Rip currents cause over 100 drowning deaths and more than 30,000 rescues annually in the United States, posing a severe threat to beach safety worldwide. However, most existing detection methods are reactive, identifying rip currents only after they form, leaving limited time for intervention. We propose RipAlert, a future-frame-aware framework that forecasts near-future coastal dynamics and proactively identifies rip current risks. We design a region-sensitive optical flow prediction method with a novel entropy-based object detector to capture early-stage reverse-flow anomalies. Unlike static-image approaches, RipAlert leverages temporal motion patterns to detect rip currents up to 5 seconds before they visibly form. To support real-world deployment, we design a lightweight mobile application and release a curated dataset with over 2,000 annotated images. Experiments on the RipVIS benchmark show that our approach achieves state-of-the-art performance. The system has been deployed at high-risk beaches in China, issuing successful early warnings over real-world events. Our work advances AI-driven coastal safety and contributes to SDG 3 (Good Health and Well-Being) and SDG 13 (Climate Action).

This paper presents a very simple, yet very effective method for combining measurements from a gyro with measurements from wheel encoders (odometry). The method, called gyrodometry, has been developed based on a careful study of the physical interaction between the ground and the vehicle. We present experimental evidence that non-systematic odometry error sources (such as bumps) impact the vehicle only during very short periods; typically a fraction of a second for each encounter. During these short instances the readings from the gyro and from odometry differ significantly, while in the absence of large non-systematic errors the readings are very similar. Gyrodometry makes use of this observation by using odometry data only-most of the time, while substituting gyro data only during those brief instances during which the gyro and odometry data differ substantially. In this way the ill-effects of gyro drift are almost completely eliminated, and our method can thus make use of inexpensive gyros with large drift rates. Experimental data is presented that demonstrates the effectiveness of this approach.

Odometry is the most widely used method for determining the momentary position of a mobile robot. This paper introduces practical methods for measuring and reducing odometry errors that are caused by the two dominant error sources in differential-drive mobile robots: 1) uncertainty about the effective wheelbase; and 2) unequal wheel diameters. These errors stay almost constant over prolonged periods of time. Performing an occasional calibration as proposed here will increase the odometric accuracy of the robot and reduce operation cost because an accurate mobile robot requires fewer absolute positioning updates. Many manufacturers or end-users calibrate their robots, usually in a time-consuming and nonsystematic trial and error approach. By contrast, the method described in this paper is systematic, provides near-optimal results, and it can be performed easily and without complicated equipment. Experimental results are presented that show a consistent improvement of at least one order of magnitude in odometric accuracy (with respect to systematic errors) for a mobile robot calibrated with our method.

This paper describes a practical method for reducing odometry errors caused by kinematic imperfections of a mobile robot. These errors, here referred to as "systematic" errors, stay almost constant over a prolonged period of time. Performing an occasional calibration as described here will increase the robot's odometric accuracy and reduce operation cost because an accurate mobile robot requires fewer absolute positioning updates. Many manufacturers or end-users calibrate their robots-usually in a time-consuming and non-systematic trial and error approach. By contrast the authors' method is systematic, provides near-optimal results, and can be performed easily and without complicated equipment. Experimental results are presented that show a consistent improvement of at least one order of magnitude in odometric accuracy (with respect to systematic errors) for a mobile robot calibrated with the procedure described in this paper.

Autonomous robot navigation requires the solution of several problems like obstacle avoidance and road following. Traditionally, the problem of obstacle avoidance has been solved by using range information acquired through some form of range measurements like laser range cameras or vision based range estimation. This paper presents a novel approach for obstacle avoidance which does not require true range measurement. Obstacle information is acquired using a single color camera. Detected obstacles are projected into 3D grid derived from the camera geometry and a locally flat earth model for the terrain. A weighted polar histogram technique is used to generate the vehicle steering angle based on the current grid information.>

This paper describes the design and implementation of a model-reference adaptive motion controller for a differential-drive mobile robot. This controller uses absolute position information to modify control parameters in real time to compensate for motion errors. Robot motion errors are classified into internal and external errors. A cross-coupling control method is used to compensate for the internal errors that can be detected by wheel encoders. The adaptive controller provides compensation for external errors. The adaptive controller is analyzed, and its stability and convergence are discussed. Experiments are conducted to evaluate the control system and the results show significant improvements over conventional controllers.>

A method that determines the absolute position of a mobile system with a hybrid optoelectronic processor has been developed. Position estimates are based on an analysis of circular landmarks that are detected by a TV camera attached to the mobile system. The difference between the known shape of the landmark and its image provides the information needed to determine the absolute position of the mobile system. For robust operation, the parameters of the landmark image are extracted at high speeds using an optical processor that performs an optical Hough transform. The coordinates of the mobile system are computed from these parameters in a digital coprocessor using fast algorithms. Different sources of position estimation errors have also been analyzed, and consequent algorithms for improving the navigation performance of the mobile system have been developed and evaluated by both computer simulation and experiments.>

A mobile system with a hybrid opto-electronic processor has been studied. The position estimation is based on analysis of landmarks being detected by a TV-camera attached to the mobile system. The difference between the known real shape of the landmark and its image provides the information necessary for determining the relative position of the mobile system to the landmark. The parameters of the landmark image are extracted at high speed using an optical processor that performs an optical Hough transform, while the coordinates of the mobile system are computed from these parameters in a digital coprocessor using fast and simple algorithms. Different sources of errors have been analyzed, and algorithms to improve the performance of the mobile system have been developed and evaluated by computer simulation.>