Christophe Grand

Bio: Christophe Grand is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 14, co-authored 48 publications receiving 806 citations. Previous affiliations of Christophe Grand include International Society for Intelligence Research & University of Paris-Sud.

Stability and Traction Optimization of a Reconfigurable Wheel-Legged Robot:

Abstract: Actively articulated locomotion systems such as hybrid wheel-legged vehicles are a possible way to enhance the locomotion performance of an autonomous mobile robot. In this paper, we address the control of the wheel-legged robot Hylos traveling on irregular sloping terrain. The redundancy of such a system is used to optimize both the balance of traction forces and the tipover stability. The general formulation of this optimization problem is presented, and a suboptimal but computationally efficient solution is proposed. Then, an algorithm to control the robot posture, based on a velocity model, is described. Finally, this algorithm is validated through simulations and experiments that show the capabilities of such a redundantly actuated vehicle to enhance its own safety and autonomy in critical environments.

Nonlinear Attitude and Position Control of a Micro Quadrotor using Sliding Mode and Backstepping Techniques

Abstract: The present study addresses the issues concerning the developpment of a reliable assisted remote control for a four-rotor miniature aerial robot (known as quadrotor), guaranteeing the capability of a stable autonomous flight. The following results are proposed: after establishing a dynamical flight model as well as models for the rotors, gears and motors of the quadrotor, different nonlinear control laws are investigated for attitude and position control of the UAV. The stability and performance of feedback, backstepping and sliding mode controllers are compared in simulations. Finally, experiments on a newly implemented quadrotor prototype have been conducted in order to validate the theoretical analysis.

A biologically inspired meta-control navigation system for the Psikharpax rat robot.

Abstract: A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e.g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment—recognized as new contexts—and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics.

Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction Coefficients

Abstract: It is well recognized in the automotive research community that knowledge of the real-time tire-road friction coefficient can be extremely valuable for active safety applications, including traction control, yaw stability control and rollover prevention. Previous research results in literature have focused on the estimation of average tire-road friction coefficient for the entire vehicle. This paper explores the development of algorithms for reliable estimation of independent friction coefficients at each individual wheel of the vehicle. Three different observers are developed for the estimation of slip ratios and longitudinal tire forces, based on the types of sensors available. After estimation of slip ratio and tire force, the friction coefficient is identified using a recursive least-squares parameter identification formulation. The observers include one that utilizes engine torque, brake torque, and GPS measurements, one that utilizes torque measurements and an accelerometer and one that utilizes GPS measurements and an accelerometer. The developed algorithms are first evaluated in simulation and then evaluated experimentally on a Volvo XC90 sport utility vehicle. Experimental results demonstrate the feasibility of estimating friction coefficients at the individual wheels reliably and quickly. The sensitivities of the observers to changes in vehicle parameters are evaluated and comparisons of robustness of the observers are provided.

The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics

Abstract: The reality gap, which often makes controllers evolved in simulation inefficient once transferred onto the physical robot, remains a critical issue in evolutionary robotics (ER). We hypothesize that this gap highlights a conflict between the efficiency of the solutions in simulation and their transferability from simulation to reality: the most efficient solutions in simulation often exploit badly modeled phenomena to achieve high fitness values with unrealistic behaviors. This hypothesis leads to the transferability approach, a multiobjective formulation of ER in which two main objectives are optimized via a Pareto-based multiobjective evolutionary algorithm: 1) the fitness; and 2) the transferability, estimated by a simulation-to-reality (STR) disparity measure. To evaluate this second objective, a surrogate model of the exact STR disparity is built during the optimization. This transferability approach has been compared to two reality-based optimization methods, a noise-based approach inspired from Jakobi's minimal simulation methodology and a local search approach. It has been validated on two robotic applications: 1) a navigation task with an e-puck robot; and 2) a walking task with a 8-DOF quadrupedal robot. For both experimental setups, our approach successfully finds efficient and well-transferable controllers only with about ten experiments on the physical robot.

Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

Abstract: Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller's performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle.

Drone-aided routing: A literature review

Abstract: The interest in using drones in various applications has grown significantly in recent years. The reasons are related to the continuous advances in technology, especially the advent of fast microprocessors, which support intelligent autonomous control of several systems. Photography, construction, and monitoring and surveillance are only some of the areas in which the use of drones is becoming common. Among these, last-mile delivery is one of the most promising areas. In this work we focus on routing problems with drones, mostly in the context of parcel delivery. We survey and classify the existing works and we provide perspectives for future research.

Hierarchical control over effortful behavior by rodent medial frontal cortex: A computational model.

Abstract: The anterior cingulate cortex (ACC) has been the focus of intense research interest in recent years. Although separate theories relate ACC function variously to conflict monitoring, reward processing, action selection, decision making, and more, damage to the ACC mostly spares performance on tasks that exercise these functions, indicating that they are not in fact unique to the ACC. Further, most theories do not address the most salient consequence of ACC damage: impoverished action generation in the presence of normal motor ability. In this study we develop a computational model of the rodent medial prefrontal cortex that accounts for the behavioral sequelae of ACC damage, unifies many of the cognitive functions attributed to it, and provides a solution to an outstanding question in cognitive control research-how the control system determines and motivates what tasks to perform. The theory derives from recent developments in the formal study of hierarchical control and learning that highlight computational efficiencies afforded when collections of actions are represented based on their conjoint goals. According to this position, the ACC utilizes reward information to select tasks that are then accomplished through top-down control over action selection by the striatum. Computational simulations capture animal lesion data that implicate the medial prefrontal cortex in regulating physical and cognitive effort. Overall, this theory provides a unifying theoretical framework for understanding the ACC in terms of the pivotal role it plays in the hierarchical organization of effortful behavior.