SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning

Our collision avoidance system SAFER takes input from Mobile robots are slowly but surely taking a place in our lidar and ultrasonic sensor scans, wheel odometry for robot everyday lives and work environments with various applications: state, and the upstream control commands. We fuse the vacuum cleaning, video recording, companionship, lidar and ultrasonic sensor scans to detect a diverse set security, tele-presence, etc. Whether they are autonomous of obstacles, including transparent glass, reflective surfaces, agents or controlled by human operators, collision avoidance furniture, humans, etc. We design a reward function for is key for mobile agents to operate safely, and effectively our RL agent with two terms. The first term encourages in the real world. There are numerous approaches to collision the reduction of AEB activation. The second term improves avoidance, including search-based planning methods, collision avoidance metrics through a cost function, such as trajectory optimization, learning-based methods, and emergency average speed, distance to obstacles, and matching human intervention systems.