Documentation and explanation here and here
DXHMP Mission Simulator
Apply a standardized mission workload to a configurable reference humanoid and inspect the complete one-second calculation.
Power trace
Mission profile definition
Phases
Second-by-second trace
| Time | Phase | Task | Speed | Payload | Reference W | Scaled W | Thermal | Joint util. |
|---|
Adjust parameters to see why runtime and power change.
Method and limitations
DXHMP profiles are proposed engineering workloads. They are not certification standards until validated through multi-platform and multi-laboratory studies. The simulator scales reference power through a fixed-power term, a mass-dependent term, payload correction, battery availability and a field factor.
Download DXHMP Mission Profiles
Download an individual active mission profile as an import-compatible CSV trace or as an Excel-compatible workbook containing metadata, phase definitions, and the complete 300-second trace.
Canonical cross-domain mission for comparable energy, thermal, electrical and runtime reporting.
Repetitive, precision-oriented manufacturing workload with sustained upper-body utilization and inspection compute.
High-throughput material movement with long loaded travel, ramp operation and repeated acceleration.
Human-proximate, low-noise mission emphasizing safe interaction, sustained support and redundant perception.
Severe physical mission combining irregular terrain, heavy tools, overhead work, pushing and repeated crouching.
Long-range outdoor inspection across variable surfaces, slopes, adverse weather and intermittent connectivity.
Maximum-dynamic rescue mission with sprinting, debris, crawling, casualty handling and degraded communication.
Fault-tolerant mission for radiation, reduced convection, severe temperature, latency and restricted maintenance.
DX Simu Parameter Guide
Definitions, practical ranges, dependencies and simulation effects for every adjustable parameter used by DX Simu.
Quick engineering starting points
Preset values are illustrative DXHMP reference configurations, not product specifications.
Light service and interaction-oriented humanoid reference.
General industrial humanoid reference.
Heavy-duty industrial and outdoor humanoid reference.
Mission definition
Selects the standardized workload and repetition plan.
Mission profile
Selects the standardized workload governing movement, payload, perception, environment, power demand, thermal loading, battery use, and resulting runtime calculations directly.
- Recommended use
- Use DXHMP-R for comparison; choose an application profile for deployment-oriented design studies.
- Simulation effect
- Changes every second of the reference workload, including power, payload, joint utilization, thermal index, compute demand and resulting runtime.
- Dependencies
- Mission database revision, profile status and selected robot capability.
Simulation influence
Planned mission cycles
Number of repeated five-minute missions evaluated; determines total energy, planned duration, final state of charge, feasibility, and completed mission count.
- Recommended use
- 1 for comparison; 12 for one hour; 96 for an eight-hour equivalent
- Simulation effect
- Multiplies mission energy and duration without changing per-cycle power. Battery feasibility and final SOC update with the selected count.
- Dependencies
- Mission energy, battery availability, initial SOC and minimum SOC.
Simulation influence
Robot and energy system
Defines physical scale, battery capacity and continuous system demand.
Robot total mass
Total operational robot mass including battery and standard equipment; higher mass increases locomotion power, joint torque, current, losses, and temperatures.
- Recommended use
- 45–90 kg for general full-size humanoids
- Simulation effect
- Raises the mass-dependent portion of power according to the selected exponent, reducing runtime and increasing actuator and inverter stress.
- Dependencies
- Reference mass, mass exponent, payload and mission speed.
Simulation influence
Installed battery energy
Installed battery energy available before operating limits; larger capacity extends runtime but increases robot mass, packaging, cost, and thermal requirements.
- Recommended use
- 1.5–4.0 kWh for general industrial humanoids
- Simulation effect
- Increases usable energy and available cycles nearly proportionally, before mass, ageing, temperature and discharge-rate effects are considered.
- Dependencies
- Usable SOC, battery ageing, initial SOC and minimum SOC.
Simulation influence
Usable SOC fraction
Share of installed battery energy permitted for operation; conservative limits protect cells and reserves but reduce runtime and mission cycles.
- Recommended use
- 0.80–0.90
- Simulation effect
- Directly scales available energy. A lower value reduces runtime but can improve battery life, reserve margin and low-voltage robustness.
