A Contract Based Approach to Collision Avoidance for UAVs

In this work, a contract-based reasoning approach is developed for obstacle avoidance in unmanned aerial vehicles (UAV's) under evolving subsystem performance. This approach is built on an assume-guarantee framework, where each subsystem (guidance, navigation, control and the environment) assumes a certain level of performance from other subsystems and in turn provides a guarantee of its own performance. The assume-guarantee construct then assures the performance of the overall system (in this case, safe obstacle avoidance). The implementation of the assume-guarantee framework is done through a set of contracts that are encoded into the guidance subsystem, in the form of a set of inequality constraints in the trajectory planner. The inequalities encode the relationships between subsystem performance and operational limits that ensure safe and robust operation as the performance of the control and navigation subsystems and environment evolve over time. The contract inequalities can be obtained analytically or numerically using an optimization based path planner and UAV simulation. The methodology is evaluated in the context of head-on obstacle avoidance, where the contracts are constructed in terms of (1) minimum obstacle detection range, (2) expected obstacle size, (3) maximum allowed cruise velocity, (4) maximum allowable thrust, roll and pitch angles, and (5) inner-loop tracking performance. Numerical and analytical generation of these contracts for this scenario is demonstrated. Finally, in-flight contract enforcement is illustrated for typical scenarios.