IROS2019国际学术会议论文集 1168

Abstract In an autonomous scientific exploration system the terrain map generated from mapping process integrates sensing information from multiple aspects and lays the base for decision making processes With the increasing challenges in planetary exploration equipping planetary rovers with the principles of terramechanics is becoming more and more common especially on rough or intricate terrain However it is difficult for conventional maps with elevation information only to reflect terrain mechanical properties which play important roles in terramechanics based simulation or motion control This study extracts the dominant parameters in terrain bearing and shearing models and presents a multi layered grid map with fundamental geometric and mechanical elements A corresponding mapping scheme based on dense visual input is designed to reconstruct elevation in the map and predict terrain mechanical parameters of the entire visual field Experiments are conducted to verify the practicability of the approach proposed in a Mars emulation yard with a rover prototype I INTRODUCTION In planetary exploration mapping is a crucial process throughout all phases In the operational phase it builds an interactive virtual planetary environment for tele operation or simulation on earth In the autonomous traverse phase the map constructed by it serves as the basis of a series of processes such as path planning localization and motion control Meanwhile it integrates the characteristics of the terrain and connects the main links with other aspects of the mission The mechanical properties of the terrain have a strong impact on the locomotive performance of rovers Hence there is an increasing tendency to take terramechanics into consideration throughout the key aspects of Mars exploration High fidelity simulation has been performed based on a terramechanics model for a wheeled mobile robot on deformable rough terrain 1 Path planning and performance evaluation with a terramechanics based dynamics simulation are also presented for lunar planetary exploration rovers 2 Future Mars missions will demand safer more adaptive and more reliable autonomous traverses than previously required This imposes the need for a more comprehensive but succinct map representation which should be equipped with more information regarding terrain properties in a compact form Conventional mapping with only geometric information plays This study was supported by the National Natural Science Foundation of China Grant No 51822502 61370033 Foundation for Innovative Research Groups of the natural Science Foundation of China Grant No 51521003 the Fundamental Research Funds for the Central Universities Grant No HIT BRETIV 201903 and the 111 Project Grant No B07018 R Zhou L Ding H Gao W Feng Z Deng and N Li are with the State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 Heilongjiang China e mail zhouryhit liangding gaohaibo 1160800125 dengzq lnlinanln Corresponding author Liang Ding e mail liangding a limited role in hazard alarm For example a rover can easily traverse the protrusion of a rock but it might become entrenched in flat and loose soil Terrain mechanical properties affect traversability and potential hazards resulting from sudden changes in terrain mechanical properties merit much attention For instance in 2005 and again in 2006 NASA s Mars Exploration Rover MER Opportunity became entrenched in loose drift material and was immobilized for several weeks Traversing without considering the mechanical properties of the terrain in some cases can even lead to mission failure The worst situation involved the MER Spirit rover which became trapped in a sand dune in 2009 with no possibility of recovery Though some efforts have been made to alarm operators or avoid rover entrapment through obstacle detection or terrain classification 3 5 simply defining an obstacle from the object perspective is sometimes difficult to meet the demand of applications For example high fidelity simulation based on terramechanics requires the terrain map accompanied with mechanical properties for interaction force and torque calculation 6 It is also a prerequisite for motion control which is associated with wheel terrain interaction based on terramechanics Without understanding the terrain mechanical parameters none of applications based on the principle of terramechanics can be applied with confidence Hence a novel mapping approach to constructing a map from terramechanics perspective and reflecting conspicuous differences in mechanical properties of the terrain is desperately needed for all the subsequent processes that rely on it In this paper a simple comprehensible and unified terrain map that reflects fundamental elements in terms of geometry and mechanics is presented It characterizes the mechanical properties of terrain in a quantitative format rather than using a qualitative judgment Meanwhile a semantic based approach is designed to predicting terrain mechanical properties from vision and is integrated into the mapping process II RELATED WORK Our work lies in the intersection of terrain characterization and mapping for planetary rovers Thus the related literatures in these domains are reviewed A Terrain Characterization for Planetary Rovers Researchers have long been interested in terrain characterization For a rover a relatively reliable way to determine terrain mechanical properties is through in situ measurement which relies on wheel soil interaction 7 Francisco et al 8 deduced the terradynamics for multi legged wheel legs and obtained soil characteristics through torque and vibrations caused by stick slip events A similar work presented in 7 identified the key characteristics of terrain from the way motor currents varied with rate of turn Though approaches based on in situ measurement is able to Mapping for Planetary Rovers from Terramechanics Perspective Ruyi Zhou Liang Ding Member IEEE Haibo Gao Wenhao Feng Zongquan