Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots remain an open challenge due to the proximity of the sensor payload to the ground coupled with a limited field of view. To address this issue, we propose Blind-motion with Intermittently Scheduled Scans (BLISS) which combines proprioception-only mobility with intermittent scans to be resilient against both localization failures and collision risks. BLISS is formulated as an integrated task and motion planning (TAMP) problem that leads to a chance-constrained hybrid partially observable Markov decision process (CC-HPOMDP), known to be computationally intractable due to the curse of history. Our novelty lies in reformulating CC-HPOMDP as a tractable, convex mixed integer linear program. This allows us to solve BLISS-TAMP significantly faster and jointly derive optimal task-motion plans. Simulations and hardware experiments on the EELS snake robot show our method achieves over an order of magnitude computational improvement compared to state-of-the-art POMDP planners and$>50 \%$better navigation time optimality versus classical two-stage planners.

The Exobiology Extant Life Surveyor (EELS) is a snake-like mobile instruments platform under development at Jet Propulsion Laboratory (JPL) for a mission concept to find evidence of life on Saturn's sixth largest moon, Enceladus. To conduct a life surveying mission there, the EELS platform must first traverse an unknown icy surface terrain before undertaking a controlled descent into a cryovolcanic vent. The remoteness of Enceladus and the icy nature of its terrain demands a level of autonomy in navigation significantly higher than previous rover missions. The perception system onboard EELS must be highly resilient to perceptually-degraded environments such as flat, open ice fields, icy plumes, and repeating geometries in vents. EELS' perception system is implemented as a multi-sensor Simultaneous Localisation And Mapping (SLAM) solution called SERPENT. State Estimation through Robust Perception in Extreme and Novel Terrains (SERPENT) estimates the robot trajectory and maintains a map database, from which dense global or local maps can be obtained on demand for downstream planning algorithms. This system opts to incorporate measurements from many sensor modalities (laser scans, images, IMU, altimeter, etc.), solving the SLAM problem through joint optimisation, and thus requires that the contribution of each sensor be balanced through careful modelling of their uncertainties. With a specific focus on Light Detection And Ranging (LiDAR) in this context, this paper proposes a principled approach to model the covariances of point-to-plane Iterative Closest Point (ICP). It performs a rigorous comparative analysis of new and existing covariance models, and is the first time some of these have been tested within a complete SLAM pipeline. These models are evaluated on perceptually challenging datasets collected in glacial environments by the EELS sensor suite (see Figures 1, 2). SERPENT is open-sourced at https://github.com/jpl-eels/serpent.

The discovery of ocean worlds such as Enceladus, Titan, and Europa motivates the development of versatile autonomous mobility systems to enable the next era of space exploration where there is large uncertainty in terrain specifications due to a lack of prior surface reconnaissance missions. To explore these environments, we propose Exobiology Extant Life Surveyor (EELS): the first large-scale (4 lm long with 400 Nm peak torque) snake robot. The large scale is achieved by using a screw-based active skin mechanism to decouple motion and shape control. Autonomous mobility for such a system remains an open problem due to its many Degrees of Freedom (DoFs), complex terrain interactions, and intermittent localization failures in GPS-denied perceptually degraded environments due to the presence of fog, dust, featureless terrains, etc. We propose NEO, an autonomy architecture that scales to large DoFs to generate a versatile set of gaits to achieve mobility in unknown extreme environments. We also discuss the resilience capabilities of NEO that achieves closed-loop tracking performance by leveraging exteroception when available but can also operate with proprioception only, leading to resiliency against localization failures via graceful degradation in performance rather than unsafe behaviors. A quantitative hardware evaluation of exteroceptive leader-follower gait is performed indoors on synthetic ice along with qualitative results of field deployment of the proprioceptive leader-follower and sidewinding gaits in extreme environments of icy and sandy terrains with mobility-stressing elements such as trenches, undulations, and steep slopes (up to 35 degrees). We present a set of lessons learned from field deployments with a summary of challenges and open research problems. Video: www.rohanthakker.in/eels-neo-autonomy.html

Many areas of scientific interest in planetary exploration, such as lunar pits, icy-moon crevasses, and Martian craters, are inaccessible to current wheeled rovers. Rappelling rovers can safely traverse these steep surfaces, but require techniques to navigate their complex terrain. This dynamic navigation is inherently time-critical and communication constraints (e.g. delays and small communication windows) will require planetary systems to have some autonomy.Autonomous navigation for Martian rovers is well studied on moderately sloped and locally planar surfaces, but these methods do not readily transfer to tethered systems in non-planar 3D environments. Rappelling rovers in these situations have additional challenges, including terrain-tether interaction and its effects on rover stability, path planning and control.This paper presents novel traversability analysis and path planning algorithms for rappelling rovers operating on steep terrains that account for terrain-tether interaction and the unique stability and reachability constraints of a rapelling system. The system is evaluated with a series of simulations and an analogue mission. In simulation, the planner was shown to reliably find safe paths down a 55 degree slope when a stable tether-terrain configuration exists and never recommended an unsafe path when one did not. In a planetary analogue mission, elements of the system were used to autonomously navigate Axel, a JPL rappelling rover, down a 30 degree slope with 95% autonomy by distance travelled over 46 meters.

