Improved Rover State Estimation in Challenging Terrain

Given ambitious mission objectives and long delay times between command-uplink/data-downlink sessions, increased autonomy is required for planetary rovers. Specifically, NASA's planned 2003 and 2005 Mars rover missions must incorporate increased autonomy if their desired mission goals are to be realized. Increased autonomy, including autonomous path planning and navigation to user designated goals, relies on good quality estimates of the rover's state, e.g., its position and orientation relative to some initial reference frame. The challenging terrain over which the rover will necessarily traverse tends to seriously degrade a dead-reckoned state estimate, given severe wheel slip and/or interaction with obstacles. In this paper, we present the implementation of a complete rover navigation system. First, the system is able to adaptively construct semi-sparse terrain maps based on the current ground texture and distances to possible nearby obstacles. Second, the rover is able to match successively constructed terrain maps to obtain a vision-based state estimate which can then be fused with wheel odometry to obtain a much improved state estimate. Finally the rover makes use of this state estimate to perform autonomous real-time path planning and navigation to user designated goals. Reactive obstacle avoidance is also implemented for roaming in an environment in the absence of a user designated goal. The system is demonstrated in soft soil and relatively dense rock fields, achieving state estimates that are significantly improved with respect to dead reckoning alone (e.g., 0.38 m mean absolute error vs. 1.34 m), and successfully navigating in multiple trials to user designated goals.     

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.     

Vibration-based terrain classification for planetary exploration rovers

Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover's safety on slopes and uneven surfaces. Visual features are often used to classify terrain; however, vision can be sensitive to lighting variations and other effects. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. This sensing mode is robust to lighting variations. Vibrations are measured using an accelerometer mounted on the rover structure. The classifier is trained using labeled vibration data during an offline learning phase. Linear discriminant analysis is used for online identification of terrain classes, such as sand, gravel, or clay. This approach has been experimentally validated on a laboratory testbed and on a four-wheeled rover in outdoor conditions.     

Slope Perception from Monoscopic Field Images: Applications to Mobile Robot Navigation

When remotely navigating a mobile robot, operators must estimate the slope of local terrain in order to avoid areas that are too steep to climb or that slope so steeply downward that the operator would lose control of the rover. Although many rovers are equipped with sensor systems to aid the operator in this task, it is sometimes necessary to estimate slopes from two-dimensional images, either when planning operations or when the operator wishes to monitor the results of a sensor system. This experiment compares the operator’s estimates of the slope in Martian terrain with the actual slope determined from three-dimensional data. The ten participants overestimated the slope of the indicated regions by an average of 19° (SD 16°). An analytic model of the error, based on psychophysical analysis, accurately predicts the average magnitude of the errors. Implementation of this model eliminates an average amount of participant error. However, the large estimate variance within and between participants and images still poses a challenge for accurate slope estimation.     

Wide-baseline stereo vision for terrain mapping

Terrain mapping is important for mobile robots to perform localization and navigation. Stereo vision has been used extensively for this purpose in outdoor mapping tasks. However, conventional stereo does not scale well to distant terrain. This paper examines the use of wide-baseline stereo vision in the context of a mobile robot for terrain mapping, and we are particularly interested in the application of this technique to terrain mapping for Mars exploration. In wide-baseline stereo, the images are not captured simultaneously by two cameras, but by a single camera at different positions. The larger baseline allows more accurate depth estimation of distant terrain, but the robot motion between camera positions introduces two new problems. One issue is that the robot estimates the relative positions of the camera at the two locations imprecisely, unlike the precise calibration that is performed in conventional stereo. Furthermore, the wide-baseline results in a larger change in viewpoint than in conventional stereo. Thus, the images are less similar and this makes the stereo matching process more difficult. Our methodology addresses these issues using robust motion estimation and feature matching. We give results using real images of terrain on Earth and Mars and discuss the successes and failures of the technique.     

