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.

BACKGROUND: Fluorescent Timer proteins, which display time-dependent changes in their emission spectra, are invaluable for analyzing the temporal dynamics of cellular events at the single-cell level. We previously developed the Timer-of-cell-kinetics-and-activity (Tocky) tools, utilizing a specific Timer protein, Fast-FT, to monitor temporal changes in cellular activities. Despite their potential, the analysis of Timer fluorescence in flow cytometry is frequently compromised by variability in instrument settings and the absence of standardized preprocessing methods. The development and implementation of effective data preprocessing methods remain to be achieved. RESULTS: In this study, we introduce the R package that automates the data preprocessing of Timer fluorescence data from flow cytometry experiments for quantitative analysis at single-cell level. Our aim is to standardize Timer data analysis to enhance reproducibility and accuracy across different experimental setups. The package includes a trigonometric transformation method to elucidate the dynamics of Fluorescent Timer proteins. We have identified the normalization of immature and mature Timer fluorescence data as essential for robust analysis, clarifying how this normalization affects the analysis of Timer maturation. These preprocessing methods are all encapsulated within the TockyPrep package. CONCLUSIONS: TockyPrep is available for distribution via GitHub at https://github.com/MonoTockyLab/TockyPrep , providing tools for data preprocessing and basic visualization of Timer fluorescence data. This toolkit is expected to enhance the utility of experimental systems utilizing Fluorescent Timer proteins, including the Tocky tools.

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

We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcome variable from one time stage to another. We approximate the posterior by sequentially removing additional uncertainties across different variables and time, based on data-physics driven correlation, to address a broader class of challenging time-dependent decision-making problems under uncertainty. Extensive experiments on real-world datasets ( i.e., urban traffic data and hurricane ensemble forecasting data) demonstrate the superior performance of the proposed targeted decision-making over the state-of-the-art baseline prediction methods across various settings.

The path planning methods based on nonconvex constrained optimization, such as mixed-integer linear programming (MILP), have found various important applications, ranging from unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) to space vehicles. Moreover, their stochastic extensions have enabled risk-aware path planning, which explicitly limits the probability of failure to a user-specified bound. However, a major challenge of those path planning methods is constraint violation between discrete time steps. In the existing approach, a path is represented by a sequence of waypoints and the safety constraints (e.g., obstacle avoidance) are imposed on waypoints. Therefore, the trajectory between waypoints could violate the safety constraints. A naive continuous-time extension results in unrealistic computation cost. In this paper, we propose a novel approach to ensure constraint satisfaction between waypoints without employing a continuous-time formulation. The key idea is to enforce that the same inequality constraint is satisfied on any two adjacent time steps, under assumptions of polygonal obstacles and straight line trajectory between waypoints. The resulting problem encoding is MILP, which can be solved efficiently by commercial solvers. Thus, we also introduce novel extensions to risk-allocation path planners with improved scalability for real-world scenarios and run-time performance. While the proposed encoding approach is general, the particular emphasis of this paper is placed on the chance-constrained, nonconvex path-planning problem (CNPP). We provide extensive simulation results on CNPP to demonstrate the path safety and scalability of our encoding and related path planners.

We present a reinforcement learning approach to explore and optimize a safety-constrained Markov Decision Process(MDP). In this setting, the agent must maximize discounted cumulative reward while constraining the probability of entering unsafe states, defined using a safety function being within some tolerance. The safety values of all states are not known a priori, and we probabilistically model them via aGaussian Process (GP) prior. As such, properly behaving in such an environment requires balancing a three-way trade-off of exploring the safety function, exploring the reward function, and exploiting acquired knowledge to maximize reward. We propose a novel approach to balance this trade-off. Specifically, our approach explores unvisited states selectively; that is, it prioritizes the exploration of a state if visiting that state significantly improves the knowledge on the achievable cumulative reward. Our approach relies on a novel information gain criterion based on Gaussian Process representations of the reward and safety functions. We demonstrate the effectiveness of our approach on a range of experiments, including a simulation using the real Martian terrain data.

