What Are Robot Sensing Systems?

Robot sensing systems are the hardware and software assemblies that provide a robot with information about its own state and its surrounding environment. They translate physical phenomena, ranging from reflected laser pulses to joint torques to camera pixel arrays, into data structures that perception and control algorithms can act on. Sensing is a prerequisite for any autonomous behavior: without environmental feedback, a robot can only execute pre-programmed open-loop motions and cannot adapt to unexpected conditions. The IEEE Robotics and Automation Society covers sensing systems across its technical committees on robot perception and sensor fusion.

Sensing systems are typically classified as proprioceptive, measuring the robot's internal state such as joint angles, velocities, and motor currents, or exteroceptive, measuring the external environment such as distance, surface texture, and light intensity. Both categories are necessary for reliable autonomous operation, and modern systems combine them through sensor fusion.

Proprioceptive and Exteroceptive Sensors

Proprioceptive sensors include encoders on motor shafts that measure joint position and velocity, force-torque sensors at the wrist that measure contact forces, and inertial measurement units (IMUs) that track linear acceleration and angular rate. These sensors are essential for closed-loop joint control and for estimating the robot's posture in space.

Exteroceptive sensors sample the surrounding environment. Lidar (light detection and ranging) emits laser pulses and times their return to produce dense three-dimensional point clouds with centimeter-level spatial accuracy. Cameras capture intensity or depth images: RGB cameras provide rich semantic texture, RGB-D cameras add per-pixel depth, and event-based cameras respond to brightness changes at microsecond timescales rather than capturing full frames. Ultrasonic sensors and radar offer complementary coverage in conditions where optical sensors degrade, such as dust, fog, or strong ambient light. The sensor-fusion-based navigation review from PMC organizes these modalities into complementary, redundant, and cooperative relationships, providing a systematic framework for assembling sensor suites.

Multisensor Fusion

No single sensor modality covers all conditions reliably. Multisensor fusion combines readings from multiple sources to produce estimates that are more accurate and resilient to individual sensor failure than any single modality could provide. The dominant fusion architectures operate at three levels: low-level fusion combines raw sensor data before feature extraction; feature-level fusion extracts keypoints or geometric features from each sensor independently and then aligns them; and decision-level fusion lets each sensor produce an independent estimate and then merges the results.

The extended Kalman filter (EKF) and its unscented variant are standard tools for fusing IMU data with lidar and visual odometry in real time, maintaining a probabilistic state estimate that updates with each incoming measurement. The review of lidar, IMU, and camera fusion for SLAM published in Artificial Intelligence Review documents how tightly coupled integration of all three sensor types outperforms any two-sensor combination, particularly in environments with repetitive geometry or intermittent GPS coverage.

Localization and Remote Sensing

Robot sensing systems directly enable localization, the process by which a robot determines its position and orientation within an environment. Sensing data feeds probabilistic filters that maintain a belief distribution over the robot's pose, updating that belief as the robot moves and as new measurements arrive. Internet-connected and remote data acquisition systems extend this capability to scenarios where a central server aggregates sensor streams from multiple robots operating in formation, allowing coordinated mapping of large areas that no single robot could cover alone. Active vibration control uses force and acceleration sensors to detect mechanical resonances in the robot structure and compensate for them in real time, improving trajectory accuracy during high-speed operations.

Applications

Robot sensing systems have applications in a wide range of fields, including:

• Autonomous vehicles using lidar, radar, and camera arrays for real-time obstacle detection
• Search and rescue robots gathering environmental data in GPS-denied disaster zones
• Industrial inspection robots using vision and force sensors to detect surface defects
• Medical robots using tactile and force feedback to guide minimally invasive procedures
• Agricultural robots using multispectral imaging to assess crop health across large fields