Nonparametric Traversability Estimation in Partially Occluded and Deformable Terrain

Terrain traversability estimation is a fundamental requirement to ensure the safety of autonomous planetary rovers and their ability to conduct long‐term missions. This paper addresses two fundamental challenges for terrain traversability estimation techniques. First, representations of terrain data, which are typically built by the rover's onboard exteroceptive sensors, are often incomplete due to occlusions and sensor limitations. Second, during terrain traversal, the rover‐terrain interaction can cause terrain deformation, which may significantly alter the difficulty of traversal. We propose a novel approach built on Gaussian process (GP) regression to learn, and consequently to predict, the rover's attitude and chassis configuration on unstructured terrain using terrain geometry information only. First, given incomplete terrain data, we make an initial prediction under the assumption that the terrain is rigid, using a learnt kernel function. Then, we refine this initial estimate to account for the effects of potential terrain deformation, using a near‐to‐far learning approach based on multitask GP regression. We present an extensive experimental validation of the proposed approach on terrain that is mostly rocky and whose geometry changes as a result of loads from rover traversals. This demonstrates the ability of the proposed approach to accurately predict the rover's attitude and configuration in partially occluded and deformable terrain.     

Quadruped robot trotting over irregular terrain assisted by stereo-vision

Legged robots have the potential to navigate in challenging terrain, and thus to exceed the mobility of wheeled vehicles. However, their control is more difficult as legged robots need to deal with foothold computation, leg trajectories and posture control in order to achieve successful navigation. In this paper, we present a new framework for the hydraulic quadruped robot HyQ, which performs goal-oriented navigation on unknown rough terrain using inertial measurement data and stereo-vision. This work uses our previously presented reactive controller framework with balancing control and extends it with visual feedback to enable closed-loop gait adjustment. On one hand, the camera images are used to keep the robot walking towards a visual target by correcting its heading angle if the robot deviates from it. On the other hand, the stereo camera is used to estimate the size of the obstacles on the ground plane and thus the terrain roughness. The locomotion controller then adjusts the step height and the velocity according to the size of the obstacles. This results in a robust and autonomous goal-oriented navigation over difficult terrain while subject to disturbances from the ground irregularities or external forces. Indoor and outdoor experiments with our quadruped robot show the effectiveness of this framework.     

Rule–based reasoning and neural network perception for safe off–road robot mobility

Operational safety and health monitoring are critical matters for autonomous field mobile robots such as planetary rovers operating on challenging terrain. This paper describes relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a mobility and navigation context. The proposed rover safety module is composed of two distinct components: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic approaches to reasoning about safe attitude and traction management are presented, wherein inertial sensing of safety status and vision–based neural network perception of terrain quality are used to infer safe speeds of traversal. Results of initial field tests and laboratory experiments are also described. The approach provides an intrinsic safety cognizance and a capacity for reactive mitigation of robot mobility and navigation risks.     

SmartNav: A rule‐free fuzzy approach to rover navigation

This paper introduces the basic concepts of the SmartNav rule‐free fuzzy approach to safe rover navigation through hazardous natural terrain. This novel “rule‐free” approach reduces the complexity in rover navigation where many alternative paths must be evaluated and compared. The SmartNav rover navigation architecture integrates goal attainment with both local and regional hazard avoidance. Goal and safety preference factors differentiate between preferred and unpreferred terrain sectors. The goal‐preference factor is used to make sector evaluation based on the sector orientation relative to the designated goal position. The safety‐preference factors are used to make sector evaluations on the basis of the sector local and regional hazards. These sector evaluations are blended to find the effective preference factor for each sector. The preference factors of all sectors are then compared to choose the heading command for the rover. The rover speed command is also computed based on the goal distance and safety‐preference factor of the chosen sector. The above navigation steps are continuously repeated throughout the rover motion. Numerical examples are presented to illustrate the basic concepts introduced in this paper, and tests on a commercial rover are planned. © 2005 Wiley Periodicals, Inc.     