Kinematics-based collision detection is important for robot motion planning in unstructured terrain. Especially, planetary rovers require such capability as a single collision may lead to the termination of a mission. For onboard computation, typical numeric approaches are unsuitable as they are computationally expensive and unstable on rocky terrain; instead, a light-weight analytic solution (ACE: Approximate Clearance Evaluation) is planning to be used for the Mars 2020 rover mission. ACE computes the state bounds of articulated suspension systems from terrain height bounds, and assess the safety by checking the constraint violation of states with the worst-case values. ACE's conservative safety check approach can sometimes lead to over-pessimism: feasible states are often reported as infeasible, thus resulting in frequent false positive detection. In this paper, we introduce a computationally efficient probabilistic variant of ACE (called p-ACE) which estimates the probability distributions of states in real time. The advantage of having probability distributions over states, instead of deterministic bounds, is to provide more flexible and less pessimistic worst-case evaluation with probabilistic safety guarantees. Empirically derived distribution models are used to compute the total probability of constraint satisfaction, which is then used for path assessment. Through experiments with a high-fidelity simulator, we empirically show that p-ACE relaxes the deterministic state bounds without losing safety guarantees.

This paper presents a method for predicting slip using Gaussian process regression. Slip models are learned for visually classified terrain types as a function of terrain geometry. Spatial correlations between terrain properties are leveraged for on-line slip model adaptation. Results show that regression-based modeling using in-situ rover data outperforms the state-of-practice, terrestrially-calibrated slip curves in both mean prediction and uncertainty bounds. Local adaptation improves slip prediction results, particularly in high-slip sand areas that pose the greatest threat to rovers. Slip estimates made using a visual classifier to identify terrain type are compared to estimates using on-line model selection with only proprioceptive slip measurements as inputs. The proprioceptive results nearly match the visual results, showing that this approach could work even when a visual classifier is not available.

This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. The objective of the p-Sulu Planner is to allow users to command continuous, stochastic systems, such as unmanned aerial and space vehicles, in a manner that is both intuitive and safe. To this end, we first develop a new plan representation called a chance-constrained qualitative state plan (CCQSP), through which users can specify the desired evolution of the plant state as well as the acceptable level of risk. An example of a CCQSP statement is ``go to A through B within 30 minutes, with less than 0.001% probability of failure." We then develop the p-Sulu Planner, which can tractably solve a CCQSP planning problem. In order to enable CCQSP planning, we develop the following two capabilities in this paper: 1) risk-sensitive planning with risk bounds, and 2) goal-directed planning in a continuous domain with temporal constraints. The first capability is to ensures that the probability of failure is bounded. The second capability is essential for the planner to solve problems with a continuous state space such as vehicle path planning. We demonstrate the capabilities of the p-Sulu Planner by simulations on two real-world scenarios: the path planning and scheduling of a personal aerial vehicle as well as the space rendezvous of an autonomous cargo spacecraft.

Autonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. For robust execution, we must take into account uncertainty, which arises due to uncertain localization, modeling errors, and disturbances. Prior work handled the case of set-bounded uncertainty. We present here a chance-constrained approach, which uses instead a probabilistic representation of uncertainty. The new approach plans the future probabilistic distribution of the vehicle state so that the probability of failure is below a specified threshold. Failure occurs when the vehicle collides with an obstacle or leaves an operator-specified region. The key idea behind the approach is to use bounds on the probability of collision to show that, for linear-Gaussian systems, we can approximate the nonconvex chance-constrained optimization problem as a disjunctive convex program. This can be solved to global optimality using branch-and-bound techniques. In order to improve computation time, we introduce a customized solution method that returns almost-optimal solutions along with a hard bound on the level of suboptimality. We present an empirical validation with an aircraft obstacle avoidance example.

Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation, disturbances, and modeling errors, as well as stochastic mode transitions such as component failures. Chance-constrained control takes into account uncertainty to ensure that the probability of failure, due to collision with obstacles, for example, is below a given threshold. In this paper, we present a novel method for chance-constrained predictive stochastic control of dynamic systems. The method approximates the distribution of the system state using a finite number of particles. By expressing these particles in terms of the control variables, we are able to approximate the original stochastic control problem as a deterministic one; furthermore, the approximation becomes exact as the number of particles tends to infinity. This method applies to arbitrary noise distributions, and for systems with linear or jump Markov linear dynamics, we show that the approximate problem can be solved using efficient mixed-integer linear-programming techniques. We also introduce an important weighting extension that enables the method to deal with low-probability mode transitions such as failures. We demonstrate in simulation that the new method is able to control an aircraft in turbulence and can control a ground vehicle while being robust to brake failures.

The robotic maneuvering of large space structures is key to a number of future orbital missions. In this paper a large space structure maneuver control algorithm, recently proposed, is extended and experimentally validated. The method uses space robots’ manipulators to control the vibration of the structures being maneuvered and their reaction jets perform the large motion maneuvers. The algorithm quickly damps out the vibrations and requires less fuel than reaction jet-based vibration control methods. The approach is called maneuver decoupled control. Its performance is demonstrated and quantitatively evaluated in simulation and experiments.