Deng and Nan Li 2019 IEEE RSJ International Conference on Intelligent Robots and Systems IROS Macau China November 4 8 2019 978 1 7281 4003 2 19 31 00 2019 IEEE1869 i ji ji ji j Czk i j z i j k i j z x y k x y x y Height Layer Stiffness Layer Friction Layer x y z Figure 1 Map model with geometrical and mechanical properties characterize terrain with high precision they are constrained to traversed regions and most approaches are only applicable to soft deformable soil Another approach to determining terrain non geometric properties is taking advantage of visual information 9 It is a relatively new and challenging research field not only because of the diversity of terrain but also because it depends on many factors such as material surface conditions and contact area Some attempts have been made For example the percentage of slip of a rover is predicted with terrain classification and slope based on data gathered on a learning stage 10 However the slip prediction model is not compatible among different rovers because the slip value is influenced by rover configurations such as lugs wheel dimension and vertical load Therefore for better reusability it is more appropriate to estimate terrain intrinsic parameters Benefitting from the broad sensing range vision based approaches to predict terrain bearing and shearing properties in Martian environment deserve more attention Although surface friction has been roughly predicted based on vision in Earth environments with diverse surfaces 11 related researches on Martian environment have been far less B Mapping for Planetary Rovers Pertaining to mapping for planetary rovers many previous studies have concentrated on building a digital elevation map DEM as well as recognizing terrain types For instance a system is introduced to automatically identify geometrical hazards on terrain and mitigate risks based on terrain classification 3 Integrating the sensing of terrain mechanical properties into the mapping process however has been rarely addressed Study similar to ours as 11 which designed a navigation system with an integrated goodness map and remote slip prediction In addition some studies have attempted to integrate geological knowledge into map representation but they did not include mapping specifically III CHARACTERIZATION OF TERRAIN MECHANICAL PROPERTIES IN GRID MAP The map constructed from terramechanics perspective is expected to be comprehensive and succinct On the one hand it should be equipped with more aspects of the terrain properties to improve the safety of the rover as well as furnish adaptability On the other hand it needs to be convenient for application in a universal format A Map Representation In planetary exploration related applications the elevation map is the most common presentation for rough terrain navigation 1 12 13 It is usually handled as a 2 5 dimensional grid map in which each cell holds a height value 14 In our work terrain information is stored in a multi layered grid map as Fig 1 in which a cell is filled with both mechanical and geometrical elements of terrain in different layers In a broad sense our grid map is characterized by a 3 dimensional matrix where Ci j is the cell in i th row and j th column It contains the basic parameters for the geological and mechanical characterization of a terrain Consequently factors in each cell can be represented as Ci j Gi j Mi j Gi j is the geometrical factor represented by height value z for simplicity and Mi j is the mechanical factor associated with the i th row and j th column cell The number of factors in a cell equals the depth of the matrix Other topographical elements can be derived from these parameters directly or indirectly The mechanical properties of terrain are generally classified into bearing properties in the normal direction and shearing properties in the tangential direction It has been proved that heterogeneous terrain shares a unified terramechanical model for wheel terrain interaction and thus can be represented with an identical group of property parameters as long as assigned appropriately 15 Based on an analysis of terrain property model intrinsic terrain parameters are extracted to characterize terrain mechanical properties in terms of bearing and shearing characteristics In order to make flexible adjustments for rovers according to terrain characteristics before traversing the dominant parameters of terramechanical model should be estimated in advance and secondary parameters can be selected based on experience The relatively precise value of terrain mechanical properties is of minor importance while numerically reflecting the sudden change in terrain mechanical properties is more urgent B Dominant Parameters on Terrain Normal Bearing Property Model The bearing property is usually characterized by the pressure sinkage relationship based on plate sinkage experiments A general and high fidelity terrain characterization model with explicit physical representation is proposed in 16 as follow s pK z 1 NsN ppK z 2 where Ks is the stiffness modulus of the terrain in units of Pa m and N is a dimensionless function used to reflect the nonlinear part of the sinkage exponent Here Ksz is in units of Pa and is a straight line with an increasing slope of Ks which is the principle parameter determining the rigidity of the soil The role of parameter N is of minor importance compared with Ks but N is able to modulate the curve of the pressure sinkage relationship around the line Ksz to improve the accuracy of 1 It has been found in 16 that the terrain with sinkage exponents larger than 0 3 may be characterized by 01 ss nn zN pK zK z 3 The parameters of kc k and n from the Bekker model for the 4 types of Mars soil listed in 17 are used to investigate the corresponding Ks n0 and n1 values according to 16 The results are shown in Table I As seen in the table the stiffness of the soil in different categories varies from 134 2 kPa m to 1870 00 10 20 30 4 z m 50 150 