Fast approximate clearance evaluation for rovers with articulated suspension systems

We present a light‐weight body‐terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse‐kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the approximate clearance evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst‐case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments.     

Slip-compensated path following for planetary exploration rovers

A system that enables continuous slip compensation for a Mars rover has been designed, implemented and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage and reach intended goals in high-slip environments. These components include visual odometry, vehicle kinematics, a Kalman filter pose estimator and a slip-compensated path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The kinematics for a rocker–bogie suspension system estimates vehicle motion by measuring wheel rates, and rocker, bogie and steering angles. The Kalman filter processes measurements from an inertial measurement unit and visual odometry. The filter estimate is then compared to the kinematic estimate to determine whether slippage has occurred, taking into account estimate uncertainties. If slippage is detected, the slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.     

六圆锥轮式月球车运动控制系统的设计研究

六圆锥轮式月球车是一种混合适应型移动机器人车,具有地形适应能力优越,越障能力强的特点。针对该款月球车,本文提出并设计了一套基于TCP/IP通讯,具有一定自主能力的遥操作运动控制系统。遥操作计算机提供月球车路径规划服务,监控月球车运行。基于实时Linux和PC104总线计算机的车载计算机运动控制系统,解析遥操作运动指令,驱动控制月球车运动。运动控制系统在六圆锥轮式月球车样机上调试,模拟现场运行验证了所设计的运动控制系统是可行的。     

The (in)accuracy of novice rover operators' perception of obstacle height from monoscopic images

Researchers have previously described a mobile robot, or rover, operator's difficulty in accurately perceiving the rover's tilt and roll, which can lead to rollover accidents. Safe mobile robot navigation and effective mission planning also require an operator to accurately interpret and understand the geometry and scale of features in the rover's environment. This work presents an experiment that measures an observer's ability to estimate height of distant (5-15 m) obstacles given an accurate local model (e.g., within 0-5 m of the rover), a panoramic image, and a physical mock-up of the local terrain. The experimental conditions were intended to represent a best-case scenario for a stopped rover equipped with short base-line stereoscopic cameras. The participants' task was to extrapolate the well-modeled local geometry to monoscopic images of the more distant terrain. The experiment compared two estimation techniques. With the first technique, each observer physically indicated his or her direct estimates of the obstacle distance and height. With the second estimation technique, which we call horizon analysis, the observer indicated the position of the top and bottom of each rock on an image and the height was calculated by measuring the visual angle between the theoretical horizon and the points indicated by the observer. The direct estimation technique overestimated the height of the rocks by an average of 190%; the horizon analysis technique overestimated by 80%. The results suggest that even when provided with a rich set of supplementary and context information, rover operators have significant difficulty in vertically perceiving the scale of distant terrain. The results also suggest that horizon analysis is a more accurate method for determining the height of distant rover navigation obstacles, when the local terrain is nearly level.     

Efficient autonomous navigation for planetary rovers with limited resources

Rovers operating on Mars require more and more autonomous features to fulfill their challenging mission requirements. However, the inherent constraints of space systems render the implementation of complex algorithms an expensive and difficult task. In this paper, we propose an architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the ExoMars path following navigation approach to enhance the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform significantly longer traverses planned by operators on the ground. The efficient navigation approach has been implemented and tested during field test campaigns on a planetary analogue terrain. The experiments evaluated the proposed architecture by autonomously completing several traverses of variable lengths while avoiding hazards. The approach relies only on the optical Localization Cameras stereo bench, a sensor that is found in all current rovers, and potentially allows for computationally inexpensive long‐range autonomous navigation in terrains of medium difficulty.     

A stereo-vision system for support of planetary surface exploration

Abstract. In this paper, we present a system that was developed for the European Space Agency (ESA) for the support of planetary exploration. The system that is sent to the planetary surface consists of a rover and a lander. The lander contains a stereo head equipped with a pan-tilt mechanism. This vision system is used both for modeling the terrain and for localization of the rover. Both tasks are necessary for the navigation of the rover. Due to the stress that occurs during the flight, a recalibration of the stereo-vision system is required once it is deployed on the planet. Practical limitations make it unfeasible to use a known calibration pattern for this purpose; therefore, a new calibration procedure had to be developed that could work on images of the planetary environment. This automatic procedure recovers the relative orientation of the cameras and the pan and tilt axes, as well as the exterior orientation for all the images. The same images are subsequently used to reconstruct the 3-D structure of the terrain. For this purpose, a dense stereo-matching algorithm is used that (after rectification) computes a disparity map. Finally, all the disparity maps are merged into a single digital terrain model. In this paper, a simple and elegant procedure is proposed that achieves that goal. The fact that the same images can be used for both calibration and 3-D reconstruction is important, since, in general, the communication bandwidth is very limited. In addition to navigation and path planning, the 3-D model of the terrain is also used for virtual-reality simulations of the mission, wherein the model is texture mapped with the original images. The system has been implemented, and the first tests on the ESA planetary terrain testbed were successful.     