Theory and experiments in SmartNav rover navigation

This paper describes theoretical and experimental results using the SmartNav rule-free fuzzy rover navigation system. SmartNav divides the terrain perceived by the rover into a number of circular sectors, and evaluates each sector using goal and safety preference factors to differentiate between preferred and unpreferred terrain sectors. The goal-preference factor is used to make sector evaluation based on the sector orientation relative to the designated goal position. The safety-preference factors are used to make sector evaluations on the basis of the sector local and regional terrain hazards. Three methods are developed to blend the three sector evaluations in order to find the effective preference factor for each sector. Two sector selection methods are then described in which the sector preference factors are used to find the heading command for the rover. The rover speed command is also computed based on the goal distance and safety-preference factor of the chosen sector. The above navigation steps are continuously repeated throughout the rover motion. Experimental results are presented to demonstrate the navigational capabilities of SmartNav using a commercial Pioneer 2AT rover traversing a simulated Martian terrain at the JPL Mini Mars Yard.

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Hyperion project follows sun

Describes the field testing of the Hyperion robotics project which uses the concept of sun-synchronous navigation. Hyperion is made of aluminum tubing and has four, wheels on two axles. Each wheel has a motor, and the front axle has a passive joint that can roll and yaw relative to the back end. Hyperion steers by driving the wheels at different speeds, and the passive front-axle joint turns the robot in arcs. The 1.5 meter-high front axle gives the robot's digital cameras and laser scanner a view of surrounding terrain; the latter helps it detect close-range obstacles. A third panoramic camera offers remote observers a view of its surroundings. All other robot components are housed in the body, which is mounted between the axles. Hyperion uses a pair of global positioning system receivers and an odometric system to determine its position and orientation and wheel-based odometric:sensing to estimate motion. Odometry also enables positioning and orientation on other planets, where the robot could use things such as a star, sun, or terrain landmark in place of GPS. Multiple onboard sensors act as Hyperion's health-monitoring and fault-detection system, checking everything from computer processes and laser scanners to a sensor suite that monitors the rate at which the system receives GPS information. The planner combines a priori knowledge of terrain, planetary rotation, sun location, solar flux predictions, and predictions of rover capability.     

Field testing of a rover guidance, navigation, and control architecture to support a ground-ice prospecting mission to Mars

This paper provides an overview of a rover guidance, navigation, and control (GN&C) architecture being developed to support a ground-ice prospecting mission to Mars. The main contribution of this paper is to detail an integrated field campaign that demonstrates the viability of the key rover GN&C techniques needed to carry out this mission. Tests were conducted on Devon Island in the Canadian High Arctic during the summer of 2009, wherein a large field robot was driven on real polygonal terrain (a landform of interest on Mars). Lessons learned and recommendations for future work are provided.     

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     

Navigating a mobile robot by a traversability field histogram.

This paper presents an autonomous terrain navigation system for a mobile robot. The system employs a two-dimensional laser range finder (LRF) for terrain mapping. A so-called "traversability field histogram" (TFH) method is proposed to guide the robot. The TFH method first transforms a local terrain map surrounding the robot's momentary position into a traversability map by extracting the slope and roughness of a terrain patch through least-squares plane fitting. It then computes a so-called "polar traversability index" (PTI) that represents the overall difficulty of traveling along the corresponding direction. The PTIs are represented in a form of histogram. Based on this histogram, the velocity and steering commands of the robot are determined. The concept of a virtual valley and an exit condition are proposed and used to direct the robot such that it can reach the target with a finite-length path. The algorithm is verified by simulation and experimental results.     

Planetary Rover Developments Supporting Mars Exploration, Sample Return and Future Human-Robotic Colonization

