A Framework for Remote Field Robotics Competitions - IEEE Xplore

2015 IEEE International Conference on Autonomous Robot Systems and Competitions

A Framework for Remote Field Robotics Competitions Gonc¸alo Cabrita, Raj Madhavan and Lino Marques

Abstract— Field robotics competitions usually require a degree of logistics that is out of reach for most of the academic and non-academic community. Nevertheless these events are a source of innovation. This article proposes a framework for remote field robotics competitions, allowing virtually anyone from around the world to participate in a robotics challenge, regardless of resources. This framework, addressed in depth in this article, allows challengers to develop their entries under a simulated environment, and later test their ideas on a state of the art robotics platform, always without ever getting into direct contact with the actual robot. The framework was validated on the Humanitarian Robotics and Automation Technology Challenge (HRATC), an international humanitarian demining competition.

framework developed for this task, which allows HRATC to be what we call a remote challenge. II. RELATED WORK Robotics competitions have been commonplace for several years. Considered by many the most significant challenge to the mobile robotics community in more than a decade, the DARPA Challenge is probably the better known robotics competition [4]. Now entering its 6th edition, the DARPA challenge started as a competition for autonomous cars. Recent instalments included a simulated challenge focusing on humanoid robotics using the Gazebo simulator [5], with a follow up edition of field trials under the same topic. RoboCup is another well known robotics competition that focuses on robotic soccer and rescue [6]. In the context of robotic rescue, researchers have developed a specific simulator called USARSim (Unmanned Search And Rescue Simulator) [7]. USARSim is commonly used by competitors of RoboCup to develop and test their algorithms, one of the main concerns being how to bridge the gap between simulation and reality [8], which naturally poses the question of how to evaluate the simulator’s accuracy [9]. A recent survey shows Gazebo and USARSim among the most popular simulators for robotics research [10]. In [11] the TeamBots simulator is used to simulate a minefield and a set of demining robots. Further into the field of agricultural robotics, a simulator named Simulation Environment for Precision Agriculture Tasks using Robot Fleets (SEARFS) was developed to study and evaluate the execution of agricultural tasks that can be performed by an autonomous fleet of robots [12]. In this case the set of requirements was so specific that the authors decided to develop a whole new solution altogether as opposed to adapting an existing simulator. The Virtual Manufacturing Automation Competition is another USARSim-based competiton designed to propel advancements in the field of automated guided vehicles in the context of manufacturing [13]. The winning team was allowed to run their software on an actial robot. It was able to successfully navigate and dock with a loading area. The Egypt Chapter of the IEEE Robotics and Automation Society initiated in 2012 Minesweepers: Towards a Landmine-free World as the first international outdoor robotic competition on humanitarian demining.1 This is a bring-your-own-robot type of competition, where competitors must travel with their materials and equipment to the venue in order to participate.

I. INTRODUCTION The UN estimates that there are currently over 100 million active mines scattered over 78 countries. It would take over 1000 years to clear the entire world of mines provided that no additional mines are planted. However for every mine cleared, 20 are laid. Landmines kill 15000-20000 people every year (mostly children) and maim countless more. Demining efforts cost US$ 300-1000 per mine, and, for every 5000 mines cleared, one person is killed and two are injured [1]. In order to remove a mine, one must first find it. When looking for buried mines, a high false alarm rate (up to 1000 false alarms per mine found) are commonly triggered. Among the reasons for such a problem are high mineralised soils, harmless metallic objects, and also the low metal content in newer anti-personnel mines [2]. It is hence clear that there is a need for new technologies to improve the rate of detection while decreasing false positives. Robotics is at the forefront of this effort. With this in mind, the IEEE Robotics & Automation Society Special Interest Group on Humanitarian Technology (RAS–SIGHT) decided to invite the academic and non-academic community to participate in a challenge that could potentially gather minds from all over the world with the goal of progressing the state of the art in this area, the Humanitarian Robotics and Automation Technology Challenge (HRATC) [3]. Humanitarian de-mining competitions are not novel, however HRATC employs a framework that allows virtually anyone in the world to have access to a state of the art robotics system designed specifically for the task of mine detection. It is this approach that makes the HRATC unique. This article presents the G. Cabrita and L. Marques are with the Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Portugal. R. Madhavan is with the Institute for Systems Research, University of Maryland, USA.

{goncabrita, lino}@isr.uc.pt, [email protected] 978-1-4673-6991-6/15 $31.00 © 2015 IEEE DOI 10.1109/ICARSC.2015.41

1 http://www.landminefree.org/

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Fig. 1.

The robot used in the challenge.

Fig. 2.

