Abstract— Visual occlusion can cause object detection and classification systems to fail due to swift movement or a soiled, moist or dusty environment. Hence, this asks for a ’blind’ collision detection and classification method. This paper presents a novel blind collision detection and classification scheme for a
heavyweight, low-speed, wheeled mobile robot. By recognizing the target signal pattern in phase current data, the robot can detect whether the wheels are blocked caused by overload (G1). After this, using acceleration and phase current data, the robot will be able to differentiate between a collision against a wall
or a collision with an object between the wall, or no collision of interest (G2). Additionally, an algorithm based on the pitch angle can detect if the robot slides on top of a soft object (G3). Three different algorithms are developed that address these goals. Finally, the developed algorithms are validated in
dynamic and soiled environment (G4). The first two goals are reached by training machine learning classifiers that identify the signal pattern of its target event and place uneventful data in a separate class (open-world classification). During training the classifiers Random Forest, Multi-layered Perceptron, Decision
Tree and Logistic Regression are compared, and the two best perfoming classifiers are subsequently tested for a time-series open-world classification task. The third goal is reached with the development of a heuristic algorithm based on the running mean of the pitch angle.
Experiments were performed in a controlled environment, creating collisions with varying floor conditions, robot weight and collision objects. The best performing algorithm for blind collision overload detection has achieved 98.6% detection accuracy. The object classification algorithm can differentiate
between two types of soft objects, a wall or no event with an accuracy of 93.3%. The algorithm based on the deviating pitch angle can detect events with 100% detection rate and 0% false positive rate. Detailed implementation schemes are provided for real-time implementation, illustrating a robust framework in soiled environments. The proposed solutions can be used to improve collision detection on blind mobile robots, as well as mapping the environment using the object classification model.