Community Cargo Planner

Useful cargo. A planned return.
Materiais úteis. Retorno planejado.

Open the interactive study · Abrir estudo em português · Guided walkthrough · Reproduce the examples

English · Português

English

Choose educational supplies under declared payload and energy assumptions. Compare what changes when reserves, carrier mass or energy cost increase.

Research question: Which independent return trips maximize declared priority without spending the return reserve?

This is a working, independent software study for a proposed Vbot companion-robotics program. You can run every example without a robot, account, package installation or API key. Physical validation has not been performed.

Explore in one minute

1. Open Library workshop.
2. Inspect the chart and the explanation below it. The starting result is 3 supplies · 54.72/55 Wh · 19 priority.
3. Adjust a control or select a contrasting example. Download a printable HTML report, a CSV evidence table or the full JSON analysis.

View a portable example report.

The JSON editor is available under Advanced; the main study uses guided controls.

What you can investigate

• Exact subset selection for up to 20 items, rather than a greedy priority ranking.
• Payload includes the carrier; energy includes outbound payload and the empty return.
• Explicit starting charge and return reserve with independent out-and-back trips.
• Six cost-sensitivity points that re-optimize the selection at each point.
• Item-level exclusion reasons distinguish overload from energy/priority tradeoffs.

All five studies include a responsive English/Portuguese interface, keyboard focus styles, synthetic narrated examples and local report exports. Analysis runs in your browser after the page loads.

Five examples, five questions

Example	Question it exposes	Reproducible core result
Supplies for an event	Compare priorities, return trips and an overloaded box.	2 supplies · 51.8/55 Wh · 12 priority
Library workshop	Five supplies compete for the same energy budget.	3 supplies · 54.72/55 Wh · 19 priority
Lower starting charge	A larger reserve leaves less energy for the exercise.	1 supply · 12.6/15 Wh · 6 priority
The carrier has mass too	Increase carrier mass and review the exclusions.	3 supplies · 40.9/55 Wh · 13 priority
Uncertain energy cost	A higher energy-cost assumption can change the optimal selection.	2 supplies · 53.12/55 Wh · 14 priority

These are deterministic scenario results, not field measurements. Experiment notes distinguish expected regression outputs from independent arithmetic and failure-case tests.

Run locally

Install Node.js 22 or newer. No npm dependencies are required.

git clone https://github.com/galafis/vbot-community-cargo-planner.git
cd vbot-community-cargo-planner
npm run serve

Open http://127.0.0.1:8765. The local server binds to your computer only. Stop with Ctrl+C. For another port, use node server.mjs 8766.

npm run check                                  # syntax, domain tests, examples and server checks
node cli.mjs examples/library-outreach.json            # core analysis
node study.mjs examples/library-outreach.json          # analysis plus derived insights
node cli.mjs examples/baseline.json new-result.json

The CLI refuses to overwrite an existing output file. Exit 1 means invalid input or an I/O failure; review findings are valid analytical outputs and do not change the exit code.

How the study works

flowchart LR
  A[Supplies and declared limits] --> B[Check payload with carrier]
  B --> C[Price independent return trips]
  C --> D[Exact subset search]
  D --> E[Preserve energy reserve]
  E --> F[Re-optimize cost sensitivity]
  F --> G[Explain selection and exclusions]

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Method: Exact subset optimization with carrier mass, return trips and sensitivity. The core engine and the insight layer are pure local calculations shared by browser and CLI. Architecture · Domain method · Input contract.

Evidence and scope

Evidence available now	Next evidence needed
19 automated tests, five reproducible fixtures and a real interface screenshot	Measurements from a specific supplied Vbot unit
Deterministic calculations and explicit counterexamples	Repeated permitted exercises with operator observations
Bilingual methods, reports and example inputs	Appropriate authorization before institutional or participant studies

A standard Vbot without an open SDK is welcome. The proposed first hardware stage uses native features, the official app and manual observations. Any later programmatic integration is optional and must be permitted by the manufacturer. Native workflow · 90-day study plan.

Project map

src/        Pure domain engine, contracts and derived insights
examples/   Five narrated inputs and reproducible expected outputs
app.mjs     Guided bilingual browser workbench
styles.css  Responsive, printable visual system
cli.mjs     Core analysis from a local JSON file
study.mjs   Complete study from a local JSON file
tests/      Domain, insight, reproduction and HTTP checks
docs/       Walkthrough, experiments, method and study plan

Português

Escolha materiais educacionais considerando hipóteses explícitas de carga e energia. Compare o efeito da reserva, do suporte e do consumo.

Pergunta do estudo: Quais viagens independentes maximizam a prioridade sem consumir a reserva de retorno?

O projeto está pronto para exploração em software. Os exemplos são fictícios e a validação física ainda depende de uma unidade. Uma unidade comum, sem SDK aberto, é suficiente para iniciar a fase proposta com recursos nativos e observação manual.

Comece por um exemplo concreto

Abra Oficina na biblioteca. O resultado inicial é 3 materiais · 54.72/55 Wh · 19 pontos. Observe o gráfico, mude um parâmetro ou escolha outro cenário. A interface explica os resultados e exporta relatório HTML imprimível, tabela CSV e análise JSON.

Funcionalidades

• Seleção exata de subconjuntos com até 20 materiais.
• O suporte entra na carga e na energia; o retorno vazio também é calculado.
• Carga inicial e reserva explícitas, com viagens independentes de ida e volta.
• Seis pontos de sensibilidade recalculam a seleção quando o consumo aumenta.
• Motivos por material distinguem excesso de carga de compromisso entre energia e prioridade.

Exemplos disponíveis

Exemplo	O que permite examinar	Resultado reproduzível
Materiais para um evento	Compare prioridade, retorno e uma caixa que excede a carga.	2 materiais · 51.8/55 Wh · 12 pontos
Oficina na biblioteca	Cinco materiais concorrem pelo mesmo orçamento de energia.	3 materiais · 54.72/55 Wh · 19 pontos
Carga inicial reduzida	Uma reserva maior deixa menos energia disponível para a missão.	1 material · 12.6/15 Wh · 6 pontos
O suporte também pesa	Aumente a massa do suporte e reveja as exclusões.	3 materiais · 40.9/55 Wh · 13 pontos
Consumo incerto	Uma hipótese de consumo maior pode mudar a seleção ótima.	2 materiais · 53.12/55 Wh · 14 pontos

Executar e contribuir

Use os comandos da seção em inglês com Node.js 22 ou superior. Não é necessário instalar pacotes. npm run check executa verificações de sintaxe, testes e reprodução dos cenários. O guia DEMO explica um percurso de exploração; EXPERIMENTS registra os resultados e suas limitações.

Sugestões úteis incluem novos cenários fictícios, cálculos independentes, melhorias de acessibilidade da interface e protocolos de observação autorizados. Veja CONTRIBUTING.

Study collection · Coleção de estudos

01 · Companion Follow Lab · 02 · Portuguese Interaction Bench · 03 · Community Cargo Planner · 04 · Inclusive Encounter Kit · 05 · Community Observation Lab

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Created by Gabriel Demetrios Lafis · Applied AI and robotics portfolio

MIT-licensed independent work. Vbot is a third-party brand; this is not an official SDK or an announced Vbot/USP partnership.