### Environment Setup for openFrameworks

Source: https://github.com/kei18/pibt/blob/master/README.md

This command sets the OF_ROOT environment variable, which is necessary for the openFrameworks visualization to function correctly. Ensure you replace {your openFrameworks directory} with the actual path to your openFrameworks installation.

```bash
export OF_ROOT={your openFrameworks directory}
```

--------------------------------

### CBS Solver Initialization and Execution in C++

Source: https://context7.com/kei18/pibt/llms.txt

Demonstrates the setup and execution of the Conflict-Based Search (CBS) solver. It initializes the environment, agents, tasks, and the MAPF problem, then creates and runs the CBS solver with independent conflict detection enabled. CBS is suitable for smaller agent counts and guarantees optimal paths.

```cpp
#include "solver/cbs.h"

// Initialize CBS solver with independent detection
std::mt19937* MT = new std::mt19937(0);
Graph* G = new SimpleGrid("./map/empty-16-16.map", MT);

Agents A;
std::vector<Task*> T;
Paths points = G->getRandomStartGoal(5);
for (int i = 0; i < 5; ++i) {
    A.push_back(new Agent(points[i][0]));
    T.push_back(new Task(points[i][1]));
}

Problem* P = new MAPF(G, A, T, MT);
P->setTimestepLimit(1000);

// Create CBS solver with independent detection enabled
bool enableID = true;
Solver* solver = new CBS(P, enableID);

// Solve and get optimal paths
bool success = solver->solve();
if (success) {
    std::cout << "Found optimal solution\n";
    std::cout << solver->logStr();
}

// CBS finds conflict-free optimal paths but has exponential worst-case
// Use for up to ~50 agents; PIBT handles hundreds efficiently

```

--------------------------------

### PPS (Parallel Push and Swap) Solver Setup in C++

Source: https://context7.com/kei18/pibt/llms.txt

Illustrates the setup and execution of the Parallel Push and Swap (PPS) solver for Multi-Agent Path Finding (MAPF). This solver uses push and swap primitives to resolve deadlocks and is complete for well-formed instances. It is recommended for dense scenarios where deadlocks are common and guarantees solutions when possible, although it is slower than PIBT.

```cpp
#include "solver/pps.h"

std::mt19937* MT = new std::mt19937(999);
Graph* G = new SimpleGrid("./map/maze-32-32-2.map", MT);

Agents A;
std::vector<Task*> T;
Paths points = G->getRandomStartGoal(15);
for (int i = 0; i < 15; ++i) {
    A.push_back(new Agent(points[i][0]));
    T.push_back(new Task(points[i][1]));
}

Problem* P = new MAPF(G, A, T, MT);
P->setTimestepLimit(8000);

// PPS solver uses push and swap primitives
Solver* solver = new PPS(P, MT);
bool success = solver->solve();

// PPS is complete for well-formed instances
// Best for dense scenarios where deadlocks common
// Slower than PIBT but guarantees solutions when possible
std::cout << "Solution found: " << success << "\n";
std::cout << solver->logStr();

```

--------------------------------

### Windowed PIBT Solver Setup and Execution in C++

Source: https://context7.com/kei18/pibt/llms.txt

Shows how to initialize and run the Windowed PIBT (winPIBT) solver for improved path quality in Multi-Agent Path Finding (MAPF). This includes setting up the environment, agents, tasks, and configuring winPIBT with a specified window size and soft mode. It is an extension of PIBT offering better solution quality at the cost of computation.

```cpp
#include "solver/winpibt.h"

std::mt19937* MT = new std::mt19937(123);
Graph* G = new SimpleGrid("./map/warehouse.map", MT);

Agents A;
std::vector<Task*> T;
Paths points = G->getRandomStartGoal(30);
for (int i = 0; i < 30; ++i) {
    A.push_back(new Agent(points[i][0]));
    T.push_back(new Task(points[i][1]));
}

Problem* P = new MAPF(G, A, T, MT);
P->setTimestepLimit(3000);

// Create winPIBT with window size 5 and soft mode enabled
int windowSize = 5;
bool softMode = true;
Solver* solver = new winPIBT(P, windowSize, softMode, MT);

// Solve with improved path quality vs standard PIBT
solver->solve();

// windowSize: larger values improve solution quality but increase computation
// softMode: allows relaxation of conflicts for better throughput
std::cout << "Elapsed: " << solver->getElapsed() << "\n";

