CHARM: A Platform for Algorithmic Robotics Education & Research

Transcription

1 CHARM: A Platfom fo Algoithmic Robotics Education & Reseach Suya P. N. Singh 1, Hanna Kuniawati 2, Kianoosh Soltani Naveh 3, Joshua Song 4, and Tyson Zastow 5 Abstact This pape intoduces autonomous soting of moving coins via a obot as a multi-level task that suppots the pincipled study of obotics fundamentals including kinematics, dynamics, peception, motion planning, contols, and optimization based aound a widely obtainable, standadized, low-cost object (a coin). The pape also pesents a demonstated solution to this in the fom of the CHARM (Coin Handling Am fo Robotics Mastey) obot, which addesses the autonomous coin soting poblem using an economical kit made fom commodity computing hadwae and thee Dynamixel sevomotos. Fom a leaning pespective, this poblem facilitates intedisciplinay pactice acoss subject and gade levels with an algoithmic foundation that is cental to moden obotics. Evidence suppoting this appoach is illustated fom case studies of student pojects and, in paticula, the CHARM obot. Beyond pactice alone, by pesenting a challenging (but manageable) eseach poblem, we found that the coin soting task teaches obotics in a pincipled way. Futhe, algoithmic complexity ties the poblem to academic levels. While motivated by obotics education, the (optimal) coin soting poblem may also be seen as an achetype poblem fo manipulation/motion-planning eseach. Thus, this also pomotes a eseach foundation suppoting late eseach oppotunities. I. INTRODUCTION Not only is obotics an inceasingly pevalent and an impotant pat of eveyday life that captivates the imagination, but it is an inheently fascinating subject. Fom US FIRST to luna oves to new gaduate pogams in obotics, inteest in the subject has inceased both populaly and academically. This, in tun, has enewed inteest in intoductoy obotics couses, paticulaly at the mezzanine level. Fom a leaning pespective such couses offe an oppotunity to intoduce systems engineeing concepts and to integate knowledge acoss multiple disciplines and topics. Fom a teaching pespective, these couses attact highly motived and engaged students due to the geneal enthusiasm fo the subject. While such excitement is helpful, the applied natue and geneal expectation of obotics often implies inteest in new mateial and moden esults. Compaed to the significant attention paid to cuiculum and leaning This wok was suppoted by the Mechatonics Pogam and the Robotics Design Lab (RDL) at The Univesity of Queensland (UQ), Bisbane, Queensland 4072, Austalia 1 Suya P. N. Singh (coesponding autho) is RDL and Mechatonic Pogam diecto and coodinates METR4202 (Advanced Contols and Robotics), spns@uq.edu.au 2 Hanna Kuniawati is with the RDL and coodinates COMP3702 (Atificial Intelligence), hannaku@uq.edu.au 3 Kianoosh Soltani Naveh is a Mechatonic Engineeing student in the UQ RDL, k.soltaninaveh@uq.edu.au 4 Joshua Song is a Mechatonic Engineeing student in the UQ RDL, mun.song@uqconnect.edu.au 5 Tyson Zastow is a Mechatonic Engineeing gaduate, tysonzastow@gmail.com Fig. 1. The CHARM obot autonomously sots coins andomly placed on its tunable. The obot involves seveal algoithmic pinciples including object ecognition, kinematics/contol, and motion planning in dynamic envionments development within paticula subdisciplines, the design and emphasis fo (intoductoy) obotics couses as a gestalt has eceived less attention [1], [2]. In pat, this is attibutable to the intedisciplinay and expanding natue of obotics, which has gown fom aticulated seial kinematics chains to mobile systems with integated sensing and contol. Both the beadth of mateial and the (elatively) shot couse peiods suggest the need fo a caeful stuctuing of such couses [3]. Robotics laboatoies spans the gamut fom couses that use obotics to suppot couse topics to the entie at of obotics. Thus, many obotics kits ae focused on teaching closely elated subjects such as pogamming [4], dynamics and contols [5], [6], and mechatonics [7] moe so than to the pinciples of obotics. Hence thee is a need fo laboatoy designs with a focus on algoithmic pinciples that enables students to navigate obotics eseach esults and to apply these methods. We delineated the algoithmic aeas in obotics using the Robotics and Automation Society s pogam stuctue as a guide. Fom this we identified algoithmic aeas as motion planning, peception, kinematics, mapping, machine leaning, contol, and systems. The poblem of autonomously soting moving coins (see Fig. 1) addesses the above impetus in a step-wise manne that allows teams of students to study these pinciples at inceasing levels of complexity so as to match thei level of leaning and engagement [8], [9]. While coin soting mechanisms abound, the sub-tasks involved pesent seveal challenges including vision [10] and contol [11], especially fo autonomous opeations. Moe geneally, the coin soting task has subpoblems that involve each of the afoementioned algoithmic aeas end-effecto position placement, velocity contol (Jacobeans), object ecognition, obstacle avoidance, system identification, autonomous opeation, and undeactuated (satuated) contols.

