This paper develops an obstacle avoidance strategy for inspection drones equipped with simple range finding sensors, such as radar or sonar. The obstacle avoidance strategy uses scenario-based model predictive control where the predicted outcomes of a set of possible control actions are evaluated. The action with the best predicted outcome amongst the safe options is chosen. The resulting behaviour is deemed safe if the probability of collision at each time-step in the prediction is lower than a given maximal accepted probability. The probability of collision is calculated by combining a probability density function of the position of the drone with an obstacle probability map generated by the range finding sensors. This constraint is checked at each step over the prediction horizon thus ensuring that the control action will give rise to safe behaviour. The algorithm is implemented in a 2D case and tested with a simple model for the range finding sensors. Simulations show that the drone is able to avoid obstacles and that the drone will change speed or take detours to avoid flying in potentially dangerous areas to mitigate risk. The algorithm is designed for avoiding obstacles along a pre-planned path. The pre-planned path is assumed to be generally good, but might be unsafe or not take some unknown obstacles into account. If the pre-planned path goes through a larger convex area or does not take a large obstacle into account, then this algorithm might not find a way around the obstacle and the drone will stop at a safe distance. The path of the drone must then be re-planned taking these obstacles into account. The resulting obstacle avoidance strategy guarantees safe behaviour which enables higher level controllers or human planners to plan a path based on lacking obstacle information without taking the safety of the drone into consideration.

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The open wording of the traffic rules of the sea, COLREGS, and the existence of unwritten rules, make it essential for an autonomous ship to understand the intentions of meeting traffic. This article uses a dynamic Bayesian network (DBN) to model and infer the intentions of other ships based on their observed real-time behavior. Multiple intention nodes are included to describe the different ways a ship can interpret and conflict with the behavioral rules outlined in CORLEGS. The prior distributions of the intention nodes are adapted to the current situation based on observable characteristics such as location and relative ship size. When a new observation is made, the probability distributions of the intention variables are updated by excluding all combinations of intention states that conflict with the observed behavior. This way of modeling makes the intention probabilities independent of how often observations are made. The resulting model is able to identify situations that are prone to cause misunderstandings and infer the state of multiple intention variables that describe the behavior. Different collision avoidance algorithms can use the resulting intention information to better know if, when, and how to act.

<p>This article develops and experimentally tests a supervisory risk controller used to increase the safety of drone operations. Its task is to monitor the state of the drone and environment and to use this information to automatically change safety-critical parameters in real-time during operation. </p> <p>A case study of a tethered industrial inspection drone is considered. A system theoretic process analysis (STPA) is performed to identify how the system can fail. A Dynamic Decision Network (DDN), used as an online risk model, is built based on the results of the STPA. An optimization approach is used to choose an optimal parameter configuration that ensures an acceptable risk level.</p> <p>Through experimental tests, it is demonstrated how the supervisory risk controller is able to identify the state of the drone and the environment by combining information from multiple measurements over time and how it chooses values for the maximum speed, safety distance, and maximum vertical acceleration that produces an acceptable risk level. The parameters are updated during flight based on the output from the supervisory risk controller. When no parameter set can ensure an acceptable risk level then a recommendation of aborting the mission is sent to the human operator.</p> <p>Video of the experimental results can be found at https://youtu.be/RKhG9bguRJY</p>

<p>This article develops and experimentally tests a supervisory risk controller used to increase the safety of drone operations. Its task is to monitor the state of the drone and environment and to use this information to automatically change safety-critical parameters in real-time during operation. </p> <p>A case study of a tethered industrial inspection drone is considered. A system theoretic process analysis (STPA) is performed to identify how the system can fail. A Dynamic Decision Network (DDN), used as an online risk model, is built based on the results of the STPA. An optimization approach is used to choose an optimal parameter configuration that ensures an acceptable risk level.</p> <p>Through experimental tests, it is demonstrated how the supervisory risk controller is able to identify the state of the drone and the environment by combining information from multiple measurements over time and how it chooses values for the maximum speed, safety distance, and maximum vertical acceleration that produces an acceptable risk level. The parameters are updated during flight based on the output from the supervisory risk controller. When no parameter set can ensure an acceptable risk level then a recommendation of aborting the mission is sent to the human operator.</p> <p>Video of the experimental results can be found at https://youtu.be/RKhG9bguRJY</p>