250 350 p kPa SS A SS B SS C SS D a 00 10 20 30 4 z m 50 150 250 350 p kPa b maxss KK minss KK 00 10 20 30 4 z m 50 150 250 350 p kPa c 00max nn 00min nn 00 10 20 30 4 z m 50 150 250 350 p kPa d 11max nn 11min nn Figure 2 a bearing property curves of different soil b bearing property change with Ks c bearing property change with n0 d bearing property change with n1 012345 j m 0 0 2 0 4 0 6 0 8 1 kPa SS A SS B SS C SS D a 012345 j m 0 0 2 0 4 0 6 0 8 1 kPa b max cc min cc 012345 j m 0 0 2 0 4 0 6 0 8 1 kPa c max min Figure 3 a shearing property curves of different soil b bearing property change with c c bearing property change with TABLE I SOIL MECHANICAL PROPERTIES Soil samples c Pa Ks kPa m n0 n1 SS A 188 24 8 134 2 0 73 0 97 SS B 441 17 8 829 5 1 07 0 28 SS C 41 25 6 331 9 0 89 0 37 SS D 13 13 4 327 4 1 63 2 17 SS A DLR soil simulant A SS B DLR soil simulant B SS C DLR soil simulant C SS D DLR soil simulant D The bold numbers are extreme values of each parameter 829 5 kPa m Most of the soil samples are of median stiffness which is approximately 331 kPa m The bearing property curves of different soils are drawn according to Table I in Fig 2 a Taking the parameters of DLR soil simulant C as the typical value and extracting the maximum and minimum value of each parameters in Table I different bearing property curves generated by varying Ks n0 and n1 separately are shown in the rest sub figures of Fig 2 These figures show that the change of Ks accounts for the most dramatic fluctuation in supporting force thus Ks accordingly plays a dominant role in the bearing property model To be more reliable the sensitivity of the bearing property parameters is defined to determine the change in the supporting force caused by them The sensitivity of Ks n0 and n1 are calculated in Table II where Se represents sensitivity The calculation results confirm the dominant role of the stiffness parameters again As a consequence Ks is used to compare bearing performance of various terrains intuitively For different terrains we collectively call their respective stiffness Ks the equivalent stiffness modulus C Dominant Parameters on Terrain Tangential Shearing Property Model Rovers on deformable terrain rely on drawbar pull to move forward which is hindered by the tangential shear strength of soil As a result the tangential shearing property is one of the most important factors that affect the traversability of rovers on deformable terrain The shearing property of soil is usually described in the relationship between shear stress and shear displacement Janosi constructed a widely applied model that depicts the relationship in 18 as follows TABLE II PARAMETER SENSITIVITY ANALYSIS Parameters vi1 vi2 ri vd1 vd2 rd Se Ks kPa m 134 2 829 5 5 18 5 20 32 14 5 18 1 00 n0 0 73 1 63 1 23 22 54 0 96 0 95 0 78 n1 0 97 2 17 1 45 31 34 10 42 0 67 0 46 c kPa 13 441 32 92 4 21 4 62 0 10 0 0029 13 4 25 6 0 91 2 10 4 21 1 01 1 10 vi is independent variable vd is dependent variable ri vi1 vi2 vi1 rd vd1 vd2 vd1 Se rd ri The bold numbers are the sensitivity of the most sensitive variables in bearing and shearing properties max 1 j K e 4 max tanc 5 where j is the shearing displacement and K is the shearing deformation modulus The maximum shear stress of soil max is proportional to normal stress and related to soil cohesion c as well as internal friction angle In terms of rigid terrain such as hard rocks microscopic friction angle can be approximated by the macroscopic coefficient of friction as 1 0 tanlim 6 When a rover moves on the Mars surface the shearing interaction works mostly in the saturation zone where the shearing stress is close to max Thus corresponding shearing effect almost depends on two parameters cohesion c and internal friction angle The shearing curves with different parameters under the same normal stress are shown in Fig 3 The parameter can be used to compare the shearing performance of various terrains intuitively and its dominant role is demonstrated in Fig 3 and Table II Therefore in our map representation Mi j is characterized by terrain stiffness modulus Ks and friction modulus in the matrix specifically The grid map model is shown in Fig 1 in which geometric elements can be derived from the slope and aspect and mechanical elements can be used to deduce movement information such as slip ratio and sinkage with a corresponding rover configuration IV PREDICT TERRAIN MECHANICAL PROPERTIES WITH VISION BASED SEMANTIC SEGMENTATION In this section how to predict the dominant terrain mechanical properties based on vision information is 1871 0 i f k k s K p P safe conservativerisky danger i F k Figure 7 The diagram of terrain property inference k 12 T i c cc Visual Mapping terrain reconstruction Terrain map with mechanical properties height layer stiffness layer friction layer raw image series Property Inference Model Terrain Segmentation Model semantic image friction image stiffness image Figure 4 Pipeline of constructing grid map with mechanical properties 1 2 i c c c Figure 5 Schematic diagram of terrain semantic segmantation input output64 128 256 512 40964096 5 convolutional layer pooling layer 512 upsampling layer 8 2 Figure 6 Fully covolutional network architecture for terrain segmentation discussed The key problem lies in building a connection between the terrain class and the corresponding mechanical properties Our method is proposed on the premise that the mechanical properties of terrain vary more significantly across different terrain categories than within categories A Overview An overview of the method for constructing a terrain grid map is shown in Fig 4 which is similar to the pipeline of semantic mapping The approach is decoupled into two processes The visual reconstruction process is stacked to expand the 2 5D map based on the fusion of sensor measurements and rover motion In the meantime the terrain semantic segmentation process conducts pixel level classification on key frames It follows with terra