CollisionAvoid

CollisionAvoid is a software framework and methodology for real-time collision avoidance in autonomous systems. It combines sensor data from cameras, radars, lidars, and proprioceptive sensors with perception to identify obstacles, predict their future positions, and generate safe trajectories. The system is designed to operate in robotics, drones, and autonomous vehicles, handling both single-agent and multi-agent scenarios.

Key components of CollisionAvoid include perception, prediction, and planning/control modules. Perception builds a representation of the environment, detection identifies potential hazards, and prediction estimates how obstacles may move. Planning then computes a collision-free trajectory that respects vehicle dynamics, kinematic constraints, and safety margins. Methods commonly used in implementations range from velocity-based planning and time-to-collision metrics to optimization-based planners. In practice, CollisionAvoid may implement approaches such as velocity obstacles, reciprocal velocity obstacles, or model predictive control, often augmented with conservative safety buffers and emergency stop mechanisms.

Features typically associated with CollisionAvoid encompass real-time operation, multi-agent coordination, dynamic obstacle handling, and seamless integration with existing control architectures. Robustness and fault tolerance are emphasized, though performance depends on sensor quality, computational resources, and accurate modeling of the environment. Limitations include challenges in very dense environments, sensor occlusions, and prediction errors, which can reduce effectiveness or force more conservative behavior.

Applications span autonomous vehicles, delivery drones, warehouse and service robots, and other autonomous systems requiring reliable motion safety. See also collision avoidance, motion planning, path planning, sensor fusion, autonomous navigation, and multi-agent systems.