The Multi-Agent Path Finding with Task Assignment (MAPF-TA) problem combines task allocation and collision-free path finding for multiple agents within a graph. It can be solved by an extension of the well-known Conflict-Based Search (CBS) algorithm called CBS-TA, which has been demonstrated to be optimal in terms of the sum of costs of all agents. While coordination between agents in MAPF-TA is limited to no-collision constraints, real-world scenarios may require cooperation between agents. For instance, in an exploration mission involving a system of multiple robots, one robot might need to enter a hazardous area only if another agent is able to monitor this area from a support location. Given a hazardous location and its corresponding support location, this coordination requirement can be modeled by a support constraint. In this paper, we propose an extension of the CBS-TA algorithm to handle these support conflicts. In addition, we improve the algorithm’s performance by introducing an alternative cost matrix for task assignment, which takes into account support coordination while maintaining the optimality of the CBS-TA algorithm. We compare the proposed approach to a greedy algorithm in which the task assignment and path finding problems are decoupled, and using two different assignment matrices: the original matrix of CBS-TA and our support-aware matrix. Experiments are carried out using standard MAPF benchmark instances, showing that the proposed cost matrix improves search time and increases the number of instances solved within a given timeout.

This work presents a novel approach for the online supervision of robotic systems assembled from multiple complex components with skillset-based architectures, using Petri nets (PN). Predictive runtime verification is performed, which warns the system user about actions that would lead to the violation of safety specifications, using online model-checking tools on the system PNs.

Performing a complex autonomous mission with a multi-robot system requires to integrate several deliberative approaches to perform task allocation, optimization, and execution control. Implementing such a deliberative architecture is a complex task: it requires the developer to master the decision algorithms themselves (e.g., automated planning models), to have a good knowledge of the involved robotic platforms, and to think about how these elements will be assembled as a system architecture. We propose a framework to help designing such deliberative architectures. The framework relies on the concept of a hierarchical structure of actors, each actor managing goals with specific planning or optimization approaches, and delegating sub-goals to other actors.

In this paper, we study the effects of impaired communications on the performances of auction-based task allocation in a dynamic surveillance scenario. We propose a novel connectivity term to include in the bid valuation formula, that aims at improving communications in the multi-robot team. We evaluate our method as well as another state-of-the-art method using robot inter-distance to maintain communication, on randomly generated scenarios and on a real-world scenario. We demonstrate that including our connectivity term in the bid valuation formula improves the performances of the auction scheme.

This paper introduces an Online Localisation and Colored Mesh Reconstruction (OLCMR) ROS perception architecture for ground exploration robots aiming to perform robust Simultaneous Localisation And Mapping (SLAM) in challenging unknown environments and provide an associated colored 3D mesh representation in real time. It is intended to be used by a remote human operator to easily visualise the mapped environment during or after the mission or as a development base for further researches in the field of exploration robotics. The architecture is mainly composed of carefully-selected open-source ROS implementations of a LiDAR-based SLAM algorithm alongside a colored surface reconstruction procedure using a point cloud and RGB camera images projected into the 3D space. The overall performances are evaluated on the Newer College handheld LiDAR-Vision reference dataset and on two experimental trajectories gathered on board of representative wheeled robots in respectively urban and countryside outdoor environments.

In this paper, we propose a formal language to specify robot skills, i.e. the elementary behaviours or functions provided by the robot platform in order to perform an autonomous mission. The advantage of the language we propose is that it integrates a wide range of elements that allows to define and provide automatic translation both to operational models, used online to control the skill execution, and descriptive models, allowing to reason about the expected skill execution, and then apply automated planning or model-checking taking skill models into account.

We consider a problem minimizing the total duration of accomplishing missions performed by heterogeneous vehicles. The problem respects constraints related to vehicles' capabilities and energy capacities. The goal is to determine the best routes of each vehicle deployed by choosing which waypoints to pass and which observations to perform. Each vehicle has a particular distance matrix and a limited energy. In order to provide high quality solutions within reasonable computational time, two decomposition-based approximate methods were implemented: (i) the Multiphase heuristic, and (ii) the Two-Phase iterative heuristic. The performance of the methods is evaluated against the Branch-and-Cut algorithm using generated instances.

