Model-based testing of a real-time adaptive motion planning system

Abstract

To enable effective and safe operations of autonomous robots in environments with unknowns and unpredictability, a key practical problem is how to test the functionality and assess the performance of real-time motion planning systems. This is a challenge because the underlying algorithms are real-time, sensing-based, and often non-deterministic. These systems’ performance depends on task environments, which can vary in countless ways. Existing testing techniques are designed heavily based on testers’ experience and hardly provide a good coverage of possible test scenarios. This paper introduces a systematic model-based testing (MBT) approach to evaluate the functionality and performance of a real-time adaptive motion planning (RAMP) system. The MBT approach uses the formal communicating extended finite state machine model to model RAMP’s concurrent components and leverage graph traversal algorithms to systematically generate behavioral test cases. First, component integration is considered by modeling the RAMP components and their interactions. Next, system-level testing is considered by modeling mobile obstacles of unpredictable motion behavior. The behavior models are leveraged to generate Abstract Behavioral Test Cases, which are transformed by test data into executable test cases. The test results demonstrate the effectiveness of applying the systematic MBT approach to the evaluation of real-time robotic systems.