This paper presents a touch-based control method for tracking 3D surfaces in robotic finishing tasks, specifically for the footwear industry. Our method combines a normal force controller and an orientation controller, both based on the forces feedback from a tactile sensor with nine contact points. The controller regulates the contact force and the orientation of a tool mounted on a robotic end-effector by generating velocity commands in Cartesian space, which allows the robot to adapt the tool pose according to the interaction with the surface. The system does not require a predefined position trajectory, instead it generates velocity commands. The results show the controller’s capability to maintain a constant contact force with an error of 0.229 ± 0.169 [N] when testing with a real shoe, and it adapts to unknown surfaces with different curvatures and slopes.

The tracking problem remains difficult because trackers may lose the target due to occlusions or changes in the target's appearance and start tracking another one, making it difficult to detect when they fail. To deal with these problems, we design a novel method to track objects robustly by fusing different trackers, using an Extended Kalman Filter, which helps to detect these situations and correct them. We use our tracker-fusion in a mobile robot to follow a pedestrian while also avoiding dynamic obstacles, an aspect not usually analyzed in pedestrian following. The perception of the environment is done with a 3D LiDAR and the tracking is performed on front-view images constructed from the point cloud. We show that by fusing two different trackers, the robot can follow the target during long experimental sessions where there are occlusions of the target by other people. The method is more precise and robust than using the trackers without the fusion.

The task of grasping objects is a simple and routinely action for humans but it is complex for robots. To integrate robots into everyday tasks, they have to be equipped with capabilities human-like dexterity. In this line, we propose an analytic method, called GeoGraspEvo, to compute grasping points to be used by robotic hands with three, four or more fingers. Our proposal uses features computed from visible surface objects captured by a single RGBD image of a scene. Additionally, it uses as input some configurable kinematic parameters to be able to carry out the grasping depending on the hand morphology. The method compute grasping points with no training process.

LiDAR sensors are used for mapping tasks, LiDAR odometry or 3D environment reconstruction. Several of them count with a high number of vertical layers, which increase their price and prevents research groups from carrying out experiments and scientific advances. In this paper, we propose a method for augmenting point cloud data by bilinear interpolation in a Spherical Range Image. Our method improves others on the state-of-the-art by means of standard deviation filtering of the newly generated layers. The system operates at a frequency greater than 10 Hz for data interpolation up to 20 times. In addition, we present two applications for our approach such as LiDAR odometry and LiDAR-Camera fusion, obtaining better results than others that do not apply data augmentation. Finally we make available to the scientific community a package development on ROS (Robot Operating System). The code is available at https://github.com/EPVelasco/lidar-camera-fusion

Single Object Tracking methods are yet not robust enough because they may lose the target due to occlusions or changes in the target’s appearance, and it is difficult to detect automatically when they fail. To deal with these problems, we design a novel method to improve object tracking by fusing complementary types of trackers, taking advantage of each other’s strengths, with an Extended Kalman Filter to combine them in a probabilistic way. The environment perception is performed with a 3D LiDAR sensor, so we can track the object in the point cloud and also in the front-view image constructed from the point cloud. We use our tracker-fusion method in a mobile robot to follow pedestrians, also considering the dynamic obstacles in the environment to avoid them. We show that our method allows the robot to follow the target accurately during long experimental sessions where the trackers independently fail, demonstrating the robustness of our tracker-fusion strategy.

OpenStreetMaps (OSM) is currently studied as the environment representation for autonomous navigation. It provides advantages such as global consistency, a heavy-less map construction process, and a wide variety of road information publicly available. However, the location of this information is usually not very accurate locally. In this paper, we present a complete autonomous navigation pipeline using OSM information as environment representation for global planning. To avoid the flaw of local low-accuracy, we offer the novel LiDAR-based Naive-Valley-Path (NVP) method that exploits the concept of “valley” areas to infer the local path always furthest from obstacles. This behavior allows navigation always through the center of trafficable areas following the road’s shape independently of OSM error. Furthermore, NVP is a naive method that is highly sample-time-efficient. This time efficiency also enables obstacle avoidance, even for dynamic objects. We demonstrate the system’s robustness in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km with 0.24 meters of average error against the road’s center with a 19.8 ms of average sample time. Our vehicle avoids static obstacles in the road and even dynamic ones, such as vehicles and pedestrians.