2 In this pape we also intoduce a candidate system in the fom of the CHARM (Coin Handling Am fo Robotics Mastey) obot (pictued in Fig. 1). It is a obotic platfom with a camea, obot am and a wokstation that allows students to exploe diffeent topics of obotics and mechatonics engineeing such as compute vision, motion planning and mechanics fom elementay to advanced. An example with novel motion planning, peception and kinematics is demonstated to highlight its educational capabilities. The emainde of this pape is stuctued as follows. In Sec. II, we detail the autonomous dynamic coin soting poblem and show that it is, fom an algoithmic pespective, at least P-space had. The design of the CHARM obot and how it addesses these algoithmic aspects is detailed in Sec. III. A discussion of this fom algoithmic obotics education pespective is in Sec. IV. II. THE COIN SORTING PROBLEM The basic coin soting poblem is designed fo mezzanine (i.e., uppe undegaduate and beginning gaduate) students. It is about moving coins to an appopiate bin, in the sense that all coins in the same bin must be of the same type. We add a twist to the poblem to make it moe inteesting: the coins ae placed on a otating table (to which thee can also be othe objects/obstacles placed aound it). Let us assume we have n coins of k diffeent types. Suppose each coin is placed on top of a tuning table that otates with an angula velocity (which is typically, but not necessaily, constant) and suppose k empty bins ae placed ight outside of the tuning table (see Fig. 2). The goal is to move each coin fom its initial position to one of the bins, such that all coins inside a bin belong to the same type. The poblem ends when no moe coins ae on the otating table. The poblem is solved when it ends with all bins having the same type of coins. To solve this poblem, students wee given thee low-cost motos and a camea. This coin soting poblem spus study in at least fou obotics aeas: 1) Hadwae design. At the vey least, students must be able to design and build a simple 3-DOF am that can push a coin. In addition, the basic coin soting poblem teaches the tade-off between hadwae design and algoithmic difficulty. Fo instance, to solve the poblem, one can build an abitay shake, which is easy to built. Howeve, such a shake would make the pocess eo pone and stochastic, especially when the shake is contolled by impecise, satuated motos (as is sometimes the case with low-cost motos). 2) Peception. The system must be able to identify, diffeentiate, and tacks multiple moving objects. While coins might be a standad object, vaiations in lighting and coin wea/quality additionally highlight (and teach) the challenges in developing obust sensing techniques. Since the types of coins can be inceased easily, the coin soting poblem also teaches the challenges in scaling vaious vision and/o object classification techniques. 3) Motion Planning. The motion planning component of the coin soting poblem can be defined moe geneally as follows. Given n closed and plana objects on a 2D Euclidean plane, whee all objects obit with the same constant angula velocity aound a common baycente. Let g i R 2 be the goal egion of object-i fo i [1, n], and let each goal egion acts as a sink, in the sense no object can leave the egion. Suppose the initial position of each object is known and is within a finite distance fom the baycente. Then, we want to find a continuous path that moves each object-i fom its initial position to its goal egion g i (i [1, n]) without colliding with any othe object. This poblem is not just complex, it is at least PSPACE had, as the poblem of motion planning with movable obstacles whee the obstacles must end at a pe-specified location a PSPACE had poblem is a special case of the above poblem [12]. 4) Estimation. The use of low-cost motos equie students to be able to estimate and filte out eos when contolling the obot. Futhemoe, to pefom well, students must also estimate the angula speed of the tuning table. Although this estimation can be done easily by placing makes on the tun table, the ability to identify the need fo such estimation and the use of such makes ae skills that could come handy in developing moe complicated obotics system. The afoementioned list is not fixed. Depending on the systems design, some poblems may be moe pominent than othes, and additional poblems may occu. Futhemoe, aside fom vaying levels of difficulty in each sub-field, the coin soting poblem also povides the oppotunity to study the inteplay between vaious afoementioned components, such as between peception