Less than a year after his spinal cord injury, Ian Burkhart was ready for whatever was next. A 2010 diving accident had severed his spine, and Burkhart lost sensation and movement below his bicep. But he had not given up on regaining some of those capabilities. He was working with doctors and physical therapists at The Ohio State University Wexner Medical Center to manage the effects of his injury. A few months after beginning treatment, he started asking his healthcare team about his options.

In this paper, we present a conceptually simple, easy-to-implement real-time search algorithm suitable for a priori partially known environments. Instead of performing a series of searches towards the goal, like most Real-Time Heuristic Search Algorithms do, our algorithm follows the arcs of a tree T rooted in the goal state that is built initially using the heuristic h. When the agent observes that an arc in the tree cannot be traversed in the actual environment, it removes such an arc from T and our algorithm carries out a reconnection search whose objective is to find a path between the current state and any node in T. The reconnection search need not be guided by $h$, since the search objective is not to encounter the goal. Furthermore, h need not be updated. We implemented versions of our algorithm that utilize various blind search algorithms for reconnection. We show experimentally that these implementations significantly outperform state-of-the-art real-time heuristic search algorithms for the task of pathfinding in grids. In grids, our algorithms, which do not incorporate any geometrical knowledge, naturally behaves similarly to a bug algorithm, moving around obstacles, and never returning to areas that have been visited in the past. In addition, we prove theoretical properties of the algorithm.