To develop all of this software from scratch would require a lot of time and expense. Fortunately, a lot of the required software already exists and this enables systems to be built more time/cost efficiently. This in turn allows development to focus on creating software to suit the specific application of the robot.

The following sections take a brief look at the example software components shown above.

Operating System

An operating system (OS) manages resources and provides a platform for a robot’s software to run on. One of the resources will be processing resource and most operating systems provide multi-tasking which facilitates modular software design. A typical robot will rely on many software tasks running concurrently.

Device Drivers

A device driver is a piece of software that is responsible for the operation of a hardware device – such as a sensor, actuator or network interface. Device drivers are often complex because they need to deal with sometimes complicated hardware interfaces and deal with asynchronous events quickly. Where an operating system is used device drivers will often need to make use of a device driver framework provided by the OS. The will also need to work co-operatively with the operating system to ensure maximum overall efficiency.

Networking

Networking software provides the mechanisms that allow a computer or robot to communicate with other systems over a network. It manages network interfaces, data transmission, and communication protocols such as TCP/IP, UDP, Ethernet, Wi-Fi, and Bluetooth. This software handles tasks like packet routing, error detection, congestion control, and security, ensuring that data is sent and received reliably and efficiently between processes, devices, or remote systems.

In robotic systems, networking software enables functions such as remote monitoring, multi-robot coordination, and communication with cloud services or control stations. By abstracting low-level network details, it allows higher-level applications and middleware to exchange information without needing to manage hardware-specific complexities. Reliable networking support is essential for distributed, collaborative, and real-time robotic applications.

Feature Extraction

Robots rely on sensors to perceive their environment. Sensor processing software collects raw data from devices such as cameras, lidar, ultrasonic sensors, infrared sensors, and encoders. This software filters noise, synchronizes sensor inputs, and converts raw signals into meaningful information.

For example, camera data may be processed using computer vision algorithms to detect objects, recognize faces, or track movement. Sensor fusion techniques combine data from multiple sensors to improve accuracy and reliability.

Motion Control

Control software governs how a robot moves and interacts with the physical world. It translates high-level commands into low-level motor actions. Examples include controlling wheel speeds, joint angles in robotic arms, or the grip force of a robotic hand.

This software often uses control algorithms such as PID (Proportional-Integral-Derivative) controllers, model-based control, or adaptive control. Real-time performance is critical here, as delays can lead to instability or inaccurate movements.

Event Detection

Event detection refers to a robot’s ability to recognize significant changes or occurrences in its environment or internal state that require attention or action. These events may include external situations such as obstacle appearance, human interaction, object detection, or environmental changes, as well as internal conditions like sensor failure, low battery, or system errors. Event detection relies on continuous monitoring of sensor data and system signals, using techniques such as threshold checks, pattern recognition, and machine learning to distinguish meaningful events from normal operation.

Once an event is detected, it can trigger appropriate responses such as alerting higher-level decision-making systems, modifying the robot’s behaviour, or initiating safety procedures. Effective event detection allows robots to react quickly and appropriately to dynamic conditions, improving autonomy, safety, and reliability. This capability is especially important in real-time applications like autonomous vehicles, industrial automation, and human–robot collaboration, where timely responses to events are critical for successful operation.

Movement Planning and Navigation

Perception software allows a robot to interpret sensor data and build an understanding of its surroundings. This includes recognizing obstacles, identifying objects, and estimating distances and positions.

A common component in this layer is mapping and localization software. Techniques such as Simultaneous Localization and Mapping (SLAM) enable robots to create maps of unknown environments while tracking their own position within those maps. This software is essential for mobile robots, autonomous vehicles, and drones.

Motion planning software determines how a robot should move from one point to another while avoiding obstacles. It calculates safe and efficient paths based on the robot’s capabilities and the environment.

Navigation systems combine motion planning, localization, and obstacle avoidance. Algorithms such as A*, Dijkstra’s algorithm, and rapidly exploring random trees (RRTs) are commonly used for path planning. This component is vital for autonomous robots operating in dynamic environments.

Action Planning and Management

At a higher level, decision-making software allows robots to choose actions based on goals, sensor inputs, and internal states. This component may include rule-based systems, state machines, or behaviour trees.

More advanced robots use artificial intelligence (AI) and machine learning techniques. These enable capabilities such as learning from experience, adapting to new situations, and recognizing patterns. Examples include reinforcement learning for control tasks and neural networks for perception.

Mission Planning

Mission planning is the process of defining what a robot must accomplish and how it will achieve those objectives within a given environment and set of constraints. It involves breaking down a high-level mission—such as delivering items, exploring an area, or inspecting infrastructure—into a sequence of achievable tasks and actions. Mission planning takes into account the robot’s capabilities, available resources (like time, energy, and sensors), environmental conditions, and operational rules or safety requirements. The goal is to ensure that the robot can complete its mission efficiently, safely, and reliably.

In practice, mission planning combines decision-making, scheduling, and task coordination. It often uses models, rules, or AI-based techniques to select appropriate actions, adapt to changes, and re-plan when unexpected events occur. For example, if a robot encounters an obstacle or a system fault, mission planning software may modify the plan or choose an alternative strategy. This ability to plan and adapt is essential for autonomous robots operating in dynamic or uncertain environments, such as search-and-rescue missions, space exploration, or warehouse automation.

Health Monitor

Safety software monitors system health and ensures the robot operates within safe limits. It can detect faults such as sensor failures, overheating, or unexpected collisions and trigger emergency stops or recovery procedures.

Fault management software increases reliability and is especially critical in industrial, medical, and collaborative robotic systems.

Action Monitor

A robot’s action monitor is responsible for tracking the execution and outcomes of the robot’s actions to ensure they are being carried out as intended. As the robot performs tasks—such as moving, grasping objects, or following a planned path—the action monitor compares expected results with actual feedback from sensors and control systems. It checks whether actions are completed successfully, delayed, or failing, and identifies deviations like missed targets, excessive force, or unexpected motion.

By continuously evaluating action performance, the action monitor supports robustness and autonomy in robotic systems. If an action does not produce the desired outcome, the monitor can trigger corrective measures such as retrying the action, adjusting parameters, or informing higher-level planning and decision-making modules. This feedback loop helps robots operate safely and reliably, especially in complex or dynamic environments where uncertainty and execution errors are common.

Process Monitor

A robot’s process monitor oversees the overall execution of ongoing tasks and system processes to ensure that the robot’s operations remain consistent with the mission plan and system constraints. It tracks the progress of high-level processes such as task sequences, navigation routines, or manipulation workflows, verifying that each stage starts, runs, and completes as expected. The process monitor also watches for abnormal conditions like stalled tasks, timing violations, resource conflicts, or unexpected state transitions.

By maintaining awareness of the robot’s internal processes, the process monitor enables timely detection of failures and inefficiencies. When problems are identified, it can initiate recovery actions such as restarting a process, switching to an alternative strategy, or escalating the issue to mission planning or supervisory control. This monitoring capability improves reliability, coordination, and safety, particularly in autonomous and long-duration robotic operations.

Human-Robot Interface

Human–robot interaction (HRI) software enables communication between humans and robots. This may include graphical user interfaces, voice recognition, gesture detection, or touchscreen controls.

HRI software ensures that robots are easy to program, monitor, and operate.

Robot-Robot Interface

Robots often need to communicate with other robots, computers, or cloud services. Communication software handles data exchange over wired or wireless networks using protocols such as Ethernet, Wi-Fi, Bluetooth, or CAN bus.

This component supports tasks like remote monitoring, coordination in multi-robot systems, and cloud-based processing or updates.