and motion-planning. That is, fo example, cetain camea (senso) placements may educe occlusions (simplifying peception), but may not allow as encompassing a view (complicating planning/contol). The coin soting poblem can be extended o simplified easily to bette cate to vaious student and/o leaning equiements. Fo instance, it could be simplified by having a stationay table and diect sensing of the taget and its location (e.g., augmenting the coin/object with a magnet fo easie detection and/o to incopoate switches, encodes, o inductive sensos unde the table to simplify object localization). Contol pefomance can be simplified, fo example, by poviding lage bins (elative the the taget size). Similaly, the poblem may be adapted to be moe challenging o igoous by consideing vaiations of (algoithmic) complexity. Fo instance: Adding 2.5D obstacles that moves on the otating table This adds significant difficulty in peception, planning, and estimation. Intoducing advesity to the system (e.g., an opponent that competes fo the coins) This would allow fo the incopoation of game theoy and/o AI stategies. Having a time optimal solution This is still an open eseach poblem.

3 III. CHARM ROBOT The CHARM (Coin Handling Am fo Robotics Mastey) obot is a coin soting system whose novel featue is its ability to slide coins on a tuntable without eve colliding othe coins on the table. It is a candidate solution to the Coing Soting poblem. Its design demonstates the featues of an algoithmic appoach to in obotics laboatoies. CHARMs diffeent hadwae pats ae shown in Fig. 2. The obot uses a camea mounted on the top ovelooking the wokspace. The wokspace includes a spinning table with a numbe of diffeent coins to be soted. The spinning table featues a make made of thee cicles ed, blue and geen espectively. The obot am is made of thee Dynamixel sevomotos (Robotis, Seoul, Koea) and is mounted to the side. This am touches the table using an extension that allows the am to slide the coins. The am is contolled by softwae on a PC. The softwae which is witten in C++ can be divided to thee main modules: 1) Detection and Classification, 2) Motion planning, and 3) Kinematics. The detection and classification module uses OpenCV to pocess image fames obtained fom the camea and identify and locate coins. The motion planning and kinematics module uses the findings of detection module to plan the tajectoy necessay to slide the coin to its espective bin without colliding with the est of the coins. A. Robot Am Kinematics and Dynamics The obot am consists of thee Dynamixel sevos at each joint giving an RRR am. Kinematically, the obot am is positioned in the plane of tun table. The thid joint then allows the am to touch the coins and slide them (touch down motion). Fo this eason, the kinematics analysis of plana motion and touch down motion can be caied out sepaately. In the kinematics analysis the coodinates wee chosen such that x y epesent the tun table coodinate and z the nomal axis as shown in Fig. 3. Fom Fig. 4, the x y plane fowad kinematics equations ae equivalent to that of an RR am: [ [ ] x L1 cos θ = 1 + L 2 cos (θ 1 + θ 2 ) (1) y] L 1 sin θ 1 + L 2 sin (θ 1 + θ 2 ) The invese kinematics equations ae deived using geometical popeties of tiangle: 2 = L L L 1 L 2 cos θ 2 (2) ( 2 L 2 1 L 2 ) 2 θ 2 = acos (3) 2L 1 L 2 ( y θ 1 + α = atan (4) x) sin α = L 2 sin θ 2 (5) $1 $2 Am 5 Camea Fig. 2. Oveall system layout of the CHARM obot showing the coins and tuntable adius () elative to bins and the camea and am locations y α L 1 x θ 1 L 2 z y x Fig. 4. x Fig. 3. α θ 1 L 1 L 2 The touch down motion is along the z axis (see Fig. 4). This motion equies thedthid 1 joint to otate a specific angle so that the tip of the am touches the suface of the table. As this joint bends the tip of the am, the adial distance of θ d the tip is affected. This is shown in the following analysis: 2 3 h z h = d 2 sin θ 3 (6) ( ) h x θ 3 L= 2 asin (7) θ 2 θ 2 Top view of the CHARM kinematics d 1 L 2 h θ 3 d 2 Side (touch down) view of the CHARM kinematics d 2 L 2 = d 1 + d 2 cos θ 3 (8) In addition to positioning the am and eaching the coins, velocity contol was citical to the coect opeation of the obot, as it would need to be able to intecept the moving tagets (i.e., the coins on tuning table). To implement the velocity contol of the obot am a tajectoy with time stamps at each point is geneated by kinematics module and each point is sent as a position command to the sevo. This method allowed an implicit setting of the velocity while minimizing the communication between PC and sevos.