A new deterministic approach in a decision support system for ship’s trajectory planning

The paper introduces a Decision Support System for ships, developed to solve a problem of collision avoidance with static and dynamic obstacles. The system maps the decision making capability of a human (navigation) expert to solve the path planning problem for a ship in a complex navigation environment. It can be further developed to provide automatic control of a ship. It utilizes a new, fast and effective, deterministic method, called the Trajectory Base Algorithm, to calculate a safe, optimal path for a ship. The system structure, a detailed explanation of a new method, followed by results of simulation tests are all presented in the paper. The results proof a successful application of the method to solve a path planning problem for ships with the consideration of both static and dynamic obstacles in the environment, marine traffic regulations and dynamic properties of a ship, what makes this approach applicable in commercial systems. The approach can also be adapted for application in mobile robots path planning. The experimental results and ability of the system to achieve a new functionality of full autonomy show significance of this contribution to the development of Expert and Intelligent Systems domain. The author believes that autonomous systems constitute the future of Expert and Intelligent Systems.     

Adaptive localization and mapping with application to planetary rovers

Future exploration rovers will be equipped with substantial onboard autonomy. SLAM is a fundamental part and has a close connection with robot perception, planning, and control. The community has made great progress in the past decade by enabling real‐world solutions and is addressing important challenges in high‐level scalability, resources awareness, and domain adaptation. A novel adaptive SLAM system is proposed to accomplish rover navigation and computational demands. It starts from a three‐dimensional odometry dead reckoning solution and builds up to a full graph optimization that takes into account rover traction performance. A complete kinematics of the rover locomotion system improves the wheel odometry solution. In addition, an odometry error model is inferred using Gaussian processes (GPs) to predict nonsystematic errors induced by poor traction of the rover with the terrain. The nonparametric GP regression serves to adapt the localization and mapping to the current navigation demands (domain adaptation). The method brings scalability and adaptiveness to modern SLAM. Therefore, an adaptive strategy develops to adjust the image frame rate (active perception) and to influence the optimization backend by including high informative keyframes in the graph (adaptive information gain). The work is experimentally verified on a representative planetary rover under a realistic field test scenario. The results show a modern SLAM systems that adapt to the predicted error. The system maintains accuracy with less number of nodes taking the most benefit of both wheel and visual methods in a consistent graph‐based smoothing approach.     

An intelligent terrain-based navigation system for planetary rovers

A fuzzy logic framework for onboard terrain analysis and guidance towards traversable regions. An onboard terrain-based navigation system for mobile robots operating on natural terrain is presented. This system utilizes a fuzzy-logic framework for onboard analysis of the terrain and develops a set of fuzzy navigation rules that guide the rover toward the safest and the most traversable regions. The overall navigation strategy deals with uncertain knowledge about the environment and uses the onboard terrain analysis to enable the rover to select easy-to-traverse paths to the goal autonomously. The navigation system is tested and validated with a set of physical rover experiments and demonstrates the autonomous capability of the system     

Mars Pathfinder Microrover

When the Mars Pathfinder (MPF) spacecraft lands on Mars, the Microrover Flight Experiment (MFEX) will be deployed and perform its mission to conduct technology experiments verifying the engineering design, to deploy an alpha proton x-ray spectrometer (APXS) to measure elemental properties of rocks and soil, and to image the MPF lander. In accomplishing this mission the MFEX rover must determine a safe path to goal locations traversing over a poorly known Martian surface. The rover does this mission with a capable mobile platform executing on-board autonomous functions of navigation and hazard avoidance. In this paper we describe the rover, its operational environment and the implementation of the on-board autonomous functions.     

Range‐dependent Terrain Mapping and Multipath Planning using Cylindrical Coordinates for a Planetary Exploration Rover

This paper presents terrain mapping and path‐planning techniques that are key issues for autonomous mobility of a planetary exploration rover. In this work, a LIDAR (light detection and ranging) sensor is used to obtain geometric information on the terrain. A point cloud of the terrain feature provided from the LIDAR sensor is usually converted to a digital elevation map. A sector‐shaped reference grid for the conversion process is proposed in this paper, resulting in an elevation map with cylindrical coordinates termed as C²DEM. This conversion approach achieves a range‐dependent resolution for the terrain mapping: a detailed terrain representation near the rover and a sparse representation far from the rover. The path planning utilizes a cost function composed of terrain inclination, terrain roughness, and path length indices, each of which is subject to a weighting factor. The multipath planning developed in this paper first explores possible sets of weighting factors and generates multiple candidate paths. The most feasible path is then determined by a comparative evaluation between the candidate paths. Field experiments with a rover prototype at a Lunar/Martian analog site were performed to confirm the feasibility of the proposed techniques, including the range‐dependent terrain mapping with C²DEM and the multipath‐planning method.     

