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This page cites the relevant journal articles published only. Other publications can be found here.

On-Going Research Projects

Social Navigation [2011-Present]

  • Be patient...

Inevitable Collision States [2003-Present]

Provably Safe
  NavigationProvably Safe Navigation for Mobile Robots with Limited Field-of-Views in Dynamic Environments (Autonomous Robots 2012).

ICS-Avoid BenchmarkingICS-Avoid Benchmarking (IEEE Int. Conf. on Robotics and Automation 2009).

  • S. Bouraine, T. Fraichard, and H. Salhi. Provably safe navigation for mobile robots with limited field-of-views in dynamic environments. Autonomous Robots, 2012. [pdf].
  • Th. Fraichard and H. Asama. Inevitable Collision States. A Step Towards Safer Robots? Advanced Robotics, 18(10), 2004. [www].

Trajectory Deformation [2006-Present]

Automated Wheelchair
  NavigationAutomated Wheelchair Navigation Using Trajectory Deformation (PhD V. Delsart 2010).

TeddyTeddy, a Trajectory Deformation Scheme (IEEE-RSJ Int. Conf. on Intelligent Robots and Systems 2008).

  • Th. Fraichard and V. Delsart. Navigating Dynamic Environments with Trajectory Deformation. Journal of Computing and Information Technology, 17(1), March 2009. [www].

Trajectory Generation [2006-Present]

  • Be patient...

Earlier Research Projects

Motion Prediction [2003-2009]

Motion PredictionLearning and Prediction of Motion with Growing Hidden Markov Models (2009).

  • D. Vasquez, Th. Fraichard, and C. Laugier. Growing Hidden Markov Models: a Tool for Incremental Learning and Prediction of Motion. Int. Journal of Robotics Research, 28(11-12), November 2009 [www].
  • C. Laugier, D. Vasquez, M. Yguel, Th. Fraichard, and O. Aycard. Geometric and Bayesian Models for Safe Navigation in Dynamic Environments. Intelligent Service Robotics, 1(1), January 2008. [pdf].
  • D. Vasquez, Th. Fraichard, O. Aycard, and C. Laugier. Intentional Motion On-Line Learning and Prediction. Machine Vision and Applications, January 2008. [www].

Partial Motion Planning [2001-2009]

PMP-NavPMP-Nav on the Cycab simulator (Int. Conf. on Field and Service Robotics 2007).
EindhovenPMP-Nav on the Cycab (Eindhoven demonstration 2007).
IntersectionCybercar Cooperation for Safe Intersection (IEEE Intelligent Transportation Systems Conf. 2006).
PMPPMP (IEEE-RSJ Int. Conf. on Intelligent Robots and Systems 2005).

  • K. Macek, D. Vasquez, Th. Fraichard, and R. Siegwart. Towards Safe Vehicle Navigation in Dynamic Urban Scenarios. Automatika, 50(3-4), November 2009. [www].
  • R. Benenson, S. Petti, Th. Fraichard, and M. Parent. Toward Urban Driverless Vehicles. Int. Journal of Vehicle Autonomous Systems, 6(1/2), 2008. [pdf].

MDP-Based Navigation [2004-2008]

MDP-Based Navigation The purpose of this work is to study how the Markov Decision Process Theory can be used to robustify mobile robot navigation. To reach a given goal, a mobile robot first computes a motion plan, i.e. a sequence of actions that will take it to its goal, and then executes it. Markov Decision Processes (MDP) have been successfully used to solve these two problems. Such approaches use a graph representation of the robot's state space and their main advantage is that they provide a theoretical framework to deal with the uncertainties related to the robot's motor and perceptive actions during both planning and execution stages. Unfortunately, their algorithmic complexity is exponential in the number of edges of the graph which limits their application to complex problems. Our approach is to address this complexity issue by reducing the number of states through aggregation techniques. We have proposed a MDP-based planning method that uses a hierarchical representation of the robot's state space (based on a quad-tree decomposition of the environment). Besides, the actions used better integrate the kinematic constraints of a wheeled mobile robot. These two features yield a motion planner more efficient and better suited to plan robust motion strategies. We have also focused on the execution stage. An execution scheme based upon Markov Localisation has been defined and validated experimentally. Central in this validation is the transition function (that encodes the uncertainties related to the robot actions), and the sensors' model: a learning procedure using a real robot and its sensors has been defined and experiments with a Koala mobile platform has demonstrated the robustness of the whole navigation approach.

  • J. Burlet, Th. Fraichard, and O. Aycard. Robust Navigation Using Markov Models. Advanced Robotic Systems, 5(2), June 2008. [www].

Bayesian Occupancy Filter [2000-2006]

BOFCycab Electric Vehicle Avoiding an Occluded Pedestrian (Int. Journal of Robotics Research 2006).
BOF-Based TrackingBof-Based Tracking of People in a Shopping Mall.

  • C. Coué, C. Pradalier, C. Laugier, Th. Fraichard, and P. Bessière. Bayesian Occupancy Filtering for Multi-Target Tracking: an Automotive Application. Int. Journal of Robotics Research, 25(1), January 2006. [www].