4 B. Dynamics of the Tun Table The tun table consists of a disk on a continuous otation Plolu sevo. The tun table s speed can be contolled via a GUI based pogam on PC. It is also possible to wite pogams to contol the speed of the tun table if necessay. The tun table foms the main pat of the wokspace with the option to put tagets o objects on the suface and use the am to manipulate those object. It also featues special makes that allow the camea detect the position, distance and oientation of efeence fame of the wokspace. Moe details ae given in the next section. C. Peception The detection system uses a Lifecam Studio (Micosoft, Redmond, Washington) in collaboation with the OpenCV toolkit. The camea is mounted above the am facing the otating tuntable; this gives an oblique view of the table suface which helps to avoid eoneous detection of spheical o cylindical objects which can be mistaken fo coins when viewing the table fom diectly above. The coin detection algoithm uses edge detection and ellipse fitting to detect elliptical shaped objects on the table suface. Fistly the image is undistoted using pe-calculated camea calibation paametes, and then a Canny edge detecto is used to find all edges in the scene. These edges ae subsequently tuned into OpenCV contous (i.e., vectoized edges), and then boken into individual cuve segments based on the change in cuvatue of the contous [13]. Then an ellipse is fitted to each cuve based on the points in that cuve and the fitting eo is calculated, any ellipses with high eos ae discaded. The emaining ellipses ae then consolidated by seaching fo and emoving duplicates (i.e., ellipses with vey simila paametes). The next step is identifying the tuntable, this is done via concentic cicles pinted on the table. Cicula makes wee chosen as they can be detected using the same technique as finding coins. The list of ellipses is seached fo concentic ellipses with elative sizes that match that of the cente table make, once that is found the thee diectional makes ae then identified using a simila concentic seach with the addition of colou sampling at the cente of the ellipse. Once the tuntables position elative to the camea is established a mask can be employed to ignoe any detected ellipses outside the tables suface as well as ignoing ellipses that have a skew diffeent to that of the table suface. Any emaining ellipses must be coins (o coin shaped objects) on the table. The detected coins ae then classified based on colou and size. Colou classification uses a histogam of the coins pixels in the YCbC colou space and compaes this to a taining set histogam fo both gold and silve coins. The match eo fo gold and silve is calculated using the Chi- Squae method povided by OpenCV and ecoded. The eal wold size of the ellipse is calculated using the distance to the table (calculated fom the tables makes) and the focal length of the camea. This size is then compaed to evey actual Austalian coin size and the eo squaed is ecoded fo each. The ellipses position elative to the otating table is then calculated and stoed with all the calculated eo values. This entie pocess is epeated fo an abitay numbe of fames, when new ellipses ae detected thei table position is compaed to pevious ellipse table position to see if it is the same coin as pevious detected, if it is then its eo values can be collated with pevious matches to that coin to povide moe data fo classification. In this way classification can be impoved by allowing moe fames (i.e., moe data) to be collected. When sufficient fames have been captued (we used 20) classification of the detected coins takes place; fist ellipses that have been detected in less than half the captued fames ae discaded as they ae unlikely to be actual coins, then the value of the emain coins ae detemined by finding the coin type with the minimum combined colou and size eo. We found size to be a moe accuately distinguishable featue and as a esult weighted its eo values 3 times highe than colou eos. Also note that at this point ellipses with minimum match eos above a cetain theshold wee also discaded as the confidence of the match was simply too low. Figue 5 shows the esults of the coin detection and classification. Fig. 5. D. Motion Planning Coin detection and classification The motion planning poblem can be descibed as multigoal motion planning in a known dynamic envionment. Although the coins act as moving obstacles, thei path and velocity can be estimated fom the video camea input. The fist pat of the poblem is deciding the sequence in which coins ae soted. This is simila to the classic tavelling salesman poblem, except the possible paths between each coin and its espective bin vay ove time. To solve this, a geedy appoach was used whee paths ae found each coin to its bin, and one path (the shotest) is chosen. The futue positions fo the emaining coins is then estimated, and the pocess is epeated until all coins ae soted. The second pat poblem is the path finding fo getting a coin to the bin. The algoithm adopted was a gid-based A* seach, with staight-line distance to the goal as the heuistic. This was chosen ove pobabilistic oadmap methods as it is less computationally intensive. The gid is 3-dimensional, consisting of x and y coodinates as well as time. The seach tee begins at the coin s stating position and an estimated stat time. A bidiectional seach is not possible as the time of aival at the goal is unknown until the path is found.

