Anibotix Robotics

Anindita Bhowmick  • Peerlist

Voice control Drone

With Raspberry pi and Arduino nano Servo motors And the chasis by autocad designs

Humanoid robot

Quantum computing in Robotics

Website

Programmable Robotics Kit for your children (7 to 16 years) Arduino Uno Ultrasonic Sensor IR sensors L398N motor driver Li-ion battery pack DC gear motors Rubber wheels Including Obstacle avoidance robot Line following robot Human following robot Radar control robot Light following robot RC car 20+ Arduino projects

Programmable Robotics Kit

Stair climbing robot

The transition from "augmented" dining to a FULLY AUTONOMOUS HOSPITALITY ECOSYSTEM. By synchronizing robotic culinary units, autonomous service droids, and an integrated IoT infrastructure, we can eliminate human error, maximize hygiene, and optimize the thermodynamic efficiency of the commercial kitchen. THE CULINARY CORE: Precision Robotics The "heart" of the restaurant features multi-axis robotic arms equipped with specialized end-effectors for chopping, sautéing, and plating. CONSISTENCY: Every dish follows a digital recipe with milligram precision, ensuring flavor profiles remain identical regardless of volume. THERMAL MANAGEMENT: Induction-based heating elements are controlled via real-time sensors Rather than mimicking human gait, the servers utilize high-speed SLAM and LiDAR to navigate dining floors. DYNAMIC PATHING: Real-time obstacle avoidance allows for fluid movement in crowded spaces. HANDOUT MECHANICS: Automated tray-leveling systems ensure liquids never spill, even during sudden stops. Automation extends beyond the visible "staff." The entire building functions as a single, coordinated machine. THE ENTRANCE : Computer Vision & Biometrics , Doors open via facial recognition or pre-booked digital tokens; seamless check-in. THE MENU: AR/Holographic Interface  Guests visualize 3D renders of dishes; real-time ingredient sourcing transparency. PAYMENT : Blockchain/IoT Tunnels , Zero-click checkout; the system detects when a guest departs and settles the tab via encrypted proximity sensors. While the "Human Touch" has long been a staple of hospitality, the AUTONOMOUS GASTRONOMY ECOSYSTEM offers a different value proposition: UNRIVALED RELIABILITY. By removing the variables of fatigue and distraction, we create a dining environment that is faster, safer, and remarkably efficient. The next challenge lies in "Social Robotics"—teaching these machines to understand the nuances of a guest's mood through sentiment analysis of their voice and posture.

The Autonomous Gastronomy Ecosystem: Integrat

As Unmanned Aerial Vehicles (UAVs) Drone transition from manual teleoperation to full autonomy, the need for intuitive, hands-free Human-Robot Interaction (HRI) has become critical. This paper presents a novel framework for Autonomous Voice Control (AVC) that leverages on-board Edge-AI and Large Language Models (LLMs) to bridge the gap between natural language intent and complex flight maneuvers. Unlike traditional systems reliant on rigid keyword matching, our proposed architecture utilizes a multi-stage pipeline: (1) a noise-robust Automatic Speech Recognition (ASR) module capable of filtering high-decibel rotor interference, (2) a semantic interpreter that maps ambiguous vocal commands (e.g., "Check the roof for damage") into discrete trajectory waypoints, and (3) a real-time PID-controlled obstacle avoidance system. Experimental results conducted in variable wind conditions and acoustic environments demonstrate a command recognition accuracy of 94.2% and a mean latency of <120ms, satisfying the requirements for real-time safety. Furthermore, we introduce a fail-safe "Semantic Lock" protocol that prevents the execution of contradictory or unsafe commands. Our findings suggest that LLM-integrated voice interfaces significantly reduce cognitive load for operators in search-and-rescue and industrial inspection domains, paving the way for truly conversational autonomous agents in the 3D workspace. Keywords: UAV Autonomy, Natural Language Processing (NLP), Human-Robot Interaction, Edge Computing, Swarm Intelligence, Flight Control Systems. Using jetson nano ROS2 WEB UI RL ML

Semantic-to-Signal: Robust Autonomous UAV Dro

Robo soccer battle bot

The Ascend-Companion project explores the intersection of high-mobility robotics and domestic service. Unlike traditional vacuum bots limited to single-floor planes, this 6-wheeled rover utilizes a rocker-bogie-inspired suspension to traverse stairs. Integration of a central 5-DOF (Degrees of Freedom) robotic arm and Embodied AI allows the agent to perceive, reason, and interact with physical objects—bridging the gap between "moving" and "doing." 2. Mechanical Configuration & Mobility The primary challenge of home assistance is the "vertical barrier." Our design addresses this through a specialized 6-wheel drive system. Chassis: A split-body frame with independent suspension for each wheel. The Robotic Arm: Centrally mounted to maintain a low center of gravity. This placement is crucial during stair ascent to prevent the rover from tipping backward.