Mars microrover navigation: Performance evaluation and enhancement

In 1996, NASA will launch the Mars Pathfinder spacecraft, which will carry an 11 kg rover to explore the immediate vicinity of the lander. To assess the capabilities of the rover, as well as to set priorities for future rover research, it is essential to evaluate the performance of its autonomous navigation system as a function of terrain characteristics. Unfortunately, very little of this kind of evaluation has been done, for either planetary rovers or terrestrial applications. To fill this gap, we have constructed a new microrover testbed consisting of the Rocky 3.2 vehicle and an indoor test arena with overhead cameras for automatic, real-time tracking of the true rover position and heading. We create Mars analog terrains in this arena by randomly distributing rocks according to an exponential model of Mars rock size frequency created from Viking lander imagery. To date, we have recorded detailed logs from over 85 navigation trials in this testbed. In this paper, we outline current plans for Mars exploration over the next decade, summarize the design of the lander and rover for the 1996 Pathfinder mission, and introduce a decomposition of rover navigation into four major functions: goal designation, rover localization, hazard detection, and path selection. We then describe the Pathfinder approach to each function, present results to date of evaluating the performance of each function, and outline our approach to enhancing performance for future missions. The results show key limitations in the quality of rover localization, the speed of hazard detection, and the ability of behavior control algorithms for path selection to negotiate the rock frequencies likely to be encountered on Mars. We believe that the facilities, methodologies, and to some extent the specific performance results presented here will provide valuable examples for efforts to evaluate robotic vehicle performance in other applications.     

Autonomous over-the-horizon navigation using LIDAR data

In this paper we present the approach for autonomous planetary exploration developed at the Canadian Space Agency. The goal of this work is to enable autonomous navigation to remote locations, well beyond the sensing horizon of the rover, with minimal interaction with a human operator. We employ LIDAR range sensors due to their accuracy, long range and robustness in the harsh lighting conditions of space. Irregular Triangular Meshes (ITMs) are used for representing the environment, providing an accurate, yet compact, spatial representation. In this paper a novel path-planning technique through the ITM is introduced, which guides the rover through flat terrain and safely away from obstacles. Experiments performed in CSA’s Mars emulation terrain, validating our approach, are also presented.     

Decisional autonomy of planetary rovers

To achieve the ever increasing demand for science return, planetary exploration rovers require more autonomy to successfully perform their missions. Indeed, the communication delays are such that teleoperation is unrealistic. Although the current rovers (such as MER) demonstrate a limited navigation autonomy, and mostly rely on ground mission planning, the next generation (e.g., NASA Mars Science Laboratory and ESA Exomars) will have to regularly achieve long range autonomous navigation tasks. However, fully autonomous long range navigation in partially known planetary‐like terrains is still an open challenge for robotics. Navigating hundreds of meters without any human intervention requires the robot to be able to build adequate representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to control its motions, and to localize itself as it moves. All these activities have to be planned, scheduled, and performed according to the rover context, and controlled so that the mission is correctly fulfilled. To achieve these objectives, we have developed a temporal planner and an execution controller, which exhibit plan repair and replanning capabilities. The planner is in charge of producing plans composed of actions for navigation, science activities (moving and operating instruments), communication with Earth and with an orbiter or a lander, while managing resources (power, memory, etc.) and respecting temporal constraints (communication visibility windows, rendezvous, etc.). High level actions also need to be refined and their execution temporally and logically controlled. Finally, in such critical applications, we believe it is important to deploy a component that protects the system against dangerous or even fatal situations resulting from unexpected interactions between subsystems (e.g., move the robot while the robot arm is unstowed) and/or software components (e.g., take and store a picture in a buffer while the previous one is still being processed). In this article we review the aforementioned capabilities, which have been developed, tested, and evaluated on board our rovers (Lama and Dala). After an overview of the architecture design principle adopted, we summarize the perception, localization, and motion generation functions required by autonomous navigation, and their integration and concurrent operation in a global architecture. We then detail the decisional components: a high level temporal planner that produces the robot activity plan on board, and temporal and procedural execution controllers. We show how some failures or execution delays are being taken care of with online local repair, or replanning. © 2007 Wiley Periodicals, Inc.     