5 As each point is exploed, the time at the point is estimated based on the path length divided by end-effecto velocity. This time is then used to estimate the obstacle positions. A simulation esult of the geneated seach tee is shown in Fig. 6. The points exploed duing path finding ae shown as geen dots, and the positions of the obstacles ove time ae shown as ed cicles. Once the paths have been found, the planne sends the tajectoy, which consists of time-stamped coodinates, to the am contolle which pefoms the invese kinematics. Fig. 6. A sample motion planning tajectoy IV. DISCUSSION TOWARDS ALGORITHMIC ROBOTICS EDUCATION As demonstated by the CHARM obot, the coin soting task pesents seveal integated challenges. While it is quite feasible to solve the poblem using Lego kits (as was done by othe student teams), the use of a simila (in pice and numbe of degees of feedom) kit based on thee Dynamixels was moe flexible, but is moe complicated to develop. The couse and poject wee assessed using couse feedback suveys. Compaed to the pevious yea the couse was administeed, the couse showed a slight incease in how well the mateials helped them with the leaning (fom 75% of students in ageement to 91% of students in ageement). Oveall couse maks ae also high (4.81/5, with 100% of students ating the couse as satisfactoy, the stongest esponse fo a class in the Mechatonics pogam). A. Robotics to Suppot Related Studies As a capstone subject, obotics integates knowledge acoss multiple disciplines and topics. This highly positive and attactive chaacteistic makes it well suited to studying systems engineeing. Fom a teaching pespective, obotics couses attact highly motived and engaged students due to the geneal enthusiasm fo the subject. Robots have been found effective in engaging and einfocing student leaning not only in obotics classes but also as a geneal leaning tool to help students undestand physical and mathematical concepts such as geomety and kinematics [7]. Theefoe it has been an appaent choice fo teaching obotics and mechatonics. In doing so, many instuctos have used LEGO Mindstom as the pefeed obotics kit due to simplicity, eusability and ease of pototyping [14]. These kits have allowed instuctos teach the concepts of diect and invese kinematics and computation of simple am tajectoies. Howeve limitations imposed by LEGO Mindstom softwae and inteface have also been epoted. These limitations and the weakness of labs in teaching obotic algoithms such as vision and motion planning seem to coelate. Rosenblatt et al. [15] have epoted, in designing lab assignments fo a obotics couse in Canegie Mellon Univesity, they used pats of LEGO Mindstom kit in combination with thei assoted sensos and contolles to let students come up with moe ceative solutions. Howeve the labs do not povide a unified poblem and theefoe each equies diffeent setup. Othe obotic kits such as Pendubot have allowed students implement theoies in the labs on one setup, but due to the natue of the obots the topics become limited to specific aeas such as contol and systems [16]. On the othe hand, mobile obots such as e-puc obot, Roomba and many othes have tied to povide one platfom fo leaning diffeent obotics concepts in signal pocessing, contol and distibuted intelligent systems [4]. Howeve, these applications do not fom a well stuctued obotics poblem and most of them do not suppot compute vision. Moeove, taking into account the numbe of kits needed fo a (lage) class, the wokspace and expense may become limitations. B. Algoithmic Robotics Cuiculum Robotics, in paticula algoithmic obotics, is a apidly developing field. Theefoe, an algoithmic obotics class must teach students the fundamental concepts and necessay skills to undestand and apply esults fom the state-of-theat. A metic fo such leaning outcome would be students ability to undestand the digest of a majo obotics confeence (e.g., IEEE/RSJ Intenational Confeence on Intelligent Robots and Systems (IROS)). The cuiculum stuctue would be based on the majo science themes as identified in the aea and technical committee divisions that ae used as pat of the editoial pocess at majo confeences with cae to sepaate