Electronics Guide

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Sensing and Perception

A robot acts on the world it can measure. Exteroceptive sensors describe the environment: cameras, lidar, millimeter-wave radar, ultrasonic rangefinders, and tactile skins. Proprioceptive sensors describe the machine itself: joint encoders, resolvers, current sensors, joint-torque sensors, and inertial measurement units. Modern manipulators instrument nearly every joint, because torque measured at the joint is what makes collision detection and compliant contact possible without an external force sensor at the wrist.

No single modality is sufficient. A camera gives dense appearance but fails in glare or darkness, lidar gives precise geometry but degrades in fog and dust, and radar measures range and radial velocity directly but resolves shape poorly. Combining them is the standard remedy, and probabilistic estimators such as the Kalman filter and its extended and unscented variants remain the workhorses for merging asynchronous, noisy measurements into a single state estimate. The design of these pipelines is treated in depth under multi-sensory fusion.

Sensor timing matters as much as sensor quality. Fusing a camera frame with an inertial sample is only meaningful if both carry accurate timestamps, so hardware triggering and a shared network timebase are standard practice. Latency, not raw accuracy, often sets the limit on how fast a robot may safely move near people or obstacles.

Computation and Real-Time Control

Robot computation is layered by timescale. Innermost are the current and torque loops inside each motor drive, which run at the switching frequency of the inverter, commonly ten to twenty kilohertz. Above them sit position and velocity loops, then whole-body or Cartesian trajectory control, then planning and perception, and finally task-level reasoning. Each layer runs slower and tolerates more jitter than the one beneath it. Only the innermost layers carry hard real-time deadlines, which is why heterogeneous designs are common: a microcontroller or field-programmable gate array closes the fast loops while an application processor runs perception and planning under a general-purpose operating system.

Deterministic communication ties the layers together. Industrial fieldbuses such as EtherCAT, PROFINET IRT, and SERCOS III, and increasingly Time-Sensitive Networking on standard Ethernet, deliver bounded latency rather than merely high throughput. EtherCAT commonly runs multi-axis machines at cycle times of one millisecond, supports cycles as short as roughly 125 microseconds, and uses hardware distributed clocks that hold network-wide jitter below a microsecond. That determinism is what allows many axes to execute a coordinated motion as if they shared a single clock.

Higher in the stack, middleware such as ROS 2 organizes perception, planning, and control as communicating nodes over a publish-subscribe transport. This layer trades strict determinism for modularity and reuse, and it is where machine-learning inference now sits. Learned perception and policy models increasingly run on dedicated accelerators at the edge, both to meet latency budgets and to avoid depending on a network link; see edge AI processors for the hardware side of that shift.

Actuation and Power Electronics

Actuation is where robotics consumes most of its energy and most of its design effort. Brushless permanent-magnet motors dominate, driven by three-phase inverters under field-oriented control, which resolves the measured phase currents into flux-producing and torque-producing components so that torque may be commanded directly. Getting this right determines both efficiency and the smoothness of motion; the underlying techniques are covered under motor drive and control and motor drivers and controllers.

Gearing choices shape the whole machine. Strain-wave and cycloidal reducers give high ratios in a compact package but introduce friction and elasticity that complicate force control. Series elastic actuators deliberately place a compliant element between motor and load, turning force control into a position measurement across a known spring and giving the joint inherent shock tolerance. Direct-drive and quasi-direct-drive designs, which use low gear ratios and large-diameter motors, trade peak torque for transparency and fast, accurate force control, and they have become common in legged robots.

Soft robots replace rigid joints altogether. Pneumatic and hydraulic actuators deliver high force with simple structures but need valves, pumps, and a compressible supply, and their bandwidth is limited by fluid dynamics. Shape memory alloys are compact and powerful but are thermally cycled, so their response is slow and their electrical efficiency low. Dielectric elastomer actuators respond quickly, at hundreds of hertz, yet conventionally require kilovolt-scale drive supplied by compact high-voltage converters; recent low-voltage designs have brought useful performance down toward a few hundred volts, which matters because high-voltage electronics are difficult to make untethered and safe near people.

Mobile platforms live inside an energy budget. Practical lithium-ion cells store roughly 200 to 300 watt-hours per kilogram, and every joule spent on computation, communication, or thermal management is a joule unavailable for motion. Battery management, high-efficiency DC-DC conversion, regenerative braking on legged and wheeled platforms, and careful duty-cycling of sensors and processors therefore determine endurance as much as the drivetrain does.

Scaling Down: Physics at the Microscale

Shrinking a robot does not simply shrink its components; it changes which forces matter. Volume-dependent effects such as inertia and weight fall off as the cube of the characteristic length, while surface-dependent effects such as adhesion, van der Waals forces, electrostatics, and surface tension fall off far more slowly. Below roughly a millimeter, surface forces dominate, and a microrobot is more likely to stick to a surface than to fall off it.

Fluid behavior changes too. At the microscale the Reynolds number is very small, viscous forces overwhelm inertia, and motion stops the instant the driving force does. Purcell's scallop theorem captures the consequence: a reciprocal motion, one that retraces its own sequence in reverse, produces no net displacement in this regime. Effective microswimmers therefore use non-reciprocal strategies, most commonly chiral helical bodies that convert rotation into translation under an external rotating magnetic field, in the manner of a corkscrew.

