Robotics × energy systems

Problem shape

Imagine a fleet of N robots, each with a battery state bᵢₜ, a task queue, and a position in a facility. The energy system provides a time-varying price signal cₜ and constraints on instantaneous power draw Pₜ.

The control problem is to assign tasks and charging decisions such that:

• Robots complete tasks with acceptable latency.
• Battery safety limits are never violated.
• Total energy cost stays within a budget.

A simple objective

Over a horizon T we can define a basic cost:

J = Σₜ cₜ Pₜ + λ Σₜ Lₜ

where Lₜ is a task-latency penalty and λ balances energy against service quality. The role of deep learning is to approximate the policy that minimises J under constraints on bᵢₜ and Pₜ.

Policy architecture

A practical setup is to combine a learned value or policy network with a hand-engineered safety layer:

def control_step(state, price_signal):
    feasible_actions = safety_layer(state)
    features = encode_state(state, price_signal)

    logits = policy_network(features)
    action = select_action(logits, feasible_actions)

    return action

The safety layer encapsulates hard constraints (battery, thermal limits, grid contracts) and the network learns to trade off cost against latency within that feasible set.

Robotics × energy mermaid schema

At system level the interaction between robots and the energy system looks like: