roll Reduction	145° → 7.2° via thrust clamp on Crazyflie 2.1	± 0.5°	[ VALIDATED ]
Allocation Methods Tested	9 methods across 4 single-motor failure cases	n/a	[ ACTIVE ]
Sim Attitude Error	0° across 24 test conditions in MuJoCo	n/a	[ NOMINAL ]
CV Detection Accuracy	97% @ 12 FPS, YOLOv8n on ROSMaster X3	± 0.5%	[ PEAK ]
Multi-Robot Success Rate	99% across 5 TurtleBots, 10 Gazebo scenarios	0.01%	[ OPTIMIZED ]
Autonomous Landing CEP	8 cm radius, Parrot Mambo, moving platform	± 1.2 cm	[ VALIDATED ]
UAV Stability Gain	10% improvement, ArduPilot PID, 3 UAV variants	± 1%	[ NOMINAL ]

[ SYSTEM: READY ] [ MODE: BUILD_LOG ]

Build Log

Experience Record: 2023 to 2026

01Aug 2025 to Apr 2026COMPLETED

Externship

Research Externship / Technical Lead, SkySpeak AI

Honeywell Aerospace, Arizona State University Collaborative Research

Directed a 3-engineer team in partnership with Honeywell and ASU stakeholders to deliver an ATC-pilot simulator built on LLM agents, structured guardrails, and a 300 ms LiveKit, Deepgram, and ElevenLabs voice pipeline. Engineered the Python voice analytics stack with YIN pitch detection, voice activity detection, z-score calibration, and abstention scoring under 0.60. Launched a CBTA dashboard used in 20+ stakeholder reviews and seed-grant evaluations.

LLM AgentsLiveKitDeepgramElevenLabsYINVADCBTAPythonReact

VISIT LIVE PRODUCTopen_in_new

02Jan 2025 to May 2026COMPLETED

Academic

Graduate Teaching Assistant

Arizona State University, MATLAB Programming and Instrumentation & Controls Lab

Mentored 90+ students across MATLAB, instrumentation, and controls labs at ASU's Fulton Schools of Engineering. Troubleshot code and hardware setups, led review sessions, and reinforced PID and control-system concepts during weekly office hours.

MATLABInstrumentationControlsPIDLab Instruction

03Aug 2023 to May 2024COMPLETED

Industry

Production Engineering Intern, UAV Systems Integration

Marut Drones (IIIT-Hyderabad)

Assembled and verified UAV subsystems including ESCs, flight controllers, GPS, avionics, and propulsion hardware across multirotor platforms, raising production efficiency by 15%. Tuned ArduPilot and Mission Planner PID parameters from flight logs across 3 UAV variants, root-caused stability issues, and increased flight stability by approximately 10%.

ArduPilotMission PlannerPIDUAV AssemblyFlight Logs3 Variants

04Feb 2026 to Apr 2026COMPLETED

Startup

Co-Founder

Aatram

Co-founded an emotion-first anti-procrastination app for students and young professionals. Designed an adaptive nudge engine leveraging Apple Intelligence for context-aware notifications. Built a Momentum Board with streak alternatives (Bounce-Back Score, Monthly Chapters, momentum tracking), replacing punitive streak systems with psychologically grounded progress mechanics. Integrated evidence-based focus techniques: implementation intentions, WOOP, affect labeling, and temptation bundling.

Product ArchitectureJITAIBehavioral DesignCloudflare Workers

VISIT LIVE PRODUCTopen_in_new

[ SYSTEM: READY ] [ MODE: DEPLOYMENTS ]

Deployments

Project Archive: 2024 to 2026

001

Fault-Tolerant Quadrotor Control

Jan 2026 to May 2026

Max Roll Reduction

145° to 7.2°

Sim Conditions

24 validated

Allocation Methods

9 tested

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allocator.pycode

# 3-motor allocation,
# motor 0 failed
W = clamp_thrust(
    M_3motor @ cmd_wrench
)

motor.set_pwm(
    [0.0, *W]
)

Closed-form 3-motor allocation derived and validated for all 4 single-motor failure cases. Identified motor saturation as the root cause of attitude loss; a thrust clamp reduced max roll from 145° to 7.2° on hardware. MuJoCo rigid-body sim built for gyroscopic coupling, with sim-to-real validation across PID, INDI, and gyroscopic-compensation controllers.

002

Multi-Robot Task Allocation

Dec 2025

Robots

5 TurtleBot3

Success Rate

99%

Tasks

8 dynamic

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task_allocator.pyschema

def distance_fn(r, t):          # double-duty: cost + cache
    path, length = astar_plan(r.xy, t.xy)
    cache[(round_xy(r.xy), t.id)] = path
    return length

while tasks:                    # dynamic, time-stepped
    free = [r for r in robots if r.busy_until <= now]
    C = cost_matrix(free, tasks, distance_fn)
    for r, t in hungarian(C):
        r.busy_until = now + length(r, t) / v_max

A from-scratch ROS2 warehouse stack for 5 TurtleBot3 robots: hand-written A* planner, inflation costmap, Hungarian assignment, and PD trajectory control, with only Nav2's map server and AMCL off-the-shelf. A dynamic, time-stepped loop re-solves the Hungarian assignment as robots free up, and a path cache keyed on (start, task) makes each robot drive exactly the scored route. ~99% mission success across 5 robots and 8 tasks in Gazebo.