We overview our recent research on planetary mobility. Products of this effort include the Field Integrated Design & Operations rover (FIDO), Sample Return Rover (SRR), reconfigurable rover units that function as an All Terrain Explorer (ATE), and a multi-Robot Work Crew of closely cooperating rovers (RWC). FIDO rover is an advanced technology prototype; its design and field testing support NASA's development of long range, in situ Mars surface science missions. Complementing this, SRR implements autonomous visual recognition, navigation, rendezvous, and manipulation functions enabling small object pick-up, handling, and precision terminal docking to a Mars ascent vehicle for future Mars Sample Return. ATE implements on-board reconfiguration of rover geometry and control for adaptive response to adverse and changing terrain, e.g., traversal of steep, sandy slopes. RWC implements coordinated control of two rovers under closed loop kinematics and force constraints, e.g., transport of large payloads, as would occur in robotic colonies at future Mars outposts. RWC is based in a new extensible architecture for decentralized control of, and collective state estimation by multiple heterogeneous robotic platforms—CAMPOUT; we overview the key architectural features. We have conducted experiments with all these new rover system concepts over variable natural terrain. For each of the above developments, we summarize our approach, some of our key experimental results to date, and our future directions of planned development.     

用势场法改进的极限环导航方法在移动机器人中的应用

结合人工势场法,提出一种新的极限环方法.它可以在诸如机器人足球赛等动态变化的环境中为自主移动机器人进行很好的实时路径规划.将障碍物的运动速度和一些不易直接表达的策略① 等转化成势场,然后把势场抽象成虚拟的障碍物,通过改变非线性方程的极限环半径,获得动态运动路径规划.这种方法能让机器人对高速运动的障碍物有很平滑的避障能力,并且能综合复杂的路径规划要求达到目标.仿真和试验都表明了这种方法在机 器人足球赛中的应用价值.􀁱 􀁽     

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.     

On responsiveness, safety, and completeness in real-time motion planning

Replanning is a powerful mechanism for controlling robot motion under hard constraints and unpredictable disturbances, but it involves an inherent tradeoff between the planner’s power (e.g., a planning horizon or time cutoff) and its responsiveness to disturbances. This paper presents an adaptive time-stepping architecture for real-time planning with several advantageous properties. By dynamically adapting to the amount of time needed for a sample-based motion planner to make progress toward the goal, the technique is robust to the typically high variance exhibited by replanning queries. The technique is proven to be safe and asymptotically complete in a deterministic environment and a static objective. For unpredictably moving obstacles, the technique can be applied to keep the robot safe more reliably than reactive obstacle avoidance or fixed time-step replanning. It can also be applied in a contingency planning algorithm that achieves simultaneous safety-seeking and goal-seeking motion. These techniques generate responsive and safe motion in both simulated and real robots across a range of difficulties, including applications to bounded-acceleration pursuit-evasion, indoor navigation among moving obstacles, and aggressive collision-free teleoperation of an industrial robot arm.     

Weaver: Hexapod robot for autonomous navigation on unstructured terrain

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.     

Fast marching subjected to a vector field–path planning method for mars rovers

Path planning is an essential tool for the robots that explore the surface of Mars or other celestial bodies such as dwarf planets, asteroids, or moons. These vehicles require expert and intelligent systems to adopt the best decisions in order to survive in a hostile environment. The planning module has to take into account multiple factors such as the obstacles, the slope of the terrain, the surface roughness, the type of ground (presence of sand), or the information uncertainty. This paper presents a path planning system for rovers based on an improved version of the Fast Marching (FM) method. Scalar and vectorial properties are considered when computing the potential field which is the basis of the proposed technique. Each position in the map of the environment has a cost value (potential) that is used to include different types of variables. The scalar properties can be introduced in a component of the cost function that can represent characteristics such as difficulty, slowness, viscosity, refraction index, or incertitude. The cost value can be computed in different ways depending on the information extracted from the surface and the sensor data of the rover. In this paper, the surface roughness, the slope of the terrain, and the changes in height have been chosen according to the available information. When the robot is navigating sandy terrain with a certain slope, there is a landslide that has to be considered and corrected in the path calculation. This landslide is similar to a lateral current or vector field in the direction of the negative gradient of the surface. Our technique is able to compensate this vector field by introducing the influence of this variable in the cost function. Because of this modification, the new method has been called Fast Marching (subjected to a) vector field (FMVF). Different experiments have been carried out in simulated and real maps to test the method performance. The proposed approach has been validated for multiple combinations of the cost function parameters.