Traversing soft soils represents a major concern of planetary rover missions. In this paper, we present a new chassis mechanism capable of a crawling gait that enhances trafficability on soft soil while relying on as few actuators as possible. Articulated by two actuated joints, MARCEL is a four-wheeled rover chassis which name stands for Mobile Active Rover Chassis for Enhanced Locomotion. MARCEL's crawling leverages a continuous adjustment of the load distribution on the four wheels using an internal torque applied between two halves of the chassis by series elastic actuation. This allows the pressure on two wheels to be minimized while they are moving forward with the assistance of the chassis's articulated motion. As a result, the wheels can be propelled forward one pair after another while avoiding the bulldozing resistance of the sand. This crawling motion is tested experimentally and is shown to generate more drawbar pull than both rolling or using a mere “push-pull” locomotion. Its ability to extricate the rover from deep sand entrapment is also tested successfully. This will allow future missions to deal with unforeseen terrain properties or to venture in more challenging areas while minimizing design complexity.

Driving energy consumption plays a major role in the navigation of mobile robots in challenging environments, especially if they are left to operate unattended under limited on-board power. This paper reports on first results of an energy-aware path planner, which can provide estimates of the driving energy consumption and energy recovery of a robot traversing complex uneven terrains. Energy is estimated over trajectories making use of a self-supervised learning approach, in which the robot autonomously learns how to correlate perceived terrain point clouds to energy consumption and recovery. A novel feature of the method is the use of 1D convolutional neural network to analyse the terrain sequentially in the same temporal order as it would be experienced by the robot when moving. The performance of the proposed approach is assessed in simulation over several digital terrain models collected from real natural scenarios, and is compared with a heuristic inclination-based energy model. We show evidence of the benefit of our method to increase the overall prediction r2 score by 66.8% and to reduce the driving energy consumption over planned paths by 5.5%.

3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named NDT-Transformer, for real-time and large-scale place recognition using 3D point clouds. Specifically, a 3D Normal Distribution Transform (NDT) representation is employed to condense the raw, dense 3D point cloud as probabilistic distributions (NDT cells) to provide the geometrical shape description. Then a novel NDT-Transformer network learns a global descriptor from a set of 3D NDT cell representations. Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information. Finally, descriptor retrieval is achieved using a query-database for place recognition. Compared to the state-of-the-art methods, the proposed approach achieves an improvement of 7.52% on average top 1 recall and 2.73% on average top 1% recall on the Oxford Robotcar benchmark.

On-orbit proximity operations in space rendezvous, docking and debris removal require precise and robust 6D pose estimation under a wide range of lighting conditions and against highly textured background, i.e., the Earth. This paper investigates leveraging deep learning and photorealistic rendering for monocular pose estimation of known uncooperative spacecraft. We first present a simulator built on Unreal Engine 4, named URSO, to generate labeled images of spacecraft orbiting the Earth, which can be used to train and evaluate neural networks. Secondly, we propose a deep learning framework for pose estimation based on orientation soft classification, which allows modelling orientation ambiguity as a mixture model. This framework was evaluated both on URSO datasets and the European Space Agency pose estimation challenge. In this competition, our best model achieved 3rdplace on the synthetic test set and 2ndplace on the real test set. Moreover, our results show the impact of several architectural and training aspects, and we demonstrate qualitatively how models learned on URSO datasets can perform on real images from space.

This paper presents CAPE, a method to extract planes and cylinder segments from organized point clouds, which processes 640 × 480 depth images on a single CPU core at an average of 300 Hz, by operating on a grid of planar cells. While, compared to state-of-the-art plane extraction, the latency of CAPE is more consistent and 4-10 times faster, depending on the scene, we also demonstrate empirically that applying CAPE to visual odometry can improve trajectory estimation on scenes made of cylindrical surfaces (e.g. tunnels), whereas using a plane extraction approach that is not curve-aware deteriorates performance on these scenes. To use these geometric primitives in visual odometry, we propose extending a probabilistic RGB-D odometry framework based on points, lines and planes to cylinder primitives. Following this framework, CAPE runs on fused depth maps and the parameters of cylinders are modelled probabilistically to account for uncertainty and weight accordingly the pose optimization residuals.

The low-cost and short-lead time of small satellites has led to their use in science-based missions, earth observation, and interplanetary missions. Today, they are also key instruments in orchestrating technological demonstrations for On-Orbit Operations (O3) such as inspection and spacecraft servicing with planned roles in active debris removal and on-orbit assembly. This paper provides an overview of the robotics and autonomous systems (RASs) technologies that enable robotic O3on smallsat platforms. Major RAS topics such as sensing & perception, guidance, navigation & control (GN&C) microgravity mobility and mobile manipulation, and autonomy are discussed from the perspective of relevant past and planned missions.

