Autonomous long-range navigation in partially known planetary-like terrains is still an open challenge for robotics. Navigating hundreds of meters without any human intervention requires the robot to be able to build various representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to control its motions and to localize itself as it moves. All these activities have to be scheduled, triggered, controlled and interrupted according to the rover context. In this paper, we briefly review some functionalities that have been developed in our laboratory, and implemented on board the Marsokhod model robot, Lama. We then present how the various concurrent instances of the perception, localization and motion generation functionalities are integrated. Experimental results illustrate the functionalities throughout the paper.

/pdf/autonomous-rover-navigation-on-unknown-terrains-2c0asjgsfr.pdf

Autonomous Rover Navigation on Unknown Terrains

The theme of this paper is to design a real-time fuzzy target tracking control scheme for autonomous mobile robots by using infrared sensors. At first two mobile robots are setup in the target tracking problem, where one is the target mobile robot with infrared transmitters and the other one is the tracker mobile robot with infrared receivers and reflective sensors. The former is designed to drive in a specific trajectory. The latter is designed to track the target mobile robot. Then we address the design of the fuzzy target tracking control unit, which consists of a behavior network and a gate network. The behavior network possesses the fuzzy wall following control (FWFC) mode, fuzzy target tracking control (FTTC) mode, and two fixed control modes to deal with different situations in real applications. Both the FWFC and FTTC are realized by the fuzzy sliding-mode control scheme. A gate network is used to address the fusion of measurements of two infrared sensors and is developed to recognize which situation is belonged to and which action should be executed. Moreover, the target tracking control with obstacle avoidance is also investigated in this paper. Both computer simulations and real-time implementation experiments of autonomous target tracking control demonstrate the effectiveness and feasibility of the proposed control schemes.

Fuzzy target tracking control of autonomous mobile robots by using infrared sensors

The paper presents an approach to reactive navigation in cross-country terrains. The approach relies on a particular probabilistic obstacle detection procedure, that describes the area perceived by a pair of stereo cameras as a set of polygonal cells. To generate the motion commands on the basis of this terrain description, we present some improvements and adaptations to the classical potential fields technique. Results on real stereo data illustrate our contribution throughout the paper, and simulated long range traverses are discussed.

/pdf/reactive-navigation-in-outdoor-environments-using-potential-2i088vohyi.pdf

Reactive navigation in outdoor environments using potential fields

Autonomous long range navigation in partially known planetary-like terrain is an open challenge for robotics. Navigating several hundreds of meters without any human intervention requires the robot to be able to build various representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to localize itself as it moves, and to schedule, start, control and interrupt these various activities. In this paper, we briefly describe some functionalities that are currently running on board the Marsokhod model robot Lama at LAAS/CNRS. We then focus on the necessity to integrate various instances of the perception and decision functionalities, and on the difficulties raised by this integration.

/pdf/autonomous-rover-navigation-on-unknown-terrains-functions-25rsmk0rwd.pdf

Autonomous Rover Navigation on Unknown Terrains Functions and Integration

: The Air Force Research Laboratory at Tyndall Air Force Base, Florida, has contracted the University of Florida to develop autonomous navigation for various ground vehicles. Autonomous vehicle navigation can be broken down into four tasks. These tasks include perceiving and modeling the environment, localizing the vehicle within the environment, planning and deciding the vehicle's desired motion, and finally, executing the vehicle's desired motion. The work presented here focuses on tasks of deciding the vehicle's desired motion and executing the vehicle's desired motion. The third task above involves planning the vehicle's desired motion as well as deciding the vehicle's desired motion. In this work it is assumed that a planned path already exists and therefore only a technique to decide the vehicle's desired motion is required. Screw theory can be used to describe the instantaneous motion of a rigid body, i.e., the vehicle, relative to a given coordinate system. The concept of vector pursuit is to calculate an instantaneous screw that describes the motion of the vehicle from its current position and orientation to a position and orientation on the planned path. Once the desired motion is determined, a controller is required to track this desired motion. The fourth task for autonomous navigation is to execute the desired motion. In order to accomplish this task, two fuzzy reference model learning controllers (FRMLCs) are implemented to execute the vehicle s desired turning rate and speed. The controllers are designed to be dependent on certain vehicle characteristics such as the maximum vehicle speed maximum turning rate. This is done to facilitate the transfer of these controllers to different vehicles. The vector pursuit path-tracking method and the FRMLCs were first tested in simulation by modeling the Navigation Test Vehicle (NTV) developed by the Center for Intelligent Machines and Robotics (CIMAR) at the University of Florida.

/pdf/vector-pursuit-path-tracking-for-autonomous-ground-vehicles-1hi6c8xffu.pdf

Vector pursuit path tracking for autonomous ground vehicles

This paper presents a set of algorithms for piloting an autonomous planetary rover along a planned path, performing obstacle avoidance, and improving position estimation. Path-tracking is accomplished using feedback-control of position and orientation errors, measured with respect to the planned path trajectory. Obstacle-avoidance is performed through application of an artificial potential field, to data that can be acquired using a scanning rangefinder. Position and velocity estimation is improved by the algorithmic filtering and fusing of odometry and inertial navigation data streams. Results from computer simulation are used to illustrate the path-tracking and obstacle-avoidance capabilities, and experimental data is used to illustrate how sensor fusion mitigates the effects of wheel-slippage and integration-error in position and velocity estimation. >

Path tracking, obstacle avoidance and position estimation by an autonomous, wheeled planetary rover

An algorithm for piloting an autonomous planetary rover along a planned trajectory, while at the same time performing realtime obstacle avoidance is presented. Path-tracking is accomplished using feedback control of the position and orientation errors, measured with respect to the planned path trajectory. Obstacle avoidance is accomplished using the concept of an artificial potential field, with which data from a scanning range sensor can be transformed into what is, in effect, an obstacle avoidance systems Results from computer simulation illustrate the path-tracking and obstacle avoidance capabilities of the steering control system.

G.S. Vukovich

Papers

Path tracking, obstacle avoidance and position estimation by an autonomous, wheeled planetary rover

Guidance and control of an autonomous planetary rover