Progress 11/01/23 to 10/31/24
Outputs
Target Audience:This project relies on a strong collaboration with the Almond Board of California (www.almonds.com). During this third year with conducted field tests in two commercial orchards (one almond, one pistachio) situated in Merced County. Moreover, the software developed was extensively tested on the field and on campus. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?This grant has primarily supported a PhD student, Azin Shamshirgaran, who successfully defended her dissertation, "Environmental Monitoring with Budget Constraints Using Reinforcement Learning," in Summer 2024 and is now employed in industry. Her dissertation acknowledges the critical support provided by this grant and offers a comprehensive summary of the research conducted to date. Following Azin's graduation, a new MS student, Sanjeeb Day, has taken over as the Graduate Student Researcher. Sanjeeb is currently conducting additional validation and experiments to enhance the planners developed during Azin's work. It's worth noting that during Fall 2023, Azin served as a teaching assistant to meet her graduation requirements. Although she was not financially supported by this grant during that semester, she continued contributing to the project. How have the results been disseminated to communities of interest?Scientific papers describing the results developed in this project have appeared in leading journals and international conferences in robotics and automation, as detailed in the results section. A particularly significant publication was a joint paper in collaboration with UC Riverside, which comprehensively described the end-to-end system featuring both planning and sensing capabilities. This paper was published in the IEEE Robotics and Automation Magazine (RAM) as part of a special issue focusing on robotics and automation in agriculture. RAM, recognized as the flagship magazine of the IEEE Robotics and Automation Society, boasts an estimated international readership of 14,000. Additionally, Azin's research was presented during the annual meeting of the NSF Engineering Research Center for the Internet of Things for Precision Agriculture (IoT4Ag), which convened at UC Merced in June 2024. The project's research output also included other papers that had been accepted but were not yet published as of 10/31/2024. What do you plan to do during the next reporting period to accomplish the goals?During the next year we anticipate continuing the integration of our efforts with UC Riverside, and perform more field validation. Ideally, this will lead to additional results that will lead to more publications and insights on usability for farmers.
Impacts
What was accomplished under these goals?
This project is being jointly developed by UC Merced and UC Riverside, and consequently the four objectives are split between the two institutions. The UC Merced team is mostly in charge of objective 3 (planning) and objective 4 (evaluation). During year four we have extended, refined and tested in the field the planning algorithms developed during the first phase of this project, with an emphasis on the use of multiple robots. Multi-robot informative path planning addresses the challenge of determining optimal routes for a team of robots to visit a set of locations that maximize the informativeness of the collected data for reconstructing an unknown scalar field - soil moisture or leaf water potential in our case. In the budgeted version of this problem, each robot operates under a travel budget that restricts the total distance it can traverse. This problem has significant applications in precision agriculture, where robots are deployed to gather spatially distributed measurements to estimate critical scalar parameters. In year four we developed an online, distributed multi-robot sampling algorithm based on Monte Carlo Tree Search (MCTS). The proposed approach enables each robot to iteratively select its next sampling location by leveraging communication with other robots and dynamically accounting for its remaining travel budget. This design ensures that sampling decisions are both cooperative and budget-aware, facilitating effective data collection under constraints. We rigorously evaluated our algorithm across various team sizes and environmental scenarios, benchmarking its performance against four baseline methods. Experimental results demonstrated that our approach consistently achieves superior performance under tight budget constraints, as evidenced by the collection of measurements that yield significantly lower scalar field reconstruction errors compared to the baselines. These findings highlighted the potential of our method for real-world applications requiring efficient, budget-limited data collection in complex, multi-robot systems. On the experimental side,building upon the autonomous navigation stack we established in the past (described in last year's technical report), we have continued to test and evaluate the performance of our algorithms in various testbeds. Experimental results confirm the results anticipated in simulation. The results produced in year 4 have been reported in four papers listed under the "products" section and in a PhD dissertation.
Publications
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2023
Citation:
A. Dechemi, D. Chatziparaschis, J. Chen, M. Campbell, A. Shamshirgaran, C. Mucchiani, A. Roy-Chowdhury, S. Carpin, K. Karydis. Robotic Assessment of a Crop's Need for Watering. In IEEE Robotics and Automation Magazine, 30(4):52-67, 2023
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
A. Shamshirgaran, S. Manjanna, S. Carpin. "Distributed Multi-robot Online Sampling with Budget Constraints." Proceedings of the 2024 IEEE International Conference on Robotics and Automation 12658-12664.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
E. Sani, A