International audience

International audience

International audience

In this article, we propose an original solution to the problem of surface reconstruction of large-scale unknown environments, with multiple cooperative robots. As they progress through the 3-D environment, the robots rely on volumetric maps obtained via a TSDF representation to extract discrete incomplete surface elements (ISEs), and a list of candidate viewpoints is generated to cover them. A next-best-view planning approach, which approximately solves a traveling salesman problem (TSP) via greedy allocation, is then used to iteratively assign these viewpoints to the robots. Two multiagent architectures, a centralized one (TSP-Greedy Allocation or TSGA) and a distributed one (dist-TSGA), in which the robots locally compute their maps and share them, are developed and compared. Extensive numerical and real-world experiments with multiple aerial and ground robots in challenging 3-D environments show the flexibility and effectiveness of our surface representation of a volumetric map. The experiments also shed light on the nexus between reconstruction accuracy and surface completeness, and between total distance traveled and execution time.

International audience

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 solve the full-consensus problem for multiple nonholonomic vehicles interacting over a directed leader-follower topology and subject to sensing constraints in the form of limited range and limited field-of-view. Remarkably, based on a polar-coordinates model transformation, the designed controller is time-invariant and smooth (in the domain of definition). Moreover, the control laws rely only on local measurements, making it well suited for implementation. The asymptotic convergence to the consensus manifold as well as the respect of the constraints is established using Lyapunov’s first method and cascaded systems theory. Realistic simulations in the Gazebo-ROS environment, which illustrate the effectiveness of our theoretical contributions, are shown in an accompanying video.

This paper presents a tele-operation system that enables a MAV to be controlled on virtual surfaces by an unskilled operator using high-level inputs. These virtual surfaces can be placed relatively to the infrastructure to be inspected, in order to ensure safety of the flight and repeatability of the acquisition conditions of the inspection data (e.g. at a constant distance from the infrastructure). The architecture, interface and embedded controller of the tele-operation system are described, and results from flight experiments in an industrial warehouse are provided for three typical inspection scenarios of infrastructures.

A new distributed algorithm is presented for waypoint navigation of a multi-robot system. The proposed two-level architecture (reference generator and local controller) exploits Voronoi partitioning and purely geometric considerations to distributively generate references for each robot in order to ensure collision avoidance and convergence of the fleet to the waypoint. Flexibility in the obtained formation pattern is made possible by the algorithm, by not pre-fixing as usually done its geometric form. In addition, the gain tuning is easy and the setting allows to naturally obtain certain formation patterns and adjust the rigidity of the fleet. Moreover the distributed nature of the algorithm also allows robustness to online modification of the number of vehicles (in the fleet or within range of communication), also addressing the 2-robot scenario. Field experiments on ground mobile robots are provided to illustrate the performance of the algorithm.

Transferring learning-based models to the real world remains one of the hardest problems in model-free control theory. Due to the cost of data collection on a real robot and the limited sample efficiency of Deep Reinforcement Learning algorithms, models are usually trained in a simulator which theoretically provides an infinite amount of data. Despite offering unbounded trial and error runs, the reality gap between simulation and the physical world brings little guarantee about the policy behavior in real operation. Depending on the problem, expensive real fine-tuning and/or a complex domain randomization strategy may be required to produce a relevant policy. In this paper, a Soft-Actor Critic (SAC) training strategy using incremental environment complexity is proposed to drastically reduce the need for additional training in the real world. The application addressed is depth-based mapless navigation, where a mobile robot should reach a given waypoint in a cluttered environment with no prior mapping information. Experimental results in simulated and real environments are presented to assess quantitatively the efficiency of the proposed approach, which demonstrated a success rate twice higher than a naive strategy.

In this paper, we propose a novel cluster-based Next-Best-View path planning algorithm to simultaneously explore and inspect large-scale unknown environments with multiple Unmanned Aerial Vehicles (UAVs). In the majority of existing informative path-planning methods, a volumetric criterion is used for the exploration of unknown areas, and the presence of surfaces is only taken into account indirectly. Unfortunately, this approach may lead to inaccurate 3D models, with no guarantee of global surface coverage. To perform accurate 3D reconstructions and minimize runtime, we extend our previous online planner based on TSDF (Truncated Signed Distance Function) mapping, to a fleet of UAVs. Sensor configurations to be visited are directly extracted from the map and assigned greedily to the aerial vehicles, in order to maximize the global utility at the fleet level. The performances of the proposed TSGA (TSP-Greedy Allocation) planner and of a nearest neighbor planner have been compared via realistic numerical experiments in two challenging environments (a power plant and the Statue of Liberty) with up to five quadrotor UAVs equipped with stereo cameras.

This paper considers collaborative stereo-vision as a mean of localization for a fleet of micro-air vehicles (MAV) equipped with monocular cameras, inertial measurement units and sonar sensors. A sensor fusion scheme using an extended Kalman filter is designed to estimate the positions and orientations of all the vehicles from these distributed measurements. The estimation is completed by a formation control to maximize the overlapping fields of view of the vehicles. Experimental tests for the complete perception and control loop have been performed on multiple MAVs with centralized processing on a ROS ground station.

This paper presents experimental results of cooperative guidance laws embedded on Lego Mindstorms NXT mobile robots for two types of missions. The first one is navigation to a waypoint as a fleet with collision and obstacle avoidance, following a model predictive control (MPC) framework. The second one is source localization, i.e., finding the maximum of a potential field, for which a distributed estimation and control strategy is proposed. Experiments show the ability to perform the two missions on these basic mobile robots, in spite of their limited computational resources. In particular, the search for the optimal control sequence through a dedicated discretization of the command space makes it possible to implement real-time MPC.

This paper focuses on viewpoint planning for 3D active object recognition. The objective is to design a planning policy into a Q-learning framework with a limited number of samples. Most existing stochastic techniques are therefore inapplicable. We propose to use Kriging and bayesian Optimization coupled with Q-learning to obtain a computationally-efficient viewpoint-planning design, under a restrictive sampling budget. Experimental results on a representative database, including a comparison with classical approaches, show promising results for this strategy.