The Importance of Adaptive Decision-Making for Autonomous Long-Range Planetary Surface Mobility

Long-distance driving is an important component of planetary surface exploration. Unforeseen events often require human operators to adjust mobility plans, but this approach does not scale and will be insufficient for future missions. Interest in self-reliant rovers is increasing, however the research community has not yet given significant attention to autonomous, adaptive decision-making. In this paper, we look back at specific planetary mobility operations where human-guided adaptive planning played an important role in mission safety and productivity. Inspired by the abilities of human experts, we identify shortcomings of existing autonomous mobility algorithms for robots operating in off-road environments like planetary surfaces. We advocate for adaptive decision-making capabilities such as unassisted learning from past experiences and more reliance on stochastic world models. The aim of this work is to highlight promising research avenues to enhance ground planning tools and, ultimately, long-range autonomy algorithms on board planetary rovers.

Safe Mission-Level Path Planning for Exploration of Lunar Shadowed Regions by a Solar-Powered Rover

Exploration of the lunar south pole with a solar-powered rover is challenging due to the highly dynamic solar illumination conditions and the presence of permanently shadowed regions (PSRs). In turn, careful planning in space and time is essential. Mission-level path planning is a global, spatiotemporal paradigm that addresses this challenge, taking into account rover resources and mission requirements. However, existing approaches do not proactively account for random disturbances, such as recurring faults, that may temporarily delay rover traverse progress. In this paper, we formulate a chance-constrained mission-level planning problem for the exploration of PSRs by a solar-powered rover affected by random faults. The objective is to find a policy that visits as many waypoints of scientific interest as possible while respecting an upper bound on the probability of mission failure. Our approach assumes that faults occur randomly, but at a known, constant average rate. Each fault is resolved within a fixed time, simulating the recovery period of an autonomous system or the time required for a team of human operators to intervene. Unlike solutions based upon dynamic programming alone, our method breaks the chance-constrained optimization problem into smaller offline and online subtasks to make the problem computationally tractable. Specifically, our solution combines existing mission-level path planning techniques with a stochastic reachability analysis component. We find mission plans that remain within reach of safety throughout large state spaces. To empirically validate our algorithm, we simulate mission scenarios using orbital terrain and illumination maps of Cabeus Crater. Results from simulations of multi-day, long-range drives in the LCROSS impact region are also presented.

Recovery Policies for Safe Exploration of Lunar Permanently Shadowed Regions by a Solar-Powered Rover

The success of a multi-kilometre drive by a solar-powered rover at the lunar south pole depends upon careful planning in space and time due to highly dynamic solar illumination conditions. An additional challenge is that real-world robots may be subject to random faults that can temporarily delay long-range traverses. The majority of existing global spatiotemporal planners assume a deterministic rover-environment model and do not account for random faults. In this paper, we consider a random fault profile with a known, average spatial fault rate. We introduce a methodology to compute recovery policies that maximize the probability of survival of a solar-powered rover from different start states. A recovery policy defines a set of recourse actions to reach a location with sufficient battery energy remaining, given the local solar illumination conditions. We solve a stochastic reach-avoid problem using dynamic programming to find such optimal recovery policies. Our focus, in part, is on the implications of state space discretization, which is often required in practical implementations. We propose a modified dynamic programming algorithm that conservatively accounts for approximation errors. To demonstrate the benefits of our approach, we compare against existing methods in scenarios where a solar-powered rover seeks to safely exit from permanently shadowed regions in the Cabeus area at the lunar south pole. We also highlight the relevance of our methodology for mission formulation and trade safety analysis by empirically comparing different rover mobility models in simulated recovery drives from the LCROSS crash region.

Overcoming the Challenges of Solar Rover Autonomy: Enabling Long-Duration Planetary Navigation

The successes of previous and current Mars rovers have encouraged space agencies worldwide to pursue additional planetary exploration missions with more ambitious navigation goals. For example, NASA's planned Mars Sample Return mission will be a multi-year undertaking that will require a solar-powered rover to drive over 150 metres per sol for approximately three months. This paper reviews the mobility planning framework used by current rovers and surveys the major challenges involved in continuous long-distance navigation on the Red Planet. It also discusses recent work related to environment-aware and energy-aware navigation, and provides a perspective on how such work may eventually allow a solar-powered rover to achieve autonomous long-distance navigation on Mars.