Fault-tolerant architectures are mandatory to ensure the robustness of autonomous robots performing missions in complex and uncertain environments. The first step of a fault-tolerant mechanism is the detection of a faulty behavior of the system. It is then important to provide tools to help robot developers specify relevant observers. It is moreover crucial to guarantee a correct implementation of the observers, i.e. that the observers do not miss data and do not trigger unsuitable recovery actions in case of false detection. In this paper, we propose a specification language for observers that uses Past-Time LTL to express complex formulas on data produced by software components, and timed constraints on the evaluations of these formulas. We moreover provide an implementation of this specification that guarantees a real-time evaluation of the observers. We briefly describe the observers we have specified for a patrolling mission, and we evaluate the performance of our approach compared to state of the art on a benchmark in which we detect errors on a laser range sensor.

Field multi-robot missions face numerous unavoidable disturbances, such as delays in executing tasks and intermittent communications. Coping with such disturbances requires to endow the robots with high-level decision skills. We present a distributed decision architecture based first on a hybrid planner that can manage decentralized repairs with partial communication, and secondly on a distributed execution algorithm that efficiently propagates delays. This architecture has been successfully experimented on the field for the achievement of surveillance missions involving eight (8) real autonomous aerial and ground robots.

Generic control of wheel-on-leg robots on arbitrary uneven terrains is a challenging task due to the complexity of the robot dynamics, surface interactions, and environmental structures. This paper deals with the control of a wheel-on-leg robot with passive and active internal compliance that enables estimation of wheel-ground interaction forces. The proposed method is based on a continuous state space Q-learning approach that uses the contact forces estimates to learn, through trial and error, the appropriate control policy from a set of predefined behaviors. Without any prior knowledge of the ground geometry, the robot is able to react to unanticipated obstacles. The learned policy proves to be generic and allows the robot to negotiate complex obstacles that had not been considered during learning phase.

In this paper, we propose the use of compliant elements in the actuation of a wheel-legged robot in order to improve its locomotion properties on unknown and irregular terrains. Detection of the obstacles is achieved by a synergistic use of the structural compliances. The robot's capabilities to surmount steep obstacles is thus improved thanks to the inertia of the chassis and flexibility in postural control. In the proposed robot's kinematics, the four wheels are attached to the main body through vertical series elastic actuators (SEA) and with a passive horizontal compliant mechanism subject to a specific wheel speed control. The overall control relies on postural servoing and a local reactive loop which adapts the vertical forces applied by the SEA on each wheel according to the detected obstacle and the stability margin. The resulting system is evaluated with physical simulations for two case studies: a canonical steep obstacle on one wheel at a time and multiple random rough terrains.

Providing guarantees on the system behavior is mandatory in order to let the robots enter our every-day life. Among these guarantees, proving the fulfillment of real-time constraints on the software is a key issue, as their violation could result into unexpected and unsafe behaviors. In this paper, we present a methodology to guarantee real-time constraints on component-based software architectures of robots. This methodology relies on the MAUVE language to model the component architecture, and on a set of analysis tools that first estimate the worst case execution time of elementary functions from actual component traces, and then check the real-time constraints of each component. We illustrate this process on the architecture developed for the autonomous navigation of a partially known area by a mobile robot.

Unmanned ground vehicles (UGV) may experience skidding when moving at high speeds, and therefore have its safety jeopardized. For this reason the nonlinear dynamics of lateral tire forces must be taken into account into the design of steering controllers for autonomous vehicles. This paper presents the design of a state feedback piecewise affine controller applied to an UGV to coordinate the steering and torque distribution inputs in order to reduce vehicle skidding on demanding maneuvers. The control synthesis consists in solving an optimization procedure involving constraints in the form of Linear Matrix Inequalities which are obtained from stability conditions of a piecewise quadratic Lyapunov function. The improved performance of the piecewise affine controller with respect to a linear controller is confirmed through simulations on degraded tire-floor adhesion.

This paper focuses on the problem of robust obstacles crossing for a high mobility wheel-legged robot. To improve the obstacle clearance capability, a method dealing with the contact stability optimization is developed. A specific stability criterion taking the friction into account is proposed. The optimization algorithm uses both the kinematic redundancy in order to modify the position of the Center of Mass (CoM), modifying the resulting distribution of contact forces, and the actuation redundancy to improve the stability of frictional contacts by adapting the internal forces. We show that the choice of this particular criterion allows us to maximize the robustness of contacts stability relatively to the modeling errors affecting force control (friction in mechanical transmission). Performances of this algorithm are evaluated in simulation and the necessity for a CoM trajectory planning is highlighted by an analysis of obstacle crossing using this criterion.