003

Crop Weed Detection

May 2025

Accuracy

97%

Throughput

12 FPS

Dataset

3000 samples

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weed_detector.pyfingerprint

def detect(self, frame):
    results = model.predict(
        frame, conf=0.5
    )
    for box in results.boxes:
        cls = box.cls.item()
        if cls == WEED_ID:
            self.flag(box.xyxy)

YOLOv8 model trained on 3000 samples, deployed on ROSMaster X3 at 97% accuracy and 12 FPS. Integrated ROS inference and visualization nodes for real-time robotic decision-making in precision agriculture.

004

Autonomous Drone Landing

May 2025

Landing Accuracy

8 cm CEP

Sim Trials

25 validated

Platform

Parrot Mambo

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landing_controller.pyflight

def track_target(self, frame):
    target = self.detect_marker(frame)
    error = target.pos - self.uav.pos

    cmd = self.pid.compute(error)
    self.publish_velocity(cmd)

Guidance and control logic built in MATLAB and Simulink as a digital twin and validated across 25+ trials. An onboard downward-camera vision-tracking pipeline estimates the landing platform's position relative to the Parrot Mambo, and a closed-loop PID controller lands the drone within 8 cm of a moving platform.

005

Maze Solving with myCobot Pro 600

Dec 2024

Grid

34×34

Plan Time

60 s

Arm

6-DOF myCobot Pro 600

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maze_solver.pyroute

grid = occupancy_grid(          # threshold + perspective crop
    calibrated(frame), cell=34
)
grid = dilate_walls(grid)       # safety margin
path = astar(grid, start, goal) # 4-connected, Manhattan

for wp in path:
    q = ik_pro600(wp)           # MATLAB RigidBodyTree
    arm.set_angles(q)           # over TCP/IP

An OpenCV vision pipeline (camera calibration, binary thresholding, perspective crop) converts a maze image into a 34×34 occupancy grid, with walls dilated to enforce a safety margin. A from-scratch A* planner (4-connected, Manhattan heuristic) finds the path, and inverse kinematics solved with a MATLAB RigidBodyTree maps each waypoint to six joint angles, streamed over TCP/IP to drive a physical 6-DOF myCobot Pro 600 arm. Planning-to-execution runs in about 60 s.

006

SkySpeak AI

Aug 2025 to Apr 2026

Voice Pipeline

300 ms latency

Stakeholder Reviews

20+

Team Size

3 engineers

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voice_analytics.pymic

frame = audio.next()
pitch = yin(frame)
score = z_score(
    pitch, baseline
)
if abstention(score) < 0.6:
    grade = cbta(transcript)
    dashboard.push(grade)

AI-powered ATC-pilot simulator for Honeywell Aerospace's Anthem ecosystem. Real-time voice pipeline at 300 ms (LiveKit, Deepgram, ElevenLabs) paired with Python voice analytics: YIN pitch, VAD, z-score calibration, and abstention scoring under 0.60. CBTA dashboard surfaces cognitive load and communication competency to instructors.

[ SYSTEM: READY ] [ MODE: MANIFEST ]

Manifest

Certifications, Publications & Education

Certifications

precision_manufacturing

Universal Robots e-Series

Core & Pro

Universal Robots

model_training

MathWorks AI Certification

Machine Learning & Deep Learning

MathWorks

psychology

Hugging Face RL Course

Reinforcement Learning

Hugging Face

smart_toy

ROS & ROS2

Robot Operating System

Udemy

Publications

article

SCADA-Based Substation Control Panel & Operations

open_in_newPUB-01

Technical Publication

Published Research

Education

Arizona State University

M.S. Robotics & Autonomous Systems, Systems Engineering Track

GPA: 3.93 / 4.0

Coursework: Aerial Robotics, Mechatronics, Multi-Robot Systems, Controls & Systems

2024 to 2026

Chaitanya Bharathi Institute of Technology

B.E. Electrical & Electronics Engineering

Hyderabad, Telangana

2020 to 2024

[ SYSTEM: READY ][ MODE: CONTACT ]

OPEN FOR OPPORTUNITIES

Initialize Contact

Available for full-time roles, internship roles, collaboration, and research roles

Graduating

May 2026

Open For

Full-time Robotics, Controls, Autonomy, UAV, Test & Validation roles

Location

Tempe, AZ · Open to relocation

Direct Signaltotobotplus@gmail.com

Schedule a CalleventBook on Calendly

ResumedownloadDownload CV

personLINKEDIN PROFILE terminalGITHUB REPOS

Current Status

Open for Opportunities

MS Robotics & Autonomous Systems · ASU

RoboticsEngineer

Origin Log

Technical Specification

Programming & Tools

Robotics & Simulation

Controls, GNC & Test

Autonomy & Perception

Flight Platforms

Sim-to-Hardware

Engineering Meta Data

Research Externship / Technical Lead, SkySpeak AI

Graduate Teaching Assistant

Production Engineering Intern, UAV Systems Integration

Co-Founder

Fault-Tolerant Quadrotor Control

Multi-Robot Task Allocation

Crop Weed Detection

Autonomous Drone Landing

Maze Solving with myCobot Pro 600

SkySpeak AI

Certifications

Universal Robots e-Series

MathWorks AI Certification

Hugging Face RL Course

ROS & ROS2

Publications

SCADA-Based Substation Control Panel & Operations

Education

Arizona State University

Chaitanya Bharathi Institute of Technology

Initialize Contact

Robotics
Engineer