Planning and analysis of safety-optimal lunar sun-synchronous spatiotemporal routes

Recovery policies for safe exploration of lunar permanently shadowed regions by a solar-powered rover

“…Trajectories entering shadowed areas are avoided. The algorithms presented in [19] heuristically combine safety functions to find feasible sun-synchronous routes. In [20], a definition of safety for solar-powered rovers exploring PSRs at the lunar south poles is provided and utilized as part of a stochastic reachability problem.…”

A Deep Learning Approach to Lunar Rover Global Path Planning Using Environmental Constraints and the Rover Internal Resource Status

“…However, when addressing additional operational conditions, the concept of accelerated exploration does not necessarily equate to effective planning. A number of studies have been conducted on global path planning, employing different algorithms to address various environmental considerations: obstacle avoidance [3] (MDP); terramechanics [4,5] (Dijkstra), [6] (Reinforcement learning); sun-synchronous motion [7] (A*), [8] (Multi-speed spatiotemporal A*); terramechanics and power generation [9] (A*), [10] (Reinforcement learning); thermal condition, power generation, and terramechanics [11] (Dijkstra); uncertainty of the information [12] (RRT*); and hazard risk and collision avoidance [13] (A*), [14] (MDP), [15] (A*). These studies emphasize the importance of carefully selecting mathematical models and algorithms based on the specific purpose and constraints to be taken into account in the path planning process.…”

“…As a result, small rovers will cause immediate change in temperature and battery status in accordance with local lunar surface temperature as well as the sun position, which constantly changes over the course of the mission period. Therefore, it is essential to control when to move (timings of relocation), as well as where to move (path), to circumvent the variation in thermal and luminous conditions the rover will encounter [7][8][9][10][11].…”

“…There are fundamentally two possible ways to consider thermal and power constraints in the path planning process. One option is to use extrinsic conditions, such as lunar surface temperature and luminous environmental conditions, to determine immediate traversal/untraversal areas, such as those described in [7][8][9][10][11]. In this research, this scenario is called environment-based path search.…”

“…Essentially, these methods are not suitable when searching for an optimal path over an extended duration in time-variant environments. Otten et al [7], Hu et al [8], and Ji et al [9] incorporated power constraints by integrating the time-variant lunar surface luminous condition through the expansion of the graph in the temporal direction (in other words, generating a 3D binary array composed of stacked 2D maps for each time step). They addressed this using either the A* algorithm or the multi-speed spatiotemporal A* algorithm.…”