Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscience.

Cognitive neuroscience has considerable untapped potential to translate our understanding of brain function into applications that maintain, restore, or enhance human cognition. Complex, real-world phenomena encountered in daily life, professional contexts, and in the arts, can also be a rich source of information for better understanding cognition, which in turn can lead to advances in knowledge and health outcomes. Interdisciplinary work is needed for these bi-directional benefits to be realized. Our cognitive neuroscience team has been collaborating on several interdisciplinary projects: hardware and software development for brain stimulation, measuring human operator state in safety-critical robotics environments, and exploring emotional regulation in actors who perform traumatic narratives. Our approach is to study research questions of mutual interest in the contexts of domain-specific applications, using (and sometimes improving) the experimental tools and techniques of cognitive neuroscience. These interdisciplinary attempts are described as case studies in the present work to illustrate non-trivial challenges that come from working across traditional disciplinary boundaries. We reflect on how obstacles to interdisciplinary work can be overcome, with the goals of enriching our understanding of human cognition and amplifying the positive effects cognitive neuroscientists have on society and innovation.

UV reflectance in crop remote sensing: Assessing the current state of knowledge and extending research with strawberry cultivars.

Remote sensing of spectral reflectance is a crucial parameter in precision agriculture. In particular, the visual color produced from reflected light can be used to determine plant health (VIS-IR) or attract pollinators (Near-UV). However, the UV spectral reflectance studies largely focus on non-crop plants, even though they provide essential information for plant-pollinator interactions. This literature review presents an overview of UV-reflectance in crops, identifies gaps in the literature, and contributes new data based on strawberry cultivars. The study found that most crop spectral reflectance studies relied on lab-based methodologies and examined a wide spectral range (Near UV to IR). Moreover, the plant family distribution largely mirrored global food market trends. Through a spectral comparison of white flowering strawberry cultivars, this study discovered visual differences for pollinators in the Near UV and Blue ranges. The variation in pollinator visibility within strawberry cultivars underscores the importance of considering UV spectral reflectance when developing new crop breeding lines and managing pollinator preferences in agricultural fields.

Minimize Tracking Occlusion in Collaborative Pick-and-Place Tasks: An Analytical Approach for Non-Wrist-Partitioned Manipulators.

Several industrial pick-and-place applications, such as collaborative assembly lines, rely on visual tracking of the parts. Recurrent occlusions are caused by the manipulator motion decrease line productivity and can provoke failures. This work provides a complete solution for maintaining the occlusion-free line of sight between a variable-pose camera and the object to be picked by a 6R manipulator that is not wrist-partitioned. We consider potential occlusions by the manipulator as well as the operator working at the assembly station. An actuated camera detects the object goal (part to pick) and keeps track of the operator. The approach consists of using the complete set of solutions obtained from the derivation of the univariate polynomial equation solution to the inverse kinematics (IK). Compared to numerical iterative solving methods, our strategy grants us a set of joint positions (posture) for each root of the equation from which we extract the best (minimizing the risks of occlusion). Our analytical-based method, integrating collision and occlusion avoidance optimizations, can contribute to greatly enhancing the efficiency and safety of collaborative assembly workstations. We validate our approach with simulations as well as with physical deployments on commercial hardware.

From Design to Deployment: Decentralized Coordination of Heterogeneous Robotic Teams.

Many applications benefit from the use of multiple robots, but their scalability and applicability are fundamentally limited when relying on a central control station. Getting beyond the centralized approach can increase the complexity of the embedded software, the sensitivity to the network topology, and render the deployment on physical devices tedious and error-prone. This work introduces a software-based solution to cope with these challenges on commercial hardware. We bring together our previous work on Buzz, the swarm-oriented programming language, and the many contributions of the Robotic Operating System (ROS) community into a reliable workflow, from rapid prototyping of decentralized behaviors up to robust field deployment. The Buzz programming language is a hardware independent, domain-specific (swarm-oriented), and composable language. From simulation to the field, a Buzz script can stay unmodified and almost seamlessly applicable to all units of a heterogeneous robotic team. We present the software structure of our solution, and the swarm-oriented paradigms it encompasses. While the design of a new behavior can be achieved on a lightweight simulator, we show how our security mechanisms enhance field deployment robustness. In addition, developers can update their scripts in the field using a safe software release mechanism. Integrating Buzz in ROS, adding safety mechanisms and granting field updates are core contributions essential to swarm robotics deployment: from simulation to the field. We show the applicability of our work with the implementation of two practical decentralized scenarios: a robust generic task allocation strategy and an optimized area coverage algorithm. Both behaviors are explained and tested with simulations, then experimented with heterogeneous ground-and-air robotic teams.