```

--------------------------------

### Custom Graph and Agent Management in C++

Source: https://context7.com/kei18/pibt/llms.txt

Provides examples of managing agent states and interacting with graph structures. It covers setting goals, updating agent positions, tracking history, and querying graph properties like neighbors, distances, paths, dimensions, and node information. This snippet is useful for customizing environments and agent behaviors.

```cpp
#include "graph/graph.h"
#include "agent/agent.h"

// Agent state tracking
Agent* agent = new Agent();
agent->setGoal(targetNode);

// Update agent position and check status
agent->setNode(newNode);
Node* current = agent->getNode();
Node* goal = agent->getGoal();
bool hasGoal = agent->hasGoal();
bool hasTask = agent->hasTask();

// Track history for logging/replay
agent->updateHist();
std::vector<AgentStatus*> history = agent->getHist();

// Access graph structure
Graph* G = new SimpleGrid("./map/arena.map", MT);
Nodes neighbors = G->neighbor(node);
int distance = G->dist(node1, node2);
Nodes path = G->getPath(startNode, goalNode);

// Query graph properties
int width = G->getW();
int height = G->getH();
int nodeCount = G->getNodesNum();
Node* node = G->getNode(x, y);
int nodeIndex = G->getNodeIndex(node);

```

--------------------------------

### Build and Run PIBT Solver with Visualization

Source: https://context7.com/kei18/pibt/llms.txt

Builds the PIBT solver with openFrameworks support and runs it with a visual interface. The parameter file includes settings for visualization like showing icons and specifying icon paths.

```bash
# Build with openFrameworks support
export OF_ROOT=/path/to/openFrameworks
make of

# Run with visualization
make ofrun param=sample-param.txt

# Parameter file with visualization settings:
# PROBLEM_TYPE=MAPF
# SOLVER_TYPE=PIBT
# field=./map/arena.map
# agentnum=50
# timesteplimit=5000
# showicon=1
# icon=./material/peterpan.png
```

--------------------------------

### Build and Run openFrameworks Implementation

Source: https://github.com/kei18/pibt/blob/master/README.md

Commands to compile and run the openFrameworks version of the PIBT simulator. 'make of' builds the project, and 'make ofrun' executes it, taking a parameter file for configuration. The param file specifies simulation details.

```bash
make of
make ofrun param=sample-param.txt
```

--------------------------------

### Configure and Run Solver Comparison Experiments

Source: https://context7.com/kei18/pibt/llms.txt

This section demonstrates how to set up and execute comparative experiments between different solvers (PIBT, CBS, ECBS) on the same map instances. It involves creating separate parameter files for each solver configuration and then iterating through them to run the comparison. This is crucial for evaluating the performance trade-offs of various algorithms.

```bash
# Test PIBT
# param-test-pibt.txt:
# PROBLEM_TYPE=MAPF
# SOLVER_TYPE=PIBT
# field=./map/ost003d.map
# agentnum=50
# timesteplimit=5000
# WarshallFloyd=0
# seed=42

# Test CBS with independent detection
# param-test-cbs.txt:
# PROBLEM_TYPE=MAPF
# SOLVER_TYPE=CBS
# field=./map/ost003d.map
# agentnum=50
# timesteplimit=5000
# ID=1
# seed=42

# Test ECBS (bounded suboptimal)
# param-test-ecbs.txt:
# PROBLEM_TYPE=MAPF
# SOLVER_TYPE=ECBS
# field=./map/ost003d.map
# agentnum=50
# timesteplimit=5000
# suboptimal=1.2
# ID=1
# seed=42

# Run comparison
for file in param-test-*.txt; do
    echo "Testing $file"
    make crun param=$file
done
```

