This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Induction heating (IH) technology is nowadays the heating technology of choice in many industrial, domestic, and medical applications due to its advantages regarding efficiency, fast heating, safety, cleanness, and accurate control. Advances in key technologies, i.e., power electronics, control techniques, and magnetic component design, have allowed the development of highly reliable and cost-effective systems, making this technology readily available and ubiquitous. This paper reviews IH technology summarizing the main milestones in its development and analyzing the current state of art of IH systems in industrial, domestic, and medical applications, paying special attention to the key enabling technologies involved. Finally, an overview of future research trends and challenges is given, highlighting the promising future of IH technology.

/pdf/induction-heating-technology-and-its-applications-past-218lub4zxh.pdf

Induction Heating Technology and Its Applications: Past Developments, Current Technology, and Future Challenges

https://www.fs.fed.us/nrs/pubs/jrnl/2013/nrs_2013_dandios_001.pdf

High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision

Lecture notes in control and information sciences

In this paper, we propose a formation control system for deforming and transporting simultaneously a de-formable object with a team of robots, modeled with double-integrator dynamics. The goal is to reach a target configuration, defined as a combination of shape, scale, orientation and position of the formation. We augment this controller with a set of control barrier functions (CBFs). The CBFs allow us to satisfy fundamental constraints for the success of the task: avoidance of agent-to-agent, agent-to-obstacle and object-to-obstacle collisions, and of excessive stretching. We test the performance of our proposal in different simulation scenarios.

Multirobot control with double-integrator dynamics and control barrier functions for deformable object transport

We propose a new approach to control the shape of deformable objects with robots. Specifically, we consider a fixed-length elastic linear object lying on a 2D workspace. Our main idea is to encode the object's deformation behavior in an offline constant Jacobian matrix. To derive this Jacobian, we use geometric deformation modeling and combine recent work from the fields of deformable object control and multirobot systems. Based on this Jacobian, we then propose a robotic control law that is capable of driving a set of shape features on the object toward prescribed values. Our contribution relative to existing approaches is that at run-time we do not need to measure the full shape of the object or to estimate/simulate a deformation model. This simplification is achieved thanks to having abstracted the deformation behavior as an offline model. We illustrate the proposed approach in simulation and in experiments with real deformable linear objects.

An offline geometric model for controlling the shape of elastic linear objects

Autonomous herding research is becoming increasingly relevant. In this work, a model for sheep detection in herds from aerial video sequences is proposed, using the convolutional neural network Mask R-CNN. Several trainings with different datasets have been performed for achieving the model. An improvement in the detection metrics, through a visual tracking tool, allows not only detecting the individual sheeps in the herd, but also tracking them along the different frames in aerial video sequences. This system could be used, for example, in a drone to carry out livestock supervision, in addition to obtaining metrics that allow knowing the status of the herd. Finally, the method has been validated using several tests on images and videos of livestock in real outdoor environments. 

Gonzalo López-Nicolás

Papers

Multirobot control with double-integrator dynamics and control barrier functions for deformable object transport

An offline geometric model for controlling the shape of elastic linear objects

Detection and Tracking of Livestock Herds from Aerial Video Sequences