III. HRATC

Screenshot of Gazebo simulating the Husky.

smooth and almost seamless transition. Competitors won’t actually travel to the robot’s location, neither will each team receive a robot. Instead the software developed by each team is remotely uploaded to the robot. The algorithms are then tested by the organisation of the challenge. Each team is able to retrieve a full dataset and footage of each trial for analysis and the algorithms can be improved week after week.

The Humanitarian Robotics and Automation Technology Challenge is a humanitarian demining robotics competition. Its goal is to push the boundaries of what technology can accomplish in this field. We need to think outside the box if we are going to come up with something really new, and sometimes the best ideas come from the most unexpected places. This is why HRATC gives the opportunity for virtually anyone with a computer to participate in the challenge. All competitors are able to develop and test their ideas under a simulated environment, followed by a set of field trials on a state of the art mine detection robot, something very few people would otherwise have access to, all without leaving home. The entire competition is performed and broadcasted remotely, without any of the competitors ever getting in actual contact with the real robot. The event is thus divided into three stages, (i) the simulation stage, (ii) the field trials stage and (iii) competition day.

C. Competition Day Competition day marks the end of the HRATC. The final stage is broadcasted live on the internet from Coimbra, Portugal. Each team is given two runs on a minefield using surrogate mines and false positives. The score is based on a set of metrics that will be discussed further on. IV. THE ROBOT All teams use the same robot throughout the competition (either real or simulated), a custom Clearpath Husky A200 (Figure 1), equipped with a wide range of sensors, designed with mine detection in mind. The robot itself had a great impact on designing the challenge framework described in section V, and is thus here briefly presented. A stereo pair of Point Grey Flea3 GigE cameras and a SICK TiM 551 laser range finder provide all the perception required for obstacle avoidance and navigation. The SICK laser is mounted on a custom tilt unit that allows the laser to generate 3D point clouds. The localisation of the Husky is achieved by means of an RTK GPS, an XSens MTi300 IMU and odometry data. The cameras, laser and GPS antenna are mounted on a bridge. Communication inside the robot is accomplished using a Netgear GigE Switch. An Alpha Tube2H wireless access point is in charge of communication to the outside world. At the heart of the Husky is a Gigabyte BRIX computer with an Intel Core i73537U processor running at 3.1 GHz, 8 GB of 1600 MHz DDR3 RAM and a 60 GB SSD. Mine detection is achieved using a Vallon VMP3 threecoil, pulse induction metal detector.

A. Simulation Stage The simulation stage is the first step on the HRATC. Competitors are able to download a simulator that mimics both the hardware and the environment that will be used for the next 2 stages. This allows the teams to focus on their ideas and to develop their algorithms without having to worry about how their software will translate into the real robot. It also allows them to focus on the actual problem, humanitarian demining, without wasting time with several common but extremely difficult robotics problems such as navigation and localisation. B. Field Trials Stage During this stage each team will be able to run the software developed on the simulator on the actual robot. Each team is allowed 3 field trials, throughout a 3 week window. The goal is to give each team a feel of what working with the real robot is like as opposed to working under a simulated environment. Nevertheless the framework should provide a

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2 4

3 1

Fig. 3.

The robot used in the challenge.

data, working with the arm, navigation and communicating detected mines for scoring purposes. The whole framework is built on top of Robot Operating System2 (ROS). ROS is an open-source, meta-operating system for robotics. It provides the services anyone would expect from an operating system, including hardware abstraction, low-level device control, implementation of commonlyused functionality, message-passing between processes, and package management [14]. It was chosen due to the fact that it has become very popular and widespread in the last few years. Furthermore it already integrates several state of the art robotics algorithms out-of-the-box, which teams can use to their advantage while focusing on humanitarian demining. Using a standard such as ROS means that everyone speaks the same language, including the team in charge of the field trials. This is a key factor in facilitating the transition of each team’s algorithm from the simulation stage to the field trials stage. The HRATC 2015 framework is organised according to Figure 4. Simulation is achieved using Gazebo,3 a simulator that makes it possible to rapidly test algorithms using realistic outdoor scenarios. Gazebo uses a robust physics engine, high-quality graphics, and programmatic and graphical interfaces [15]. Gazebo is also open-source and comes installed with ROS. A screenshot of Gazebo running the HRATC framework simulation can be seen in Figure 2. Gazebo’s capabilities are expanded using two plugins that will be discussed in the following two subsections V-A and V-B. The HRATC judge deals with scoring metrics and visualisation. Finally a robot localisation and 3D point cloud reconstruction modules are provided to help challengers avoid dwelling into common robotic problems, allowing