Active depth cameras suffer from several limitations, which cause incomplete and noisy depth maps, and may consequently affect the performance of RGB-D Odometry. To address this issue, this paper presents a visual odometry method based on point and line features that leverages both measurements from a depth sensor and depth estimates from camera motion. Depth estimates are generated continuously by a probabilistic depth estimation framework for both types of features to compensate for the lack of depth measurements and inaccurate feature depth associations. The framework models explicitly the uncertainty of triangulating depth from both point and line observations to validate and obtain precise estimates. Furthermore, depth measurements are exploited by propagating them through a depth map registration module and using a frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D reprojection errors, independently. Results on RGB-D sequences captured on large indoor and outdoor scenes, where depth sensor limitations are critical, show that the combination of depth measurements and estimates through our approach is able to overcome the absence and inaccuracy of depth measurements.

Identification of the wheel sinkage of exploration rovers provides valuable insight into the characteristics of deformable soils and thus the ease of traversal is also identified. In this paper we propose a simple vision based approach that robustly detects and measures the sinkage of any shaped wheel in real-time and with little sensitivity to various operating conditions. The method is based on color-space segmentation to identify the wheel contour and consequently the depth of the sinkage. In addition, our approach also provides a dynamic sinkage analysis which potentially allows for the identification of non-geometric hazards. The robustness of the algorithm has been validated for poor lighting, blurring, and background noise. The experimental results presented are for a hybrid legged wheel from our in-house single-wheel test-bed.

The integral image is typically used for fast integrating a function over a rectangular region in an image. We propose a method that extends the integral image to do fast integration over the interior of any polygon that is not necessarily rectilinear. The integration time of the method is fast, independent of the image resolution, and only linear to the polygon's number of vertices. We apply the method to Viola and Jones' object detection framework, in which we propose to improve classical Haar-like features with polygonal Haar-like features. We show that the extended feature set improves object detection's performance. The experiments are conducted in three domains: frontal face detection, fixed-pose hand detection, and rock detection for Mars' surface terrain assessment.

This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability. System approximation formulated by the G-FNN is employed in the adaptive controller design for drug infusion in intensive care environment. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through intravenous infusion of sodium nitroprusside (SNP), which is one attractive application in automation of drug delivery. Simulation studies demonstrate the capability of the proposed approach in estimating the drug's effect and regulating blood pressure at a prescribed level.

This paper presents an adaptive modeling and control scheme for blood pressure regulation based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model the unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability, etc. System approximation formulated by the G-FNN is thus employed in the adaptive control of drug infusion for blood pressure regulation. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through the intravenous infusion of sodium nitroprusside (SNP), which is one of the most attractive applications in automation of drug delivery. Simulation study demonstrates superior performance of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

This paper presents an adaptive fuzzy neural control strategy to regulate Mean Arterial Pressure (MAP) through the intravenous infusion of Sodium NitroPrusside (SNP). The proposed indirect adaptive controller involves a feedforward Generalized Fuzzy Neural Network (G-FNN) together with a linear feedback loop. It is capable of achieving real-time fine control under significant uncertainties and without any prior knowledge of the system dynamics. This is achieved through adaptive learning and modeling of the system dynamics and its uncertainties based on the G-FNN. Salient features of the proposed G-FNN include dynamic fuzzy neural structure, fast online learning ability and adaptability, etc. Simulation studies demonstrate the superior performance of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.

A fast approach for automatically generating fuzzy rules from sample patterns using generalized dynamic fuzzy neural networks (GD-FNNs) is presented. The GD-FNN is built based on ellipsoidal basis functions and functionally is equivalent to a Takagi-Sugeno-Kang fuzzy system. The salient characteristics of the GD-FNN are: (1) structure identification and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori; (2) fuzzy rules can be recruited or deleted dynamically; (3) fuzzy rules can be generated quickly without resorting to the backpropagation (BP) iteration learning, a common approach adopted by many existing methods. The GD-FNN is employed in a wide range of applications ranging from static function approximation and nonlinear system identification to time-varying drug delivery system and multilink robot control. Simulation results demonstrate that a compact and high-performance fuzzy rule-base can be constructed. Comprehensive comparisons with other latest approaches show that the proposed approach is superior in terms of learning efficiency and performance.