In this study we present a chain sliding mode controller for the control of a four wheeled autonomous mobile robot performing aggressive turning maneuver to 90 degrees on a slippery surface. The controller consists of a set of local sliding mode controllers and the hyperplanes of switching between them. The parameters of the sliding mode controllers and the hyperplanes are obtained using methods of multiobjective stochastic optimization applied to a model of the robot. The obtained controller is used to drive the mobile robot. The results show that the controller allowed the robot to execute the aggressive maneuver. Moreover, the turn radius obtained with the controller was twice less than the minimal turn radius admitted by the robot's geometry and the steering system.

This paper is focused on the problem of accurate and reliable path tracking control of a 4-wheels car-like mobile robot moving off-road at high speed. Dynamic and extended kinematic models that take into account the effects of wheel skidding are presented. Based on the extended kinematic model, an adaptive and predictive controller for path tracking is derived. This control law is combined to a stabilization algorithm of yaw motion, based on the dynamic model and the modulation of driven wheel forces. The overall control architecture is experimentally evaluated on a slipping terrain. Results demonstrate enhanced performances as the robot succeed in following the path at high speed, accurately and without loss of control.

In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity (about 10 m/s) on a loose surface (dirty road). To tackle this highly nonlinear control problem, we employ multi-objective evolutionary algorithms to explore and optimize the parameters of a neural network-based controller. The obtained controller is shown to be robust with respect to uncertainties of the robot parameters, speed of the maneuver and properties of the ground. The controller is tested using two mathematical models of significantly different complexity and accuracy.

The paper develops a method for analyzing and improving by control obstacle clearance capacities of articulated multi-wheeled rovers. On uneven ground surface, load and traction force distributions through the wheel/ground contact system are highly coupled. They are both conditioned by the global equilibrium of the mechanical system and the contact stability constraints. The optimal traction force distribution problem is formulated here as a convex optimization problem using Linear Matrix Inequalities (LMIs). Velocity and force transmissions in articulated multi-wheeled mobile robots are introduced under a generic form decomposed in task, joint and contact levels. A tyre-model is used for the evaluation of the robustness of the solution with respect to slippage phenomena. Simulation results show that the traction distribution forces which is so determined lead to a significant increase in obstacle clearance capacities compared to an usual velocity control technique.

A robust dynamic feedback controller is designed and implemented, based on the dynamic model of the six-wheel skid-steering RobuROC6 robot, performing high speed turns. The control inputs are respectively the linear velocity and the yaw angle. The main object of this paper is to elaborate a sliding mode controller, proved to be robust enough to ignore the knowledge of the forces within the wheel-soil interaction, in the presence of sliding phenomena and ground level fluctuations. Finally, a 3D simulation is performed with an accurate physical engine to evaluate the efficiency of this designed control law.

This paper deals with design and implementation of a stabilization algorithm for a car like robot performing high speed turns. The control of such a kind of system is rather difficult because of the complexity of the physical wheel- soil interaction model. In this paper, it is planned to analyze the complex dynamic model of this process to elaborate a stabilization algorithm only based on the measurement of the system yaw rate. Finally, a 3D simulation is performed to evaluate the efficiency of this designed stabilization algorithm.

This paper concerns the control of an autonomous high mobility wheel-legged rover crossing uneven terrains. A new control strategy, using active redundancies of the robot, leads to elaborate a posture control based on the potential field approach of the stability measurement. Then a decoupled posture and trajectory control algorithm based on the velocity model of the robot is proposed. Last, simulation results showing performance of the control algorithm are presented.

A model-based control for fast autonomous four-wheel mobile robots on soft soils is developed. This control strategy takes into account slip and skid effects to extend the mobility over planar granular soils. Each wheel is independently actuated by an electric motor. The overall objective is to follow a path roughly at relatively high speed. Some results obtained in dynamic simulation are presented.

This paper addresses the control of a hybrid wheel-legged system evolving on rough terrain. First, the posture and trajectory parameters are introduced. Then, a decoupled posture and trajectory control algorithm based on the velocity model of the robot is proposed. Last, the performance and feasibility of the control algorithm are evaluated through simulations and experiments with the Hylos robot.