In this paper, we propose a heuristic-based fast motion planning framework which can be readily incorporated by the on-board path planner of Unmanned Aerial Vehicles (UAVs) to generate safe and efficient trajectories while traversing through challenging environments cluttered with obstacles. The proposed planning technique is effective for the scenarios where the exact obstacle locations need to be detected during flight and the obstacle detection range is limited by degraded environmental conditions like fog. Unlike many kinematic based planning strategies, the generated planned trajectories can be tracked effectively as they preserve the dynamics of the UAV. The planning problem is graphically represented by discretizing input and state spaces to facilitate usage of discrete search algorithms. We also propose a heuristic calculation strategy based on dynamics relaxation to accurately encode the obstacle. The Bellman optimality condition is used to modify the heuristic to facilitate faster search. This faster planning contributes to requiring a reduced minimum obstacle detection range for receding horizon planning. The proposed algorithm has been compared against an off-the-shelf nonlinear program solver and the proposed method produced superior planning times and feasible trajectories avoiding collisions. Further, we analyzed the sub-optimality of the planned trajectories and the minimum obstacle detection range required for the receding horizon planning framework.
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.
In this paper, an assume-guarantee reasoning approach is developed for obstacle avoidance in unmanned aerial vehicles (UAVs) in the presence of multiple obstacles in an obstacle field. This construct assumes certain properties of the environment and the vehicle to guarantee the safety and performance of the UAV (in this case, executing safe collision avoidance trajectories). In the presence of a single obstacle, the assumptions on the environment and the vehicle parameters are constructed such that the UAV can plan a safe trajectory once the obstacle is detected. The approach to guaranteeing safety in the presence of multiple obstacles requires enforcing additional assumptions which is done by constructing a region of influence (RoI) around each obstacle, whose size depends on the environment and the vehicle parameters. The safe combinations of these parameters (codified as contracts) are developed such that the RoIs in the obstacle field do not intersect. The aforementioned approach is then used to decompose the general multiple obstacle avoidance problem into a sequential single-obstacle avoidance problem by constructing an induction-based algorithm framework. The proposed methodology is validated by an illustrative example with minimum obstacle detection range, maximum allowed cruise velocity, maximum allowable agility as vehicle properties; and maximum obstacle size and minimum obstacle separation as environmental properties. Contract generation for specific scenarios and implementation of sequential avoidance on a 6-DoF quadcopter simulation are demonstrated. Finally, the effect of tracking error on the contract-based framework is discussed, along with a mechanism to incorporate this source of uncertainty into the contract.
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