Robotics Engineering Services in Kerala

The Robotics Revolution: Engineering Intelligent Machines

Robotics engineering sits at the convergence of mechanical design, embedded systems, sensor technology, control theory, and artificial intelligence — creating machines that perceive their environment, make decisions, and execute physical actions autonomously or semi-autonomously. The global robotics market is projected to surpass $280 billion by 2030, driven by industrial automation, logistics, healthcare, defense, and consumer applications.

At Hexcode Plus R&D in Thiruvananthapuram, our active robotics laboratory develops autonomous mobile robots, robotic manipulator systems, and sensor fusion platforms. We bridge the gap between academic robotics research and practical industrial deployment, building working prototypes that navigate real environments, manipulate objects, and integrate with broader automation systems. Our expertise spans the complete robotics stack — from motor driver firmware and PID control loops to high-level path planning and SLAM algorithms running on embedded Linux platforms.

India's robotics adoption is accelerating across manufacturing, warehousing, agriculture, and defense sectors. Kerala, with its strong educational institutions and growing technology infrastructure, is well-positioned to contribute to this growth. Our laboratory in Thiruvananthapuram provides robotics R&D services including custom robot design, prototype development, ROS integration, and proof-of-concept validation for organizations exploring automation solutions.

Service Portfolio

Robotics Engineering Services

Autonomous Mobile Robots (AMR)

Autonomous Mobile Robots navigate dynamic environments without external guidance infrastructure like magnetic tape or floor markers. Using onboard sensors (LiDAR, cameras, IMU), SLAM algorithms, and path planning, AMRs build maps of their surroundings and navigate to goal positions while avoiding obstacles. We design and build complete AMR platforms — from chassis and motor selection to navigation software stack integration.

AMR DEVELOPMENT SERVICES:

→ Robot platform design: chassis, drive train, power system
→ Motor selection and driver integration (DC, BLDC, Stepper)
→ Differential drive, Ackermann, and omnidirectional kinematics
→ Wheel odometry with quadrature encoder integration
→ PID controller tuning for velocity and position control
→ Obstacle detection: ultrasonic, infrared, LiDAR, depth camera
→ Sensor fusion: combining odometry, IMU, and LiDAR for localization
→ Battery management: Li-Po/Li-Ion charging, monitoring, protection

Platforms: Raspberry Pi 4/5, NVIDIA Jetson, STM32

Sensors: RPLidar, YDLiDAR, Intel RealSense, IMU

Drive: Differential, Mecanum, Ackermann

SLAM: Simultaneous Localization & Mapping

SLAM is the computational problem of constructing or updating a map of an unknown environment while simultaneously tracking the robot's location within that map — a fundamental capability for truly autonomous navigation. We implement SLAM solutions using 2D LiDAR sensors (RPLidar, YDLiDAR), depth cameras (Intel RealSense), and ROS-based SLAM packages (Cartographer, GMapping, SLAM Toolbox).

SLAM IMPLEMENTATION:

→ 2D LiDAR SLAM using Cartographer (Google) with loop closure
→ GMapping particle-filter SLAM for small to medium environments
→ SLAM Toolbox for lifelong mapping and dynamic environments
→ Visual SLAM (V-SLAM) using stereo/depth cameras
→ Sensor calibration: LiDAR-IMU, camera-IMU extrinsic calibration
→ Map saving and loading for persistent environment memory
→ Multi-session mapping for large-scale area coverage
→ EKF/UKF sensor fusion for improved localization accuracy

SLAM Packages: Cartographer, GMapping, SLAM Toolbox, ORB-SLAM3

ROS Version: ROS 2 Humble, ROS Noetic

Accuracy: ±5cm position, ±2° orientation

Path Planning & Navigation

Once a map is built, the robot must plan collision-free paths from its current position to goal locations while accounting for dynamic obstacles, kinematic constraints, and optimization criteria (shortest path, smoothest trajectory, minimum energy). We implement path planning stacks using ROS 2 Navigation (Nav2) with support for multiple planners, costmaps, and behavior trees.

NAVIGATION SERVICES:

→ Global planners: Dijkstra, A*, Theta*, RRT, PRM
→ Local planners: DWB (Dynamic Window), TEB (Timed Elastic Band)
→ Costmap configuration: static, dynamic, inflation layers
→ Behavior tree-based navigation state machines
→ AMCL (Adaptive Monte Carlo Localization) for position tracking
→ Multi-goal waypoint navigation with action servers
→ Dynamic obstacle avoidance with sensor-based replanning
→ Navigation in GPS-denied environments (indoor, underground)

Framework: ROS 2 Navigation (Nav2)

Planners: DWB, TEB, Smac Planner (A*, Hybrid-A*)

Localization: AMCL, EKF, UKF

Robotic Manipulator Control

Robotic arms and manipulators require precise kinematic and dynamic modeling to perform tasks such as pick-and-place, assembly, dispensing, and inspection. We develop control systems for multi-axis robotic arms, solving forward and inverse kinematics, trajectory planning, and servo-based joint control with feedback from encoders and force sensors.

MANIPULATOR SERVICES:

→ Forward kinematics: computing end-effector pose from joint angles
→ Inverse kinematics: solving joint angles for desired end-effector pose
→ DH parameter-based robot modeling
→ Trajectory planning: linear, polynomial (cubic, quintic), trapezoidal velocity
→ Multi-axis servo control with synchronized motion
→ Gripper design: pneumatic, electric, vacuum end-effectors
→ Force/torque feedback for compliant manipulation
→ ROS MoveIt integration for motion planning and collision checking

DOF: 3-6 Axis Arms

Control: Position, Velocity, Torque, Hybrid

Framework: ROS MoveIt, Custom IK Solvers

ROS 2 Integration & Development

The Robot Operating System (ROS) has become the standard middleware framework for robotics development worldwide. We specialize in ROS 2 (Humble Hawksbill and later distributions), developing custom ROS nodes, configuring navigation stacks, integrating sensors and actuators, and building complete robot software architectures using ROS 2's DDS-based communication layer for improved real-time performance and security.