Development of an Embodied AI 6-Wheeled Rover

This model presentation develops a robust control law for a mobile robot operating in a dynamic environment where the objectives of target tracking and obstacle avoidance are non-orthogonal. We propose a unified navigation function based on a composite potential field U(q) defined in the robot’s configuration space C. By utilizing the gradient descent of this manifold, we derive a control velocity vector v that ensures asymptotic stability toward the target while maintaining a strict safety manifold around detected obstacles. Analytical proofs of stability are provided using Lyapunov’s Direct Method, ensuring that the robot converges to the target state qt without violating the proximity constraints ρmin. Simulations and hardware benchmarks indicate that this mathematical coupling significantly mitigates the “local minima” problem and provides smoother trajectory tracking compared to traditional heuristic-based switching logic.

Lyapunov-Based Control Synthesis for Integrat

This study investigates the mathematical modeling and control stabilization of a Two-Wheeled Inverted Pendulum (TWIP) robot. Inherently unstable and non-linear, the TWIP system requires high-frequency sensory feedback and precise control actuation to maintain equilibrium. We derive the equations of motion using Lagrangian Mechanics and linearize the system around its unstable equilibrium point. The research evaluates the efficacy of a State-Space control approach versus a multi-loop PID (Proportional-Integral-Derivative) architecture. To mitigate sensor inaccuracies, a recursive Kalman Filter is integrated for optimal state estimation. The results provide a robust mathematical foundation for developing balancing platforms capable of handling dynamic payloads and external perturbations.

Dynamics and Control Optimization of a Two-Wh

The "battle" aspect is facilitated by a high-impulse solenoid. A capacitor bank is utilized to provide a momentary burst of high current, allowing the solenoid to extend with enough force to propel a standard soccer projectile. The firing sequence is managed via a MOSFET switching circuit opto-isolated from the ESP32 to prevent EMF back-flow. The device utilizes the ESP-NOW protocol, a connectionless communication technology that reduces the overhead of traditional Wi-Fi handshaking. This allows multiple "battle bots" to operate in the same physical space without channel interference. A robotic soccer platform comprising A chassis configured for high-impact resistance Hardware Configuration: The robot's hardware is centered around the Xtensa® Dual-Core 32-bit LX6 microprocessor. Locomotion: A differential drive system utilizing high-torque DC gear motors controlled via TB6612FNG dual-channel motor drivers. Kinetic Striker: A solenoid-based kicker system. High-performance strikers often utilize a high-voltage capacitor bank to provide the necessary impulse for ball displacement (Hasbialloh et al., 2021). The solenoid translates electrical energy into linear kinetic energy through electromagnetic induction. Software & Task Distribution: The system utilizes FreeRTOS for preemptive multitasking. By employing task pinning via xTaskCreatePinnedToCore(), the system architecture is split as follows: Core 0 (Networking Core): Handles the ESP-NOW stack. ESP-NOW is a connectionless protocol that bypasses the traditional Wi-Fi handshaking process, reducing latency significantly compared to standard TCP/IP protocols. Core 1 (Application Core): Executes time-critical functions, including Pulse Width Modulation (PWM) generation for motors, Proportional-Integral (PI) speed control, and trigger logic for the kicker bots.

Design and Implementation of a Low-Latency Ro

We finally finished the "cable spaghetti" phase. Integrating 42 degrees of freedom (DoF) into a chassis the size of a standard adult male is, frankly, a spatial nightmare. We’ve opted for high-torque density quasi-direct drive motors in the hips and knees to allow for transparency in force control. Observations: Thermal management: The internal cooling fans are louder than anticipated. It sounds less like a futuristic helper and more like a vacuum cleaner having an existential crisis. Weight Distribution: The center of mass (CoM) is currently 2cm higher than the CAD model predicted due to reinforced plating in the thoracic cavity.