A. The Metal Detector Arm The metal detector is mounted on a 2DOF robotic arm (Figure 3). The arm was designed in a parallelogram scheme, assuring that the metal detector antenna (1) is parallel to the platform’s base at all times, and consequently to the ground if its surface is planar. The height of the metal detector antenna can be set using a linear actuator (2). Sweeping the antenna from side to side is accomplished using a servo motor (3) that is connected to the arm’s shaft using a belt that allows slippage. This mechanism acts as a clutch so if the arm bumps into obstacles the motor’s gear box will not be damaged. Also on this note, an air shock (4) is connected in-line with the linear actuator to allow some compliance should the metal detector sensing head hit the ground or any obstacle. Both actuators use position control, although the arms velocity sweeping is adjusted in order to provide smooth accelerations at the trajectory extremes. The arm’s position is measured through absolute encoders coupled to each of its joints. The two tubes used for the arm are made of woven carbon fibre and kevlar. All remaining parts are made of 3D printed polyamide and held together using teflon screws and epoxy resin. This minimizes the metal content near the metal detector coils, which would greatly degrade the metal detector readings, while at the same time not sacrificing rigidity. V. THE HRATC 2015 FRAMEWORK The HRATC 2015 framework is a key factor in making this remote challenge feasible. This software package lays the ground for challengers to develop their own software, which will later be executed on a robot at a remote location. The framework also counts with an online tutorial and an extensive FAQs section. Furthermore the framework is paired with a set of examples to help jump-start software development. Examples include help with accessing metal detector

2 http://www.ros.org/ 3 http://gazebosim.org/

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Metal Detector Simulator

arm_base

Mimic Joint

arm_axel_joint / revolute

arm_axel

Gazebo

Rviz Visualisation

lower_arm_joint / revolute mimic: upper_arm_joint

upper_arm_joint / revolute

lower_arm

upper_arm

ROS Ecosystem

metal_detector_axel_joint / revolute mimic: upper_arm_joint, multiplier: -1

metal_detector_axel

HRATC Judge

EKF Localisation

3D Point Cloud Reconstruction

metal_detector_antenna_joint / fixed

metal_detector_antenna

Fig. 4.

The HRATC framework block diagram. Fig. 5. arm.

Diagram of the links and joints structure for the metal detector

them to spend more energy on the problem of humanitarian demining in particular. A. The Simulated Arm

d

y

The transformation tree rules in ROS state that a certain link might have several children, however it can only have one parent link. This presents a problem in representing the arm in section IV-A due to the fact that it is, in its essence, a parallelogram. Figure 5 shows the representation of the arm links and joints in URDF (Unified Robot Description Format) in ROS. Notice that no link has more than one parent as required. As a workaround joints lower arm joint and metal detector axel joint are defined as mimic joints. This means that these joints will (as the name states) mimic another joint, in this case the upper arm joint. Notice that metal detector axel joint is actually mirroring and not mimicking the upper arm joint, hence the −1 multiplier attribute. In this scheme the active joints are the arm axel joint for sweeping the arm and the upper arm joint for lifting or lowering the metal detector antenna. This is a simplification, as the linear actuator is not considered in the URDF of the arm. This means that both the simulated hardware and the real arm hardware drivers receive two position command values in radians. In the real hardware driver this value is converted to a linear actuator command and forwarded to the device. The linear actuator 3D model is nevertheless part of the lower arm link model for self-filtering purposes as discussed further ahead in section V-D. The mimic parameter is not, however, available in Gazebo. For this reason we developed a mimic plugin for Gazebo that is distributed with the HRATC framework and that adds this feature to the simulator, allowing the metal detector arm to work under the simulated environment. It should be noticed that compliance of the arm is not simulated at this time. This corresponds to assuming that both the air shock and the transmission belt arm axel joint are locked at all times during a simulation.

z

hs hd

x

z Fig. 6.

Relevant measurements for modelling the f function.

B. The Metal Detector Simulator Gazebo is able to simulate a wide range of sensors, however, a metal detector is not one of them. Simulating a metal detector can be quite challenging. For that reason we decided to use a different approach to the problem. The idea is to use a dataset of a the real metal detector (in this case the Vallon VMP3) to teach the simulator how to reproduce this data under a simulated environment. A regression SVM is used to generate a function f (equation 1), where cn is the metal detector coil output for channel n (each coil can have multiple channels), d is the distance between the coil center and the metal sample projected on the xy plane, hs is the height of the coil sensor above the ground level, hd is the depth at which the metal sample is buried and k is the type of mine (or false positive) that is being mapped by f . These values are depicted in Figure 6. cn = fn,k (d, hs , hd )

(1)

This means that for each type of metal sample in the dataset there will be n functions f , one for each channel. Adding a new type of metal sample does not imply retraining the whole system. The following assumptions were made when designing this simulator: • All metal detector coils have the same behaviour; • All metal samples are round (so that the sample pose does not affect the readings);

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Algorithm 1: The pseudocode for the metal detector simulator algorithm. while true do if new coil pose is available then d, mine = nearestMine(coil pose); for each coil channel do if d