ROS 2 SERVICES:

→ Custom ROS 2 node development in C++ and Python
→ Topic, service, and action interface design
→ URDF/Xacro robot description modeling
→ Gazebo/Ignition simulation environment setup
→ Sensor driver integration: LiDAR, camera, IMU, GPS
→ Nav2 navigation stack configuration and tuning
→ MoveIt 2 motion planning for manipulators
→ Behavior tree design for complex task sequences
→ CI/CD pipeline for ROS 2 package testing

ROS Version: ROS 2 Humble, Iron, Jazzy

OS: Ubuntu 22.04/24.04 LTS

Simulation: Gazebo, Ignition, RViz2

Sensor Fusion & Computer Vision

Robust robot perception requires fusing data from multiple heterogeneous sensors — IMU, LiDAR, cameras, encoders, and GPS — into a coherent understanding of the robot's state and environment. We implement multi-sensor fusion pipelines using Kalman filters (EKF, UKF) and complementary filters, combined with computer vision techniques for object detection, tracking, and scene understanding.

PERCEPTION SERVICES:

→ Extended Kalman Filter (EKF) for IMU + odometry fusion
→ Unscented Kalman Filter (UKF) for non-linear system models
→ Complementary filter for attitude estimation (Mahony, Madgwick)
→ Camera-LiDAR extrinsic calibration and sensor fusion
→ Object detection using YOLO, SSD, and custom CNNs
→ AprilTag and ArUco marker detection for precision docking
→ Optical flow for visual odometry
→ Depth estimation from stereo vision and ToF sensors

Filters: EKF, UKF, Particle Filter, Complementary

Vision: OpenCV, YOLO, TensorFlow Lite

Sensors: IMU, LiDAR, Stereo Camera, ToF, GPS

Hardware Architecture

Typical Robot Hardware Stack

COMPUTE

Brain

Raspberry Pi 4/5 or NVIDIA Jetson running Ubuntu + ROS 2. Handles high-level processing: SLAM, navigation, vision, and decision-making. Interfaces with low-level controllers via UART/I2C.

CONTROL

Nervous System

STM32/Arduino-based motor controller running PID loops at 1kHz+. Reads encoder feedback, executes velocity commands from ROS, implements safety watchdogs and emergency stop.

SENSE

Perception

Sensor array: 2D/3D LiDAR for mapping, stereo camera for depth perception, 9-DOF IMU (accel, gyro, magnetometer) for orientation, wheel encoders for odometry, ultrasonic for close-range obstacle detection.

POWER

Energy

Li-Po/Li-Ion battery packs with BMS (Battery Management System), voltage regulation for compute (5V) and motors (12V/24V), power monitoring, and automatic low-battery return-to-dock behavior.

Application Domains

Robotics in Industry

Warehouse Automation

AMRs for goods-to-person order picking, autonomous forklift guidance, inventory scanning robots, and conveyor-to-AMR transfer stations. ROS 2 Nav2-based navigation in known warehouse layouts.

Agriculture Robotics

Autonomous tractors/rovers for crop monitoring, precision spraying, soil sampling, and harvesting assistance. GPS+RTK waypoint navigation combined with computer vision for row following.

Inspection & Monitoring

Pipe inspection robots, confined space survey bots, solar panel cleaning robots, and infrastructure inspection platforms. Sensor payloads for thermal imaging, gas detection, and visual inspection.

Defense & Security

Unmanned ground vehicles (UGV) for surveillance, bomb disposal assistance, and perimeter patrol. Long-range teleoperation with low-latency video streaming and autonomous return-to-base on signal loss.

Education & Research

ROS 2-based educational robot platforms for university laboratories, research prototype development, and competition robots (Robocon, e-Yantra). Custom hardware and software for specific research objectives.

Healthcare Robotics

Hospital delivery robots for medicine and sample transport, telemedicine robot platforms, disinfection UV robots, and assistive robotics for elderly care and rehabilitation therapy.

Building a Robot? Let's Engineer It Together

Whether you need a proof-of-concept autonomous platform, a custom robotic arm controller, or ROS 2 integration for an existing system — our robotics laboratory in Thiruvananthapuram is ready to take on the challenge.

DISCUSS ROBOTICS PROJECT

Common Questions

Frequently Asked Questions

Do you build robots with ROS 2?

Yes. Hexcode Plus builds autonomous platforms on ROS 2 (including Humble) with the Nav2 navigation stack, covering SLAM, AMCL localisation, path planning and sensor fusion. ROS 1 has reached end of life, so new projects should start on ROS 2.

What does SLAM require in hardware?

A capable 2D SLAM platform needs a LiDAR (an RPLidar A2 class unit is sufficient to start), an IMU, wheel encoders for odometry, and a compute board such as a Raspberry Pi 4. A separate microcontroller should close the motor control loop, because a Linux board is not a real-time device.

Why does my robot's map drift or come out bent?

Almost always odometry, not SLAM. SLAM fuses wheel odometry with LiDAR scans, so if the wheel radius, wheelbase or encoder counts are wrong, or the IMU is uncalibrated, the map warps. Fix odometry accuracy first; no amount of SLAM parameter tuning rescues a robot that does not know how far it has travelled.

Can you build robotic arms and manipulators?

Yes. Hexcode Plus works on robotic manipulator control including inverse kinematics, servo and motor control systems, and computer vision integration for pick-and-place and inspection applications.