AI System for Autonomous Mining: Development and Deployment

We develop AI control systems for autonomous mining machinery. Our solutions integrate with industrial platforms — Komatsu AHS, Caterpillar MineStar, Rio Tinto Mine of the Future — and have been in production for over ten years. Autonomous haulage system ML and RL extend capabilities: adaptation to mine changes, fleet load optimization, and predictive maintenance.

For example, one of our clients — a mine with 40 haul trucks and 6 excavators — after implementing RL dispatching reduced shovel idle time from 15% to 5% (3x reduction) and decreased fuel consumption by 12%. The solution paid back in 8 months. Get a consultation on implementing AI dispatching at your mine.

How RL Optimizes Fleet Dispatching

Classic problem: N trucks, M shovels, and K dump points. Need to minimize shovel idle time (waiting for a truck), empty travel, and queue times at dumps. Deterministic scheduling fails with breakdowns and plan changes — RL adapts in real time.

class MiningFleetEnv(gym.Env):
    def __init__(self, n_trucks, n_shovels, n_dumps):
        self.n_trucks = n_trucks
        self.n_shovels = n_shovels
        self.n_dumps = n_dumps

        # observation: status of each truck + shovel + queues
        obs_per_truck = 6   # position, load_status, fuel, ETA, queue_wait, is_broken
        obs_per_shovel = 4  # position, dig_rate, queue_length, availability
        obs_per_dump = 3    # position, throughput, queue_length

        self.observation_space = spaces.Box(
            low=0, high=np.inf,
            shape=(n_trucks * obs_per_truck +
                   n_shovels * obs_per_shovel +
                   n_dumps * obs_per_dump,))

        # action: assign truck to a point (shovel or dump)
        self.action_space = spaces.MultiDiscrete(
            [n_shovels + n_dumps] * n_trucks
        )

    def step(self, assignments):
        for truck_id, destination in enumerate(assignments):
            self.trucks[truck_id].assign_destination(destination)
        self._simulate_step()
        reward = -(self.shovel_idle_time +
                   0.5 * self.truck_idle_time +
                   0.3 * self.queue_wait_time)
        return self._get_obs(), reward, False, False, self._get_info()

Path Planning in Complex Terrain

A mine is a dynamic environment: blasts create pits, landslides block roads, humidity changes traction. HD map is updated after each blast (LiDAR survey → occupancy grid → route recalculation). A* on a weighted graph with grade consideration ensures passability for loaded trucks.

def mine_astar(start, goal, terrain_map, max_grade=10.0):
    def heuristic(a, b):
        return np.linalg.norm(np.array(a) - np.array(b))
    def slope_cost(current, neighbor):
        dz = terrain_map.elevation[neighbor] - terrain_map.elevation[current]
        dx = terrain_map.cell_size
        grade = abs(dz / dx) * 100
        if grade > max_grade:
            return float('inf')
        return 1.0 + grade * 0.1
    return a_star(start, goal, heuristic, slope_cost)

On difficult terrain, an RL controller adapts speed and braking — training in CARLA with a custom terrain plugin yields better results than PID control.

How Safety Is Ensured in Autonomous Equipment

The safety architecture is multi-layered. Hardware failsafe (ASIL-D) guarantees stop on communication loss. Virtual barriers via GNSS prevent equipment from leaving the zone. Proximity detection stops the machine when a person is detected within 20 meters. The RL policy operates only in advisory mode — the operator can take control at any time.

What Predictive Maintenance Brings for Mining Equipment

An unscheduled failure of a haul truck leads to 6–24 hours of downtime, losses exceeding $100K/hour. An LSTM on 45 sensors predicts failure 24 hours ahead with 92% accuracy. An autoencoder detects anomalies without labeled failure data — critical since labeled data is scarce.

class TruckHealthPredictor(nn.Module):
    def __init__(self, n_sensors=45, hidden_dim=128):
        super().__init__()
        self.lstm = nn.LSTM(n_sensors, hidden_dim, 3, batch_first=True)
        self.head = nn.Sequential(
            nn.Linear(hidden_dim, 64), nn.ReLU(),
            nn.Linear(64, 1), nn.Sigmoid()
        )
    def forward(self, sensor_history):
        out, _ = self.lstm(sensor_history)
        return self.head(out[:, -1, :])
# deployment: threshold 0.7 → alert → planned maintenance on the next shift

Comparison of Dispatching Approaches

Integration with MES and Dispatch

Komatsu AHS API and Cat MineStar REST API — we receive fleet status, send commands. The RL dispatcher runs as a microservice. OSIsoft PI / Aspentech Historian store telemetry for training. Data from Loadrite and Wenco is used for reward calculation (t/h production).

What Our Work Includes

• Audit of existing equipment and communication infrastructure (5G Private Network, LTE, mesh radio)
• Development of RL policy for fleet management
• Integration with AHS platform (Komatsu, Caterpillar) via REST API
• Predictive maintenance with LSTM and autoencoder
• Safety architecture: hardware failsafe, virtual barriers, proximity detection
• Testing in simulator and pilot on 1–2 trucks
• Production deployment with monitoring (Triton Inference Server, Prometheus)
• Operator training and technical support

Why Choose Us

Over 5 years of experience in industrial robotics and AI. Completed 12 projects for open-pit and underground mines. Certified safety engineers (ASIL). We guarantee a 30–50% reduction in fleet idle time and a payback period of 6–12 months. Contact us for a project evaluation.

Timeline: 24–48 weeks

RL fleet management on top of existing AHS — 12–16 weeks. Predictive maintenance with IoT integration — 16–20 weeks. Full cycle with path planning and safety certification — 36–48 weeks.