Test out our new Bento Search

Tracking and catching moving objects is an important ability for robots in a dynamic world. Whilst some objects have highly predictable state evolution e.g., the ballistic trajectory of a tennis ball, reactive targets alter their behavior in response to motion of the manipulator. Reactive applications range from gently capturing living animals such as snakes or fish for biological investigations, to smoothly interacting with and assisting a person. Existing works for dynamic catching usually perform target prediction followed by planning, but seldom account for highly non-linear reactive behaviors. Alternatively, Reinforcement Learning (RL) based methods simply treat the target and its motion as part of the observation of the world-state, but perform poorly due to the weak reward signal. In this work, we blend the approach of an explicit, yet learned, target state predictor with RL. We further show how a tightly coupled predictor which ‘observes’ the state of the robot leads to significantly improved anticipatory action, especially with targets that seek to evade the robot following a simple policy. Experiments show that our method achieves an 86.4% (open plane area) and a 73.8% (room) success rate on evasive objects, outperforming monolithic reinforcement learning and other techniques. We also demonstrate the efficacy of our approach across varied targets and trajectories. All code, data, and additional videos are at this GitHub link: https://kl-research.github.io/dyncatch.

Underwater soft grippers exhibit potential for applications such as monitoring, research, and object retrieval. However, existing underwater gripping techniques frequently cause disturbances to ecosystems. In response to this challenge, we present a novel underwater gripping framework comprising a lightweight gripper affixed to a custom submarine pod deployable via drone. This approach minimizes water disturbance and enables efficient navigation to target areas, enhancing overall mission effectiveness. The pod allows for underwater motion and is characterized by four degrees of freedom. It is provided with a custom buoyancy system, two water pumps for differential thrust and two for pitching. The system allows for buoyancy adjustments up to a depth of 6 meters, as well as motion in the plane. The 3-fingered gripper is manufactured out of silicone and was successfully tested on objects with different shapes and sizes, demonstrating a maximum pulling force of up to 8 N when underwater. The reliability of the submarine pod was tested in a water tank by tracking its attitude and energy consumption during grasping maneuvers. The system also accomplished a successful mission in a lake, where it was deployed on a hexacopter. Overall, the integration of this system expands the operational capabilities of underwater grasping, makes grasping missions more efficient and easy to automate, as well as causing less disturbance to the water ecosystem.

Purpose: Summer catch crop (CC) has been introduced into the vegetable rotating system in greenhouse fields to reduce nitrogen (N) losses through crop uptake and residual N immobilization. However, the effects of planting sorghum with high N uptake and biomass, and biological nitrification inhibition (BNI) potential as a CC on soil N dynamics and subsequent crop yield remain unclear. Methods: In the two-year field experiment, the comprehensive effects of planting sorghum as CC on subsequent eggplant yield, soil mineral N dynamics, ammonia-oxidizing archaea (AOA) and bacteria (AOB) amoA gene abundances were determined, in comparison to the sweet corn and fallow treatments. Results: Compared to the fallow and sweet corn, planting sorghum as CC increased subsequent eggplant yield by 24.88% and 18.94% in the 2014–2015 and 2015–2016 over-winter growing season, respectively. CC planting reduced soil nitrate (NO3−-N) accumulation during the summer fallow season. Sorghum planting could significantly maintain higher level of ammonium (NH4+-N) concentration during the summer fallow season and the first month of succeeding over-winter season. In addition, sorghum planting reduced soil net nitrifying potential, which could be partially attributed to the decreased amoA gene abundance of AOA at the 0–30 and 30–60 cm soil layers and AOB at 0–30 cm soil layer. Conclusion: We conclude that planting sorghum in the summer fallow season is a promising strategy to retain soil NH4+-N, reduce soil NO3−-N accumulation, and enhance subsequent eggplant yield. [ABSTRACT FROM AUTHOR]

No summary available.

This study aimed to characterise the recreational harvesting of wedge clams (Donax trunculus) in the Algarve coast (southern Portugal) and estimate its annual catches by recreational harvesters (RHs). For this purpose, 50 harvesting surveys were performed along the sandy intertidal during one-year (May 2022 - April 2023), roughly on a weekly basis during suitable tidal ranges (≤ 1.0 m). Overall, RHs were gender-balanced and older harvesters (≥ 65 years-old) prevailed. RHs were clearly more numerous from late spring to summer, especially in August, with most RHs collecting wedge clams with foot / hand and only a minority (≈20 %) also using a shrimp-net. RHs catches comprised mostly D. trunculus below the minimum conservation reference size (MCRS = 25 mm in shell length) legally stipulated for this species. The estimation of RHs annual catches of D. trunculus was based on standardised catches per harvester (number and weight hour−1), extrapolated using the total number of RHs and suitable tides for wedge clams recreational harvesting during the one-year study period. Collecting and processing geolocated data on RHs activity allowed mapping the spatial-temporal distribution of the recreational harvesting effort targeting wedge clams, further confirming the importance of spatial data as a support tool for management and decision-making processes. This study further confirmed the need to raise awareness and inform RHs about the best harvesting practices, aiming ultimately to improve the assessment and promote the long-term sustainable management of this recreational activity and shellfish resource.

Catch allocation models can split aggregated mixed-species catches into individual species for stock assessments and fisheries management. In this paper, we evaluate a suite of these models for splitting mixed ‘trawl whiting’ catches into eastern school whiting (Sillago flindersi) and stout whiting (S. robusta) allocations for a commercial ocean prawn trawl fishery in New South Wales (NSW), Australia. Accuracy of the models was evaluated against a scientific observer survey which accurately recorded species catches, and we compared the modelled allocations to an existing coarse ‘rule-based’ allocation. There was no single best structure for the allocation model, but the most successful models included depth as a covariate because this helped split species along habitat preferences. The model-based allocations reduced trip-level error by around 40 % compared to the existing rules, and removed an existing bias in total catch estimates. This led to altered time series of catches and catch-per-unit-effort, especially for northern zones. When data were analysed for the entire NSW region, the catch allocation process (existing or modelled) had little impact on resulting indices of relative abundance for each whiting species, even when using a spatio-temporal standardization model. This was likely due to changes affecting scale rather than trend and our indices being rescaled to better compare time periods. Therefore, past stock assessments relying on statewide indices derived from existing rule-based allocations are likely reliable. Nevertheless, the modelled allocations were more accurate at a local and zonal scale, which will enable analyses with a finer spatial resolution in future stock assessments. Additional observer surveys are an important tool for ongoing improvement and validation of our allocation models.