--------------------------------

### Run Pre-defined Scenarios with PIBT

Source: https://context7.com/kei18/pibt/llms.txt

This snippet shows how to compile and run the PIBT solver using a predefined parameter file that specifies a scenario from the MovingAI format. Ensure the 'param-scenario.txt' file is correctly configured with paths to map and scenario files. This is useful for standardized benchmarking.

```bash
# Parameter file with scenario (param-scenario.txt):
# PROBLEM_TYPE=MAPF
# SOLVER_TYPE=PIBT
# field=./map/random-32-32-20.map
# scenario=1
# scenariofile=./scen/random-32-32-20-random-1.scen
# timesteplimit=10000
# seed=0
# log=1
# printtime=1

make c
make crun param=param-scenario.txt

# Scenario files specify exact start/goal positions
# Format: version, map, width, height, startX, startY, goalX, goalY, optimal
# Enables reproducible benchmarking against published results
```

--------------------------------

### Build and Run PIBT Solver for MAPF

Source: https://context7.com/kei18/pibt/llms.txt

Builds the PIBT solver without visualization and runs it using a parameter configuration file for Multi-Agent Path Finding (MAPF). The parameter file specifies problem type, solver type, map, agent count, and time limits.

```bash
# Build without visualization
make c

# Run PIBT solver with configuration
make crun param=sample-param.txt

# Example parameter file (sample-param.txt):
# PROBLEM_TYPE=MAPF
# SOLVER_TYPE=PIBT
# field=./map/arena.map
# agentnum=100
# timesteplimit=50000
# seed=0
# log=1
# printlog=0
# printtime=1
```

--------------------------------

### C++ PIBT Core Algorithm Initialization and Execution

Source: https://context7.com/kei18/pibt/llms.txt

Demonstrates programmatic initialization and execution of the PIBT solver for MAPF problems in C++. It covers loading a graph, creating agents and tasks, setting problem parameters, and running the solver, followed by retrieving results.

```cpp
#include "problem/mapf.h"
#include "solver/pibt.h"
#include "graph/simplegrid.h"
#include "agent/agent.h"

// Initialize random generator
std::mt19937* MT = new std::mt19937(42);

// Load graph from map file
Graph* G = new SimpleGrid("./map/arena.map", MT);

// Create agents at initial positions
Agents A;
Paths startGoalPairs = G->getRandomStartGoal(10);
for (int i = 0; i < 10; ++i) {
    Agent* agent = new Agent(startGoalPairs[i][0]);
    A.push_back(agent);
}

// Create tasks (goals)
std::vector<Task*> T;
for (int i = 0; i < 10; ++i) {
    Task* task = new Task(startGoalPairs[i][1]);
    T.push_back(task);
}

// Initialize MAPF problem
Problem* P = new MAPF(G, A, T, MT);
P->setTimestepLimit(5000);

// Create and run PIBT solver
Solver* solver = new PIBT(P, MT);
bool success = solver->solve();

// Get results
double elapsedTime = solver->getElapsed();
std::string logOutput = solver->logStr();
std::cout << "Solved: " << success << "\n";
std::cout << "Time: " << elapsedTime << "s\n";
```

--------------------------------

### Build and Run Non-Visualization Implementation

Source: https://github.com/kei18/pibt/blob/master/README.md

Commands to compile and run the PIBT simulator without visualization, suitable for experiments. 'make c' compiles the C++ version, and 'make crun' executes it with a specified parameter file for configuration.

```bash
make c
make crun param=sample-param.txt
```

