Robotics Solutions

We primarily use ROS/ROS2 for complex robotic systems, with custom firmware for resource-constrained applications. Our software stack includes navigation (Nav2), computer vision (OpenCV, TensorFlow), motion planning (MoveIt), and simulation (Gazebo, Isaac Sim) tools.

Yes, we design safety-rated systems following ISO 10218 for industrial robots, ISO 13849 for safety-related control systems, and ISO 3691-4 for autonomous mobile robots. Our designs include safety PLCs, emergency stop systems, and collision avoidance features.

Sensor fusion combines data from multiple sensing modalities like LiDAR, cameras, IMUs, and ultrasonic sensors using algorithms like Extended Kalman Filters or particle filters to produce a unified environmental model more accurate than any single sensor alone. This compensates for individual sensor weaknesses, such as LiDAR's inability to detect transparent surfaces or camera degradation in low light.

Mobile robots commonly use A* or Dijkstra's algorithm for global path planning on known maps, combined with local planners like the Dynamic Window Approach (DWA) for real-time obstacle avoidance. For complex or high-dimensional spaces, sampling-based planners such as RRT* provide asymptotically optimal paths, while lattice-based planners generate kinematically feasible trajectories for non-holonomic robots.

ROS 2 is a middleware framework built on DDS (Data Distribution Service) that provides a publish-subscribe communication layer, hardware abstraction, and a rich ecosystem of packages for perception, navigation, and manipulation. Unlike ROS 1, ROS 2 supports real-time execution, multi-robot systems, and production-grade security with DDS-based access control.

Autonomous navigation in dynamic environments combines SLAM for building and updating spatial maps with predictive obstacle tracking using algorithms like velocity obstacles to anticipate moving agents. The navigation stack continuously replans paths using costmap layers that distinguish between static infrastructure and dynamic obstacles, enabling safe operation in warehouses, hospitals, or public spaces.

Collaborative robot safety systems implement ISO 10218 and ISO/TS 15066 standards through speed and separation monitoring, power and force limiting, and safety-rated monitored stop functions using redundant sensor networks. We integrate 3D time-of-flight cameras and capacitive skin sensors to create dynamic safety zones that adjust robot speed based on human proximity.

Robotic arm motion planning uses forward and inverse kinematics solvers to map between joint space and Cartesian task space. Motion planning frameworks like MoveIt 2 layer collision-aware trajectory optimization on top, using OMPL planners to generate smooth, jerk-limited joint trajectories that avoid obstacles while respecting joint limits, velocity constraints, and payload dynamics.

Swarm robotics coordination uses decentralized algorithms inspired by biological systems, where individual robots follow simple local rules that produce emergent collective behaviors like formation control, area coverage, or cooperative transport. Communication is typically limited to local neighbor-to-neighbor messaging, ensuring the system scales gracefully and remains resilient to individual robot failures.

Robot simulation environments like Gazebo, NVIDIA Isaac Sim, or MuJoCo allow developers to test algorithms in physics-accurate virtual worlds before deploying to physical hardware, reducing development cycles and preventing costly damage. Digital twins extend this by maintaining a real-time synchronized virtual replica of a deployed robot, enabling remote monitoring, predictive maintenance, and what-if scenario testing.

Edge computing processes sensor data locally on the robot rather than round-tripping to the cloud, which is critical for real-time control loops requiring sub-10ms latency for tasks like obstacle avoidance or force feedback. We deploy optimized inference engines on edge GPUs like NVIDIA Jetson to run perception models locally, while offloading non-latency-sensitive tasks like map aggregation to cloud infrastructure.