Obstacle Avoidance and Surrounding Capture Strategies for Underwater Robots

Research on obstacle avoidance and surrounding capture strategies for underwater robots has gained significant attention in recent years, particularly in applications such as marine exploration, military reconnaissance, and aquaculture. Obstacle avoidance represents a core capability for autonomous navigation of underwater robots, while surrounding capture strategies enable cooperative task completion in complex marine environments.

Obstacle avoidance technology typically relies on multi-sensor fusion systems incorporating sonar, lidar, and vision systems. The robot must identify obstacles through real-time data processing and dynamically adjust its trajectory using path planning algorithms such as A*, Rapidly-exploring Random Trees (RRT), or artificial potential field methods. Implementation typically involves creating cost maps from sensor data and utilizing heuristic search algorithms with collision detection modules. Considering underwater environmental factors like currents and turbidity, the obstacle avoidance algorithms must demonstrate strong robustness through adaptive thresholding and sensor redundancy mechanisms.

In surrounding capture missions, multiple underwater robots typically employ swarm intelligence strategies such as ant colony optimization or fish school algorithms for cooperative mechanisms. Through communication networks, robots share target positions and environmental information to form encirclements and gradually narrow the capture area. This strategy requires not only efficient communication protocols but also considerations for power limitations and underwater signal attenuation. Code implementation often involves distributed consensus algorithms and neighbor discovery protocols using acoustic modems with packet loss compensation.

Future research directions may include applying deep reinforcement learning for underwater robot obstacle avoidance and capture tasks, along with higher-precision environmental modeling techniques. This would enhance the autonomy and adaptability of robots in complex underwater missions through neural network-based decision systems and 3D hydrodynamic simulation environments.