The disassembly of power batteries poses significant challenges due to their complex sources, diverse types, variations in design and manufacturing processes, and diverse service conditions. Human memory capacity and robot cognitive and understanding capabilities are limited when faced with different dismantling tasks for end-of-life power batteries. Insufficient human-computer interaction capabilities greatly hinder the efficiency of human-robot collaboration (HRC) operations. The existing HRC relies heavily on the experience of operators, while the existing disassembly system fails to update new disassembly strategies in real time when facing new battery varieties. Therefore, this paper proposes an augmented reality-assisted human-robot collaboration (AR-HRC) power battery dismantling system based on transfer learning. It consists of three modules: AR-HRC knowledge modeling, dismantling subgraph similarity assessment, and strategy transfer update. The AR-HRC knowledge modeling module aims to establish an intelligent mapping from tasks to collaborative strategies based on part features. Based on the evaluation of task similarity, the mobility assessment model divides subtasks into similar and dissimilar classes. For similar subtasks, the original dismantling strategy can be applied to the current task. However, for different subtasks, operators can issue instructions to the AR-HRC system through the human-computer interaction function of AR and develop new collaborative strategies based on actual conditions. Finally, a case study of power battery dismantling is conducted, and the results show that compared to traditional pre-programmed assembly, this system can improve dismantling efficiency and reduce cognitive burden.

The level of past industry participation in registering herbicide minor crop uses has been a function of active ingredient discovery output and the combination of incentives and barriers that drive minor use registration decisions. Over the past 10 yr there have been several external factors negatively impacting the rate of new herbicide introductions by industry. These include industry consolidation, a decrease in the global value of the conventional herbicide market, adoption of Herbicide-Resistant Crop (HRC) technology, a substantial increase in required regulatory activity primarily through reregistration programs, and increased costs of discovery research and product development. The increased cost of conducting business in an increasingly competitive market has undoubtedly influenced herbicide registrants to adopt different discovery strategies. Economics dictate registration efforts only towards crops that will create a significant, positive return on investment. In most cases, development of new herbicides for minor crops is not economically viable due to low or negative return on investment and disproportionate liability risk. Therefore, to motivate increased participation, companies need incentives and mechanisms to mitigate risk and registration barriers. Increased data protection and the Interregional Project 4 (IR-4) program, a cooperative government program with the goal of developing data to support regulatory clearances of pest control products for specialty crops, are current government programs in place that provide incentives and defray cost. Other incentives should be explored to make minor crops more attractive targets. However, product registration, liability risk, and dedicating the necessary resources to adequately research crop selectivity are still major economic barriers. Creative solutions that ensure companies are not unreasonably exposed to yield loss claims would remove a primary reason why companies are reluctant to register herbicides for minor crops.

Multipath is one of the main error sources in global navigation satellite system (GNSS) positioning. The high-resolution correlator (HRC) is a multipath mitigation technique well known for its outstanding performance for mid-delayed multipath, but still has a remaining error for the short-delayed multipath. This paper proposes a modified HRC scheme that can remove or reduce the error for short-delayed multipath signals. It estimates the HRC tracking error and augments the conventional HRC with the estimates. The method was implemented with a software receiver and the test results show short-delayed multipath-induced errors were reduced to about one third of those from the conventional HRC.