Development of a control architecture for the ROBUDEM outdoor mobile robot platform

Humanitarian demining still is a highly labor-intensive and high-risk operation. Advanced sensors and mechanical aids can significantly reduce the demining time. In this context, it is the aim to develop a humanitarian demining mobile robot which is able to scan a minefield semi-automatically. This paper discusses the development of a control scheme for such a semi-autonomous mobile robot for humanitarian demining. This process requires the careful consideration and integration of multiple aspects: sensors and sensor data fusion, design of a control and software architecture, design of a path planning algorithm and robot control. Introduction The goal of this research project is to prepare the ROBUDEM, an outdoor mobile robot platform as shown on Figure 1, for a humanitarian demining application. In this setup, the robot navigates and searches for mines by moving and sensing with the metal detector for suspicious objects in the soil. Once a suspicious object is detected, the robot stops and invokes its Cartesian scanning mechanism. This scanning mechanism performs a 2D scan of the soil, allowing mine imaging tools to make a reliable classification of the suspicious object as a mine or not. This paper describes partial aspects of this research work and focuses mainly on the design of the control and software architecture. A goal for the future is to implement an existing cognitive approach for mobile robot navigation on the mobile robotic platform. This will allow the robot to scan a suspected minefield semiautonomously and return a map with locations of suspected mines. The development of such an intelligent mobile robot requires consideration of different side-aspects. Robots use sensors to perceive the environment. Sensors under consideration for this research work are ultrasonic sensors, a laser range scanner, a stereo camera system, an inertial measurement system, a GPS receiver and of course a metal detector. All but the last one of these sensors return positional and perceptual information about the surroundings. This sensor data has to be fused in a correct way to form a coherent “image” of the environment. If a robot needs to gain a more or less complete “image” of its environment, it cannot rely on only one type of sensor. Hence the need for an intelligent sensor fusion algorithm to combine the often erratic, incomplete and conflicting readings received by the different sensors, to form a reliable model of the surroundings. Sensor fusion has been subject to a lot of research [1][4], most of the proposed methods use Kalman Filtering [17] and Bayesian reasoning [15]. However, in recent years, there has been a tendency to make more and more use of soft computing techniques such as artificial neural networks [8] and fuzzy logic for dealing with sensor fusion. [3][6]. An autonomous mobile agent needs to reason with perceptual and positional data in order to navigate safely in a complex human-centered environment with multiple dynamic objects. This translation of sensory data into motor commands is handled by the robot navigation controller. Its design is closely related to the design of the control architecture which describes the general strategy for combining the different building blocks. The basis for this reasoning process is often a map, which represents a model of the environment. These maps can be simple grid maps, topological maps [7], or integrated methods [16]. The used path planning technique depends highly upon the type of map chosen before. A survey of different methods can be found in [5].The goal of this research is to use a behaviour-based control architecture to navigate while modeling (mapping) the environment in 3 dimensions, using vision as a primary sensing modality. The control architecture has to be translated into a software architecture which manages the building blocks on a software level. This software architecture has to provide the flexibility of modular design while retaining a thorough structure, enabling an easy design process. All the different processes (sensor measurements, measurement processing, sensor fusion, map building, path planning, task execution ...) must be coordinated in an efficient way in order to allow accomplish a higher goal [2]. A number of control strategies can be set up, varying from simple serial sense-model-plan-act strategies to complex hybrid methods. A discussion of some of these control strategies can be found in [13]. An interesting approach here, is to use fuzzy behaviours, partially overriding each other, to build up complex navigation plans, as discussed in [9][10][11][12]. This research work aims at implementing such a hybrid control strategy. During the design of all these sub-aspects, the outdoor nature of the robot has to be taken into account. Outdoor robots face special difficulties compared to their indoor counterparts. These include totally uncontrolled environments, changing illumination, thermal, wind and solar conditions, uneven and tough terrain, rain, ... The rest of this paper is organized as follows: The control strategy and architecture are described in section 2, the software architecture is summarized in section 3 and finally, conclusions are given in section 4. Figure 1: ROBUDEM robot with scanning mechanism Control Architecture The control architecture describes the strategy to combine the three main capabilities of an intelligent mobile agent: sensing, reasoning (intelligence) and actuation. These three capabilities have to be integrated in a coherent framework in order for the mobile agent to perform a certain task adequately. The working principle of the proposed control architecture is sketched on Figure 2. There are three distinctive modules to be discriminated: Navigation (on the right side on Figure 2), Mine Detection Scanning (in the middle on Figure 2) and Metal Detection (on the left side on Figure 2). These three processes are controlled by a watchdog, the robot motion scheduler, which manages the execution of each module and decides on the commands to be sent to the robot actuators. This robot motion scheduler is explained more in detail in Figure 3 and is discussed here more in detail for each of the three modules. 1. Navigation Different Sensors provide input for a Simultaneous Localization and Mapping module Sensors: GPS (Global Positionment System) gives absolute coordinates IMS (Inertial Measurement System) gives acceleration (and speed and position by integration) US (Ultrasonic sensors) give distance measurements to obstacles IR (Infrared sensors) give distance measurements to obstacles LASER gives line 3D data Mine Sensor: The mine imaging module will return locations of mines, which have to be represented on the map and which are obstacles themselves As the SLAM module works with a global map, it doesn’t have to re-calculate the whole map from scratch every time, but the map can just be iterated to improve the different estimates, hence the loopback arrow. The SLAM module outputs a global map with obstacles and also with mines, thanks to the input from the mine imaging module. This map is used by the navigation module to calculate a safe path. The safe path is given as an input to the robot motion scheduler which will transform it into a motor command and execute it, unless another module has a higher priority task (and trajectory) to perform.