- Dependencies
- Battery management limits, chemistry, temperature, ageing and safety reserve.
Simulation influence
Fixed system power
Continuous power for compute, sensing, controls, communications, cooling, and standing; strongly influences runtime especially during idle or low-motion mission phases.
- Recommended use
- 250–500 W for a general industrial humanoid
- Simulation effect
- Adds continuously to every mission second. Higher fixed power particularly penalizes healthcare, inspection and assembly profiles with limited locomotion.
- Dependencies
- Compute configuration, sensors, cooling, communications and standing-control strategy.
Simulation influence
Scaling and calibration
Controls how the reference workload is translated to a specific robot configuration.
Reference mass
Robot mass associated with the reference power trace; it determines how simulated mass scales the mission’s mass-dependent electrical power contribution.
- Recommended use
- 80 kg for the current DXHMP baseline
- Simulation effect
- Defines the denominator of the mass-scaling relationship. A higher reference mass lowers the scaling factor for a fixed simulated mass.
- Dependencies
- Robot mass and mass exponent.
Simulation influence
Mass-scaling exponent
Controls nonlinear scaling of mass-dependent power; values above one penalize heavier robots, while values below one reduce mass sensitivity substantially.
- Recommended use
- 0.9–1.1 until platform-specific calibration exists
- Simulation effect
- Changes the curvature of power versus mass and therefore affects runtime sensitivity, peak power and comparisons between robot classes.
- Dependencies
- Robot mass, reference mass and calibrated physical model.
Simulation influence
Payload factor
Scales payload-related demand without changing task timing; higher values increase carrying energy, joint loading, current, losses, and thermal stress proportionally.
- Recommended use
- 0.5–1.5
- Simulation effect
- Adjusts the payload contribution in loaded mission phases. It affects energy and joint stress only where the profile carries payload.
- Dependencies
- Profile payload trace, payload location and actuator architecture.
Simulation influence
Power-model correction
Calibration multiplier applied to modeled power; measured data correct systematic underprediction or overprediction across the complete standardized mission trace consistently.
- Recommended use
- 1.0 before calibration; measured ratio afterward
- Simulation effect
- Scales all modeled power, energy and runtime results directly. It should not be used to conceal model-structure errors.
- Dependencies
- Measurement quality, selected mission, robot configuration and model maturity.
Simulation influence
Operational availability
Defines battery condition, field allowance and usable operating window.
Battery ageing factor
Remaining usable battery capacity relative to new condition; degradation directly reduces available energy, runtime, and completed mission cycles proportionally overall.
- Recommended use
- 0.80 end-of-life qualification; 1.00 new battery
- Simulation effect
- Scales usable battery energy without changing instantaneous mission power. It reduces runtime and available cycles in direct proportion.
- Dependencies
- Cell chemistry, calendar age, cycle history, temperature and maintenance.
Simulation influence
Field-efficiency factor
Conservative adjustment from ideal to field runtime, accounting for unmodeled losses, pauses, corrections, auxiliaries, environmental variability, and operational inefficiencies realistically.
- Recommended use
- 0.80–0.90 for early engineering estimates
- Simulation effect
- Reduces reported expected runtime while leaving per-cycle energy unchanged. It provides a transparent planning allowance rather than hidden conservatism.
- Dependencies
- Model completeness, deployment variability, operator workflow and environmental conditions.
Simulation influence
Initial SOC
Battery state of charge at mission start; lower values reduce available energy, runtime, completed cycles, and peak-power operating margin immediately.
- Recommended use
- 0.90–1.00 for qualification tests
- Simulation effect
- Defines starting energy. It changes available runtime, cycles and final SOC but does not alter per-cycle energy demand.
- Dependencies
- Charging strategy, battery voltage and minimum SOC.
Simulation influence
Minimum SOC
Lowest permitted battery state during operation; increasing reserve improves protection and emergency margin but reduces runtime and completed mission cycles.
- Recommended use
- 0.10–0.20
- Simulation effect
- Defines the runtime termination reserve. Higher values reduce available energy and protect against voltage sag, ageing and emergency demands.
- Dependencies
- Battery chemistry, power demand, safety concept and reserve policy.
Simulation influence
No parameter matches the current search.
