Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks.

Abstract
Soil Moisture (SM) is one of the most critical factors for a crop's growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR's output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner.

Abstract
Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations.

Abstract
The manipulation of deformable objects poses a significant challenge for the automotive industry. In particular, the assembly of flexible cables and wire-harnesses in vehicles is still performed manually as there is yet to be a reliable and general solution for this problem. This paper presents a simple yet efficient motion planning algorithm to mount a flexible wire in an assembly jig, where the wire must traverse a set of forks in order. The algorithm uses a heuristic based on a set of control points to guide the wire’s movement. Various controlled assembly scenarios are built in simulation using MuJoCo, a physics engine that can emulate the dynamics of Deformable Linear Objects (DLO). Experimental results in simulation demonstrated that the amount and orientation of the forks has a large impact in the solution’s performance and highlighted several key ideas and challenges moving forward. Thus, this work serves as a stepping stone towards the development of more complete solutions, capable of assembling flexible items in vehicles. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
In recent years, scientific and technological advances in robotics, have enabled the development of disruptive solutions for human interaction with the real world. In particular, the application of robotics to support people with physical disabilities, improved their life quality with a high social impact. This paper presents a stereo image based perception solution for autonomous wheelchairs navigation. It was developed to extend the Intellwheels project, a development platform for intelligent wheelchairs. The current version of Intellwheels relies on a planar scanning sensor, the Laser Range Finder (LRF), to detect the surrounding obstacles. The need for robust navigation capabilities means that the robot is required to precept not only obstacles but also bumps and holes on the ground. The proposed stereo-based solution, supported in passive stereo ZED cameras, was evaluated in a 3D simulated world scenario designed with a challenging floor. The performance of the wheelchair navigation with three different configurations was compared: first, using a LRF sensor, next with an unfiltered stereo camera and finally, applying a stereo camera with a speckle filter. The LRF solution was unable to complete the planned navigation. The unfiltered stereo camera completed the challenge with a low navigation quality due to noise. The filtered stereo camera reached the target position with a nearly optimal path.