Continuous-Curvature Path Planning [1996-2004]

CC Path Planning CC-Steer We are interested in computing collision-free paths for car-like vehicles that better take into account their kinematic properties. Many standard path planning techniques would compute paths made up of straight segments connected with tangential circular arcs. The curvature of this type of path is discontinuous so that if a car-like vehicle were to actually follow such a path, it would have to stop at each curvature discontinuity in order to reorient its front wheels. Accordingly, we are primarily seeking to compute paths with continuous curvature profiles. To that end, we have designed CC-Steer: it is a steering method (a.k.a. a trajectory generation method) for car-like vehicles, i.e. an algorithm planning paths in the absence of obstacles. CC-Steer computes paths with:

  1. continuous curvature,
  2. upper-bounded curvature (to account for steering angle limits), and
  3. upper-bounded curvature derivative (to account for steering velocity limits).

CC-Steer is complete, i.e. it can connect arbitrary pairs of configurations. Besides it verifies a topological property that ensures that when it is used within a general motion-planning scheme, it yields a complete collision-free path planner. The coupling of CC-Steer with a general planning scheme yields a path planner that computes collision-free paths verifying the properties mentioned above. Accordingly, a car-like vehicle can follow such paths without ever having to stop in order to reorient its front wheels. Besides, such paths can be followed with a nominal speed which is proportional to the curvature derivative limit. The paths computed by CC-Steer are made up of line segments, circular arcs, and clothoid arcs. They are not optimal in length. However, it has been shown that they converge toward the optimal "Reeds and Shepp" paths when the curvature derivative upper bound tends to infinity. The capability of CC-Steer to serve as an efficient steering method within general path planning schemes has been demonstrated.

  • Th. Fraichard and A. Scheuer. From Reeds and Shepp's to Continuous-Curvature Paths. IEEE Trans. on Robotics, 20(6), December 2004. [pdf].

Fuzzy Logic-Based Navigation [1993-2001]

Control architecture The purpose of this work is to study how Fuzzy Logic can be used for mobile robot navigation. Fuzzy Logic has been used to develop the reactive component of a navigation architecture for a car-like vehicle intended to move in dynamic and partially known environments. This component is called the Execution Monitor (EM). Its purpose is to generate commands for the servo-systems of the vehicle so as to follow a given nominal trajectory while reacting in real-time to unexpected events. EM is designed as a fuzzy controller. A behaviour-based approach is used to set up the fuzzy rule base: the overall behaviour of the vehicle results from the combination of several basic behaviours (trajectory following, obstacle avoidance, etc.). This approach permits an easy and incremental construction of the fuzzy rule base and also to develop and test the basic behaviours separately. EM has been implemented and tested on a real computer-controlled car equipped with sensors of limited precision and reliability. Experimental results obtained with the prototype vehicle have demonstrated the capability of EM to actually control a real vehicle and to perform trajectory following and obstacle avoidance in real outdoor environments by using simple fuzzy behaviours relying upon low-resolution sensor data.

  AvoidanceLigier Avoiding Unexpected Obstacle (IEEE-RSJ Int. Conf. on Intelligent Robots and Systems 1995).

  • Th. Fraichard and Ph. Garnier. Fuzzy Control to Drive Car-Like Vehicles. Robotics and Autonomous Systems, 34(1), January 2001. [www].

State-Time Space

  • Th. Fraichard. Trajectory Planning in a Dynamic Workspace: a `State-Time Space' Approach. Advanced Robotics, 13(1), 1999. [www].
  • Th. Fraichard. Trajectory Planning Amidst Moving Obstacles: Path-Velocity Decomposition Revisited. Journal of the Brazilian Computer Science Society - Special issue on Robotics, 4(3), April 1998. [www].

Hybrid Navigation Architecture [1996-1999]

     Navigation Architecture The purpose of this work is to develop a control architecture endowing a car-like vehicle moving in a dynamic and partially known environment with autonomous motion capabilities. Like most recent control architectures for autonomous robot systems, it combines three functional components: a set of basic real-time skills, a reactive execution mechanism and a decision module. The main novelty of the architecture proposed lies in the introduction of a fourth component akin to a meta-level of skills: the sensor-based manoeuvres, ie general templates that encode high-level expert human knowledge and heuristics about how a specific motion task is to be performed. The concept of sensor-based manoeuvres permit to reduce the planning effort required to address a given motion task, thus improving the overall response-time of the system, while retaining the good properties of a skill-based architecture, ie robustness, flexibility and reactivity. The paper focuses on the trajectory planning function (which is an important part of the decision module) and two types of sensor-based manoeuvres, trajectory following and parallel parking, that have been implemented and successfully tested on a real automatic car-like vehicle placed in different situations.
  • C. Laugier, Th. Fraichard, Ph. Garnier, I. E. Paromtchik, and A. Scheuer. Sensor-Based Control Architecture for a Car-Like Vehicle. Autonomous Robots, 6(2), April 1999. [www].