--------------------------------

### Run PIBT Solver for Multi-Agent Pickup and Delivery (MAPD)

Source: https://context7.com/kei18/pibt/llms.txt

Configures and runs the PIBT solver for Multi-Agent Pickup and Delivery (MAPD) problems, focusing on continuous task assignment. The parameter file specifies MAPD-specific settings like task number and frequency, and the output includes task completion metrics.

```bash
# Configuration for MAPD (param-mapd.txt):
# PROBLEM_TYPE=MAPD
# SOLVER_TYPE=PIBT
# field=./map/warehouse.map
# agentnum=20
# timesteplimit=10000
# tasknum=500
# taskfrequency=1
# seed=42

make c
make crun param=param-mapd.txt

# Output shows task completion metrics:
# [solver] elapsed: 1.234s
# [problem] tasks completed: 500
# [problem] makespan: 8234
```

--------------------------------

### Node Selection with Conflict Avoidance in C++

Source: https://context7.com/kei18/pibt/llms.txt

Implements PIBT's node selection logic to choose the next node for an agent, considering the shortest path to the goal and avoiding collisions with other agents. It prioritizes staying in place or random movement if no goal is set and uses tie-breaking randomization. Dependencies include Agent, Node, Graph (G), and Agent list (A).

```cpp
Node* PIBT::chooseNode(Agent* a, Nodes C) {
    // Randomize candidates for tie-breaking
    std::shuffle(C.begin(), C.end(), *MT);

    // If no goal, prefer staying or random walk
    if (!a->hasGoal()) {
        if (inArray(a->getNode(), C)) {
            return a->getNode();
        }
        return C[0];
    }

    Node* g = a->getGoal();

    // Fast path lookup if available
    Nodes p = G->getPath(a->getNode(), g);
    if (p.size() > 1 && inArray(p[1], C)) {
        return p[1];
    }

    // Select minimum cost node
    Nodes cs;
    int minCost = 10000;
    for (auto v : C) {
        int cost = pathDist(v, g);
        if (cost < minCost) {
            minCost = cost;
            cs.clear();
            cs.push_back(v);
        } else if (cost == minCost) {
            cs.push_back(v);
        }
    }

    if (cs.size() == 1) return cs[0];

    // Avoid nodes occupied by other agents
    for (auto v : cs) {
        bool occupied = std::any_of(A.begin(), A.end(),
                                   [v](Agent* b){ return b->getNode() == v; });
        if (!occupied) return v;
    }

    return cs[0];
}
```

--------------------------------

### C++ PIBT Priority Inheritance Mechanism

Source: https://context7.com/kei18/pibt/llms.txt

Details the core update step of the PIBT algorithm in C++, focusing on the priority inheritance mechanism. It includes functions for updating agent priorities based on goal achievement and tie-breakers, and for processing agents based on their current priorities.

```cpp
void PIBT::update() {
    // Update priority values for all agents
    updatePriority();

    // Copy priority list for manipulation
    std::vector<float> PL(priority.size());
    std::copy(priority.begin(), priority.end(), PL.begin());

    // Track reserved nodes and available agents
    Nodes CLOSE_NODE;
    Agents OPEN_AGENT(A.size());
    std::copy(A.begin(), A.end(), OPEN_AGENT.begin());

    // Select agent with highest priority
    auto itr = std::max_element(PL.begin(), PL.end());
    int index = std::distance(PL.begin(), itr);
    Agent* a = OPEN_AGENT[index];

    // Process all agents via priority inheritance
    while (!OPEN_AGENT.empty()) {
        priorityInheritance(a, CLOSE_NODE, OPEN_AGENT, PL);

        // Select next highest priority agent
        itr = std::max_element(PL.begin(), PL.end());
        index = std::distance(PL.begin(), itr);
        a = OPEN_AGENT[index];
    }
}

// Priority increments for agents not reaching goals
void PIBT::updatePriority() {
    for (int i = 0; i < A.size(); ++i) {
        if (A[i]->isUpdated()) {
            eta[i] = 0;
        } else if (A[i]->hasTask() && (A[i]->getNode() != A[i]->getGoal())) {
            eta[i] += 1;  // Increment priority each step
        } else {
            eta[i] = 0;
        }
        priority[i] = eta[i] + epsilon[i];  // epsilon is tie-breaker
    }
}
```

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