Power is the other hard constraint. Batteries do not scale down gracefully, so most micro and nanorobots carry no onboard energy source. They are instead driven and steered by external fields, using magnetic coil systems, focused ultrasound, or optical traps, with catalytic chemical propulsion as an alternative in suitable media. The electronics consequently migrate off the robot and into the field-generation and imaging apparatus, while the robot itself becomes a passive but carefully engineered structure. Where devices do integrate transducers on-chip, they build on the fabrication techniques described under MEMS devices.

Safety, Standards, and Fault Tolerance

Once robots and people share space, safety becomes an electronic design requirement rather than a fence. The ISO 10218 series is the core international standard for industrial robot safety, with part 1 addressing the robot itself and part 2 addressing the robot system and its integration. The 2025 revision, the first major update since 2011, folded in the collaborative-application requirements previously published separately as ISO/TS 15066, and it deliberately speaks of collaborative applications rather than collaborative robots, on the principle that safety is a property of the installed application and not of the arm alone.

Four collaborative techniques underpin these requirements: safety-rated monitored stop, hand guiding, speed and separation monitoring, and power and force limiting. The first two are largely procedural and sensor-gated. Speed and separation monitoring requires continuous measurement of the distance between person and robot, typically with safety-rated scanners, light curtains, or three-dimensional vision, and it slows or stops the robot as that distance closes. Power and force limiting requires torque sensing or accurate current-based torque estimation at every joint so that contact forces stay below biomechanical thresholds.

Implementing these functions correctly is the domain of functional safety. ISO 13849-1 expresses the required capability of a safety function as a performance level, IEC 62061 expresses it as a safety integrity level, and IEC 61800-5-2 defines the drive-level functions that realize it, including safe torque off, safe stop 1 and 2, and safely limited speed. Meeting the higher levels generally demands redundant channels, diagnostic coverage, and hardware that detects its own faults. The site treats this subject in detail under functional safety standards and robotics and collaborative robot standards.

Environmental robustness is a related discipline. Robots working in space, in reactor buildings, or in accelerator halls need radiation-hardened electronics and tolerance of single-event effects. Mobile and industrial machines need electromagnetic compatibility good enough that a motor inverter switching tens of amperes does not corrupt the encoder feedback a few centimeters away, along with thermal designs that survive sealed enclosures and continuous duty.

Coordination and Collective Behavior

Multi-robot systems raise a distinct set of electronic problems. Swarm approaches deliberately keep the individual unit simple and cheap, relying on local sensing and short-range communication so that useful global behavior emerges without a central controller. Research platforms have demonstrated self-assembly and shape formation in collectives of a thousand or more minimal robots, which is possible only because each unit is inexpensive and its communication needs are modest.

Wireless links become the bottleneck as the collective grows. Bandwidth is shared, contention rises with density, and latency becomes unpredictable exactly when coordination matters most. Practical designs therefore favor algorithms tolerant of message loss, such as consensus and gossip protocols, and they push decision-making onto each robot rather than routing it through a base station. Localization is the companion problem: satellite navigation is unavailable indoors and underwater, so swarms rely on relative sensing, ultra-wideband ranging, or collaborative estimation in which robots refine one another's position estimates.

The same distributed thinking now appears in warehouse fleets and in agricultural machinery, where dozens of vehicles must share space and tasks safely. The industrial end of this spectrum is covered under industrial robotics and automation.

Applications Across Industries

Advanced robotics is reshaping a wide span of fields, and each one imposes its own electronic constraints. In healthcare, teleoperated robotic surgery platforms demand low-latency force and motion pipelines, sterilizable mechanisms, and rigorous fault detection, while targeted microrobots promise localized drug delivery and minimally invasive intervention. Assistive robotics adds the requirement that the machine be safe, quiet, and comprehensible in a home.

In manufacturing and logistics, collaborative arms share workcells with people while autonomous mobile robots move material through warehouses, and both depend on safety-rated sensing and deterministic control networks. Agriculture uses robotic platforms for selective harvesting and crop monitoring, where perception must cope with wind, dust, and variable daylight. Exploration relies on autonomous systems in deep-sea, planetary, and disaster environments, including orbital servicing robotics, where communication delay makes onboard autonomy mandatory rather than optional. Development across all of these domains increasingly relies on shared toolchains and simulation, described under robotics development platforms.

Engineering Trade-offs

Advanced robotics offers few free choices. Stiffness delivers precision and bandwidth but makes contact dangerous; compliance makes contact safe but blurs positioning and complicates control. Onboard computation reduces latency and dependence on a network but consumes power and generates heat inside a sealed shell. Richer sensing improves robustness yet raises data rates, processing load, and cost. Redundancy improves fault tolerance but adds mass, which a mobile platform pays for in every second of operation.

Learned control policies illustrate the newest version of this tension. They handle variation and contact-rich tasks that are difficult to model explicitly, yet they resist the deterministic analysis that safety certification expects. The prevailing practice is architectural: a learned policy proposes motion while an independent, verifiable safety layer, implemented in certified hardware, retains authority to limit speed, limit force, or remove torque. Understanding where each boundary lies is what separates a demonstration from a deployable system.

About This Category

This category examines the electronic technologies that enable next-generation robotic capabilities, from the nanoscale to the macroscale. The pages that follow explore the sensing, control, actuation, and coordination systems that make modern robots possible, and they trace how the same constraints of latency, energy, and safety reappear in very different forms at each scale.