the technical ( science ) aeas (e.g., contol, peception, leaning, and planning) and application ( system ) domains (e.g., medical and life sciences, industial obotics and automation, field obotics, etc.). To achieve the above goal, leaning should be focused on the ideas and undelying concepts behind the methods, athe than the methods pe se. Fo example in motion planning, it is possible to use a bevy of methods to solve the poblem of moving object - moving obstacle poblem, including potential fields, apidly exploing andom tee (RRT) methods, etc. Howeve, instead of only exposing students to such methods, we need to expose them to the undelying pinciples and ideas, such that students not only

6 able to use these methods, but even moe impotantly, able to use the methods appopiately, selecting the ight method fo the ight poblem and undestanding why the paticula method is appopiate. C. Leaning Goals CHARM aims to povide a obotic platfom that allows undegaduate and postgaduate students exploe diffeent concepts in engineeing. This obot not only ties to accommodate simple applications such as fowad and invese kinematics but also ties to povide students with a platfom on which velocity contol of obot am is possible. This enables students to conduct motion planning in dynamic envionment as opposed to envionments with static tagets Compute vision utilities of the obot allow students to pactice peception, localization and velocity estimation of tagets on wokspace. This opens up moe possible aeas fo students to exploe. The kinematic featues of the obot am along with compute vision utility allow motion planning on up to a 7- dimensional with moving tagets on wokspace. These stong featues of CHARM let students use this obot to exploe motion planning, vision and kinematics fom elementay to advanced. Theefoe the most impotant goal in this poject is to povide students with a platfom that does not involve the limitations posed by othe kits. The leaning by doing also allows fo team pojects allowing students fom vaious disciplines to wok togethe and develop team wok qualities such as communication, collaboation and leadeship. V. CONCLUSION The evolution of obotics and automation has esulted in the gowing significance of taining and educating the futue enginees, eseaches and technicians with a good gasp of mechatonic systems. Many of the topics in this aea can be masteed though leaning by doing. Robots ae natually one of the main tools in teaching obotics and mechatonics and they play an inceasingly impotant ole in education fom high school to postgaduate studies. Thei application does not stop at obotics education but also extends to othe aeas such as mathematics and physics. Fo this eason development of suitable obotic kits can pove to be vey useful fo students inteested in obotics. In summay, the CHARM obot platfom has been developed as an example of a flexible, yet systematic, leaning and eseach tool in the study of obotics concepts including obot am kinematics, motion planning, and compute vision. The obot platfom aims to alleviate the limitations in othe kits by poviding velocity contol in obot am, compute vision and testbed that allows fo implementing motion planning concepts in time vaying envionments with a vey good quality. Theefoe we believe this obot platfom will seve as a moe suitable leaning tool fo undegaduate and postgaduate univesity students by allowing innovation and leaning by doing what is appopiate fo univesity level students. 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Fioini, Innovative obotics teaching using lego sets, in Robotics and Automation, ICRA Poceedings 2006 IEEE Intenational Confeence on. IEEE, 2006, pp [15] M. Rosenblatt and H. Choset, Designing and implementing handson obotics labs, Intelligent Systems and Thei Applications, IEEE, vol. 15, no. 6, pp , [16] A. G. Alleyne, D. J. Block, S. P. Meyn, W. R. Pekins, and M. W. Spong, An intedisciplinay, intedepatmental contol systems laboatoy, Contol Systems, IEEE, vol. 25, no. 1, pp , VI. APPENDIX 1: VIDEO ATTACHMENT A video of the CHARM obot soting coins has been submitted as pat of this pape. It is also online at: http: //youtu.be/hh5_9t7lphq.