Obstacle Avoidance
Motion Estimation
Local Mapping


Architectures for Sensor- Based Navigation

Within the mobility of the vehicle, the sensor-based motion system is the part in charge of generating movement free of collisions between successive positions. The design of these systems is determined by diverse factors involved in this question, like the model construction, the deliberative planning and the motion generation. The model builder constructs a representation which is the base for the deliberation and which provides with memory the reactive behavior (obstacle avoidance), the planner module generates global plans and the reactive module computes the local motion. The sensor-based systems made up as synthesis of modules with these functionalities mainly differ in the interaction between the planner and the reactor (i.e. how the reactive navigation uses the information available of the planner), and in the tools used to implement each module.

We give in this Section a global vision of the sensor-based system, which is formed by an architecture that integrates three modules with the following functionalities: model construction, motion planning and reactive navigation:

  1. Model Builder Module: construction of a model of the environment (to increase the spatial domain of the planning and used as local memory for the reactivity). We use a binary occupancy grid that is updated whenever a new sensory measurement is available. The grid has a limited size and travels centred with the robot. Furthermore, we employ a scan matching technique to improve the vehicle odometry before integrating any new measure in the grid. Although, a scan matching technique does not guarantee global consistency, its precision is enough to build the local map needed by the other modules.

  2. Planner Module: extraction of the connectivity of the free space (used to avoid the cyclical motions and trap situations). We have developed a new planner that computes the existence of a path that joins the robot and goal locations. The planner constructs iteratively a graph whose nodes are locations in the space and the arcs are tunnels of free space that joins them. When the goal is reached, the current tunnel contains a path to the goal. This planner avoids the local minima and is very efficient so that it can be executed in real time.

  3. Obstacle Avoidance: computation of the collision-free motion. We chose the Nearness Diagram Navigation (ND method in short), which is based on selecting at every moment a navigational situation and to apply a motion law adapted for each one. This method has demonstrated to be very efficient and robust in environments with little space to manoeuvre.


Globally the system works as follows (see Figure): given a laser scan and the odometry of the vehicle, the model builder incorporates this information into the existing model. Next, the information of obstacles and free space in the grid is used by the planner module to compute the course to follow to reach the goal. Finally, the reactive module uses the information of the obstacles contained in the grid and information of this tactical planner to generate the motion (to drive the vehicle free of collisions towards the goal). The motion is executed by the vehicle controller and the process restarts with a new sensorial measurement. It is important to stress that the three modules work synchronously within the perception - action cycle. Next, we address the design of the modules and the integration architecture.

Most relevant publications:

  1. J. Minguez, L. Montano. Sensor-Based Robot Motion Generation in Unknown, Dynamic and Troublesome Scenarios Robotics and Autonomous Systems, 2005. (pdf draft)

  2. J. Minguez. Integration of Planning and Reactive Obstacle Avoidance in Sensor-based Navigation.  Proceedings of the Conference on Intelligent Robots and Systems (IROS), 2005. Edmonton, Canada. (pdf)

  3. L. Montesano, J. Minguez and L. Montano. Modeling the Static and the Dynamic Parts of the Environment to Improve Sensor-based Navigation. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2005. Barcelona, Spain. (pdf)

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Principal | Obstacle Avoidance | Architectures | Motion Estimation | Local Mapping

Este sitio se actualizó por última vez el 28 de octubre de 2005