This study evaluated the usability and effectiveness of robotic platforms working together with foresters in the wild on forest inventory tasks using LiDAR scanning. Emphasis was on the Universal Access principle, ensuring that robotic solutions are not only effective but also environmentally responsible and accessible for diverse users. Three robotic platforms were tested: Boston Dynamics Spot, AgileX Scout, and Bunker Mini. Spot's quadrupedal locomotion struggled in dense undergrowth, leading to frequent mobility failures and a System Usability Scale (SUS) score of 78 +/- 10. Its short, battery life and complex recovery processes further limited its suitability for forest operations without substantial modifications. In contrast, the wheeled AgileX Scout and tracked Bunker Mini demonstrated superior usability, each achieving a high SUS score of 88 +/- 5. However, environmental impact varied: Scout's wheeled design caused minimal disturbance, whereas Bunker Mini's tracks occasionally damaged young vegetation, highlighting the importance of gentle interaction with natural ecosystems in robotic forestry. All platforms enhanced worker safety, reduced physical effort, and improved LiDAR workflows by eliminating the need for human presence during scans. Additionally, the study engaged forest engineering students, equipping them with hands-on experience in emerging robotic technologies and fostering discussions on their responsible integration into forestry practices. This study lays a crucial foundation for the integration of Artificial Intelligence (AI) into forest robotics, enabling future advancements in autonomous perception, decision-making, and adaptive navigation. By systematically evaluating robotic platforms in real-world forest environments, this research provides valuable empirical data that will inform AI-driven enhancements, such as machine learning-based terrain adaptation, intelligent path planning, and autonomous fault recovery. Furthermore, the study holds high value for the international research community, serving as a benchmark for future developments in forestry robotics and AI applications. Moving forward, future research will build on these findings to explore adaptive remote operation, AI-powered terrain-aware navigation, and sustainable deployment strategies, ensuring that robotic solutions enhance both operational efficiency and ecological responsibility in forest management worldwide.

Soft wet grounds such as mud, sand, or forest soils, are difficult to navigate because it is hard to predict the response of the yielding ground and energy lost in deformation. In this article, we address the control of quadruped robots' static gait in deep mud. We present and compare six controller versions with increasing complexity that use a combination of a creeping gait, a foot-substrate interaction detection, a model-based center of mass positioning, and a leg speed monitoring, along with their experimental validation in a tank filled with mud, and demonstrations in natural environments. We implement and test the controllers on a Go1 quadruped robot and also compare the performance to the commercially available dynamic gait controller of Go1. While the commercially available controller was only sporadically able to traverse in 12 cm deep mud with a 0.35 water/solid matter ratio for a short time, all proposed controllers successfully traversed the test ground while using up to 4.42 times less energy. The results of this article can be used to deploy quadruped robots on soft wet grounds, so far inaccessible to legged robots.

The success of weed control is critical for our food security. Non-chemical weed control is a promising technique in sustainable agriculture to ensure the food security. In this review, multiple directed energy weed control methods are reviewed with a specific focus on laser and optical radiation weed control. The mechanisms of the weed control in terms of adverse ablation, radiation thermal effects, and molecular-level damages are systematically reviewed. In particular, the underlying mathematical models determining the dose and response relationship of the weed control are also analyzed for a rigorous study of the physical law of the control process. Challenges of applying the techniques into practice are also illustrated to guide practical weed control applications.

Compliant materials are indispensable for many emerging soft robotics applications. Hence, concerns regarding sustainability and end-of-life options for these materials are growing, given that they are predominantly petroleum-based and non-recyclable. Despite efforts to explore alternative bio-derived soft materials like gelatin, they frequently fall short in delivering the mechanical performance required for soft actuating systems. To address this issue, we reinforced a compliant and transparent gelatin-glycerol matrix with structure-retained delignified wood, resulting in a flexible and entirely biobased composite (DW-flex). This DW-flex composite exhibits highly anisotropic mechanical behavior, possessing higher strength and stiffness in the fiber direction and high deformability perpendicular to it. Implementing a distinct anisotropy in otherwise isotropic soft materials unlocks new possibilities for more complex movement patterns. To demonstrate the capability and potential of DW-flex, we built and modeled a fin ray-inspired gripper finger, which deforms based on a twist-bending-coupled motion that is tailorable by adjusting the fiber direction. Moreover, we designed a demonstrator for a proof-of-concept suitable for gripping a soft object with a complex shape, i.e., a strawberry. We show that this composite is entirely biodegradable in soil, enabling more sustainable approaches for soft actuators in robotics applications.