Adaptive Path Planning and Dynamic Obstacle Avoidance in Crowd Evacuation

Adaptive Path Planning and Dynamic Obstacle Avoidance in Crowd Evacuation

Problem Description
During emergency evacuations, the environment can change dynamically due to sudden events such as fires or collapses (e.g., blocked passages, smoke diffusion). It is necessary to design adaptive path planning algorithms for individuals or groups, enabling them to perceive obstacles in real time, dynamically adjust routes, and simultaneously avoid local congestion. This problem requires analyzing the core challenges of adaptive path planning and explaining how to integrate sensor data, environmental prediction models, and decision logic to achieve dynamic obstacle avoidance.

Core Challenges

  1. Environmental Uncertainty: The location and timing of obstacle appearances are unpredictable.
  2. Real-time Computational Efficiency: Path replanning must be completed within seconds.
  3. Individual and Group Coordination: Avoid creating new congestion due to local rerouting.

Detailed Solution Steps

Step 1: Basic Path Planning Methods (Static Environment)

  • Core Tool: Use the A* Algorithm or Dijkstra's Algorithm to calculate the shortest path.
    • The A* algorithm estimates the cost to the goal using a heuristic function (e.g., Manhattan distance), prioritizing the expansion of the most promising nodes.
    • Example: In a grid map, the algorithm avoids fixed obstacles (like walls) to find the shortest path to the exit.
  • Limitation: Static algorithms cannot handle dynamic obstacles (e.g., suddenly falling debris).

Step 2: Introducing Dynamic Obstacle Perception

  • Sensor Data Fusion:
    • Assume individuals carry sensors (e.g., cameras, infrared detectors) to detect in real-time whether passages ahead are clear.
    • Data fusion method: Integrate signals from multiple sensors into an Obstacle Probability Map (e.g., marking an area as impassable when smoke concentration exceeds a threshold).
  • Dynamic Map Updating:
    • Model the environment as a Grid Map, assigning a passability probability value to each grid cell.
    • When a sensor detects a new obstacle, immediately update the corresponding grid cell to "blocked" status.

Step 3: Real-Time Path Replanning Strategies

  • Local Replanning:
    • The global path is still generated by algorithms like A*, but once an obstacle is detected ahead, switch to the D* Lite Algorithm or RRT* (Rapidly-exploring Random Tree) for local adjustments.
    • Principle of D* Lite: It performs a reverse search from the goal to the start, caching path costs; when the environment changes, it only updates the costs of affected nodes, avoiding global recomputation.
  • Example:
    • Initial path is "A → B → C → Exit". At point B, a road blockage is discovered:
      1. Freeze the global path and initiate a local RRT* search near point B;
      2. Quickly generate a detour path "B → D → E → C";
      3. Merge into the new path "A → B → D → E → C → Exit".

Step 4: Group Coordination and Anti-Congestion Mechanisms

  • Collision Avoidance:
    • Employ the Velocity Obstacle Method: Predict the movement trajectories of surrounding individuals and adjust one's own speed and direction to avoid collisions.
    • For example, upon detecting an oncoming crowd, automatically decelerate and move sideways to an open area.
  • Distributed Decision-Making:
    • Individuals share local obstacle information through communication (e.g., "Smoke concentration rising in area X").
    • Design a Path Selection Strategy based on game theory: Individuals adjust their routes based on neighbors' decisions to avoid clustering (similar to Wardrop's equilibrium principle).

Step 5: Simulation Verification and Parameter Tuning

  • Simulation Tools: Use NetLogo or AnyLogic to build a multi-agent model, simulating smoke diffusion, individual movement, and communication processes.
  • Key Metrics:
    • Total evacuation time, number of path changes, average group speed.
  • Parameter Sensitivity Testing:
    • Adjust sensor detection range (e.g., 5 meters vs. 10 meters) and observe its impact on replanning frequency;
    • Test the effects of different communication delays (0.5 seconds vs. 2 seconds) on group coordination efficiency.

Conclusion
Adaptive path planning requires integrating a three-layer logic of real-time perception → local replanning → group coordination:

  1. Dynamically update the environmental map via sensors;
  2. Employ algorithms like D* Lite for efficient path adjustments;
  3. Utilize distributed decision-making to avoid local congestion.
    The future may further incorporate machine learning, allowing individuals to learn optimal avoidance strategies from historical evacuation data (e.g., reinforcement learning).