  This paper presents a sampling-based motion planning algorithm for real-time
and propellant-optimized autonomous spacecraft trajectory generation in
near-circular orbits. Specifically, this paper leverages recent algorithmic
advances in the field of robot motion planning to the problem of
impulsively-actuated, propellant-optimized rendezvous and proximity operations
under the Clohessy-Wiltshire-Hill (CWH) dynamics model. The approach calls upon
a modified version of the Fast Marching Tree (FMT*) algorithm to grow a set of
feasible trajectories over a deterministic, low-dispersion set of sample points
covering the free state space. To enforce safety, the tree is only grown over
the subset of actively-safe samples, from which there exists a feasible
one-burn collision avoidance maneuver that can safely circularize the
spacecraft orbit along its coasting arc under a given set of potential thruster
failures. Key features of the proposed algorithm include: (i) theoretical
guarantees in terms of trajectory safety and performance, (ii) amenability to
real-time implementation, and (iii) generality, in the sense that a large class
of constraints can be handled directly. As a result, the proposed algorithm
offers the potential for widespread application, ranging from on-orbit
satellite servicing to orbital debris removal and autonomous inspection
missions.