The ocean world Europa is a prime target for exploration given its potential habitability [1]. We propose a mobile robotic system that is capable of autonomously traversing tens of meters to visit multiple sites of interest on a Europan analogue surface. Due to the topology of Europan terrain being largely unknown, it is desired that this mobility system traverse a large variety of terrain types. The mobility system should also be capable of crossing unstructured terrain in an autonomous manner given the communications limitations between Earth and Europa.A wheel-on-limb robotic rover is presented that may actively conform to terrain features up to 1.5 wheel diameters tall while driving. The robot uses a sampling-based motion planner to generate paths that leverage its unique locomotive capabilities. The planner assesses terrain hazards and wheel workspace limits as obstacles. It may also select a mobility mode based on predicted energy usage and the need for limb articulation on the terrain being traversed. This autonomous mobility was evaluated on chaotic salt-evaporite terrain found in Death Valley, CA, an analogue to the Europan surface. Over the course of 38 trials, the rover autonomously traversed 435m of extreme terrain while maintaining a rate of 0.64 traverse ending failures for every 10m driven.

Our work builds upon Visual Teach & Repeat 2 (VT&R2): a vision-in-the-loop autonomous navigation system that enables the rapid construction of route networks, safely built through operator-controlled driving. Added routes can be followed autonomously using visual localization. To enable long-term operation that is robust to appearance change, its Multi-Experience Localization (MEL) leverages many previously driven experiences when localizing to the manually taught network. While this multi-experience method is effective across appearance change, the computation becomes intractable as the number of experiences grows into the tens and hundreds. This paper introduces an algorithm that prioritizes experiences most relevant to live operation, limiting the number of experiences required for localization. The proposed algorithm uses a visual Bag-of-Words description of the live view to select relevant experiences based on what the vehicle is seeing right now, without having to factor in all possible environmental influences on scene appearance. This system runs in the loop, in real time, does not require bootstrapping, can be applied to any pointfeature MEL paradigm, and eliminates the need for visual training using an online, local visual vocabulary. By picking a subset of visually similar experiences to the live view, we demonstrate safe, vision-in-the-loop route following over a 31 hour period, despite appearance as different as night and day.

Vision-based, route-following algorithms enable autonomous robots to repeat manually taught paths over long distances using inexpensive vision sensors. However, these methods struggle with long-term, outdoor operation due to the challenges of environmental appearance change caused by lighting, weather, and seasons. While techniques exist to address appearance change by using multiple experiences over different environmental conditions, they either provide topological-only localization, require several manually taught experiences in different conditions, or require extensive offline mapping to produce metric localization. For real-world use, we would like to localize metrically to a single manually taught route and gather additional visual experiences during autonomous operations. Accordingly, we propose a novel multi-experience localization (MEL) algorithm developed specifically for route-following applications; it provides continuous, six-degree-of-freedom (6DoF) localization with relative uncertainty to a privileged (manually taught) path using several experiences simultaneously. We validate our algorithm through two experiments: i) an offline performance analysis on a 9km subset of a challenging 27km route-traversal dataset and ii) an online field trial where we demonstrate autonomy on a small 250m loop over the course of a sunny day. Both exhibit significant appearance change due to lighting variation. Through these experiments we show that safe localization can be achieved by bridging the appearance gap.

Stereo Visual Teach & Repeat (VT&R) is a system for long-range, autonomous route following in unstructured 3D environments. As this system relies on a passive sensor to localize, it is highly susceptible to changes in lighting conditions. Recent work in the optics community has provided a method to transform images collected from a three-channel passive sensor into color-constant images that are resistant to changes in outdoor lighting conditions. This paper presents a lighting-resistant VT&R system that uses experimentally trained color-constant images to autonomously navigate difficult outdoor terrain despite changes in lighting. We show through an extensive field trial that our algorithm is capable of autonomously following a 1km outdoor route spanning sandy/rocky terrain, grassland, and wooded areas. Using a single visual map created at midday, the route was autonomously repeated 26 times over a period of four days, from sunrise to sunset with an autonomy rate (by distance) of over 99.9%. These experiments show that a simple image transformation can extend the operation of VT&R from a few hours to multiple days.