Terrain-evaluation-based motion planning for legged locomotion on irregular terrain

The path planning of legged locomotion is complex in that path generation is based on constraints not only from body motion, but also from leg motion. A general approach to path planning will fail in generating a feasible path for walking machines when facing the huge searching space of legged locomotion. In this paper, an effective method of path planning is introduced by virtue of terrain evaluation. It maps obstacles into the robot configuration space by evaluating the obstacles' influence on the legged locomotion. The evaluation produces an index of terrain, called terrain complexity, for path planning. Using potential-guided searching, the terrain with mapped obstacles is searched to generate a feasible path.     

Development of a steerable,wheel-type,in-pipe robot and its path planning

This paper presents the development of a steerable, wheel-type, in-pipe robot and its path planning. First, we show the construction of the robot and demonstrate its locomotion inside a pipe. The robot is composed of two wheel frames and an extendable arm which links the centers of the two wheel frames. The arm presses the frames against the interior wall of a pipe to support the robot. The wheels of the frames are steered independently so that the robot can turn within a small radius of rotation. Experimental results of the locomotion show that the steering control is effective for autonomous navigation to avoid obstacles and to enter the joint spaces of L- and T-shaped pipes. Generally, autonomous navigation is difficult for wheel-type robots because the steering angles required to travel along a desired path are not easily determined. In our previous work, the relationship between the steering angles and locomotion trajectories in a pipe has already been analyzed. Using this analysis, we propose the path planning in pipes.     

Terrain trafficability analysis for autonomous navigation: A GIS-based approach

This article presents a method of generating grid maps for autonomous navigation using a geographic information system (GIS). Besides developing ordinary map systems, a GIS can be applicable to the terrain trafficability analysis for an unmanned ground vehicle (UGV) by generating maps in which information about its spatial environment is encoded for path planning as part of autonomous navigation. For such maps, the most commonly used is a grid map over which a UGV’s paths can be optimized. As the UGV moves and its situation changes, grid maps need to be generated quickly in an automatic manner for new paths. First of all, a GIS is analyzed to identify meaningful data, layers and attributes, which are then related to the UGV’s navigation. For each grid in the grid map, every meaningful layer included is processed so that the set of values of attributes of the layer can be converted into the traverse cost. If multiple layers are included in a single grid, those traverse costs are fused and the result data is then replaced in the grid. Grid map generation is finished after all grids are filled with such costs. From test results using an experimental program, it is validated that the proposed method can be a viable tool for the timely generation of grid maps using a geographic information system for the UGV’s autonomous navigation.     

Visual terrain mapping for Mars exploration

One goal for future Mars missions is for a rover to be able to navigate autonomously to science targets not visible to the rover, but seen in orbital or descent images. This can be accomplished if accurate maps of the terrain are available for the rover to use in planning and localization. We describe techniques to generate such terrain maps using images with a variety of resolutions and scales, including surface images from the lander and rover, descent images captured by the lander as it approaches the planetary surface, and orbital images from current and future Mars orbiters. At the highest resolution, we process surface images captured by rovers and landers using bundle adjustment. At the next lower resolution (and larger scale), we use wide-baseline stereo vision to map terrain distant from a rover with surface images. Mapping the lander descent images using a structure-from-motion algorithm generates data at a hierarchy of resolutions. These provide a link between the high-resolution surface images and the low-resolution orbital images. Orbital images are mapped using similar techniques, although with the added complication that the images may be captured with a variety of sensors. Robust multi-modal matching techniques are applied to these images. The terrain maps are combined using a system for unifying multi-resolution models and integrating three-dimensional terrains. The result is a multi-resolution map that can be used to generate fixed-resolution maps at any desired scale.     

A new autonomous celestial navigation method for the lunar rover

A secure and autonomous navigation system is needed for the lunar rover in future lunar missions in case of emergencies. Celestial navigation is a very attractive solution for long distance navigation on the Moon without the need of ground navigation aids. It only uses star altitudes, which are measured by a high accuracy star sensor and inertial measurement unit (IMU) to estimate the position of the rover. The navigational accuracy of this method depends largely on the accuracy of measurements, so the measurement errors have a great impact on the navigational performance. A new autonomous celestial navigation method for the lunar rover is presented in this paper, which uses the augmented state unscented particle filter (ASUPF) to deal with the systematic error and random error in the measurements. The validity and feasibility of this new method is tested and examined by the hardware-in-loop test. A position estimation error within 60 m is obtained. Compared to the conventional method, this method shows better navigation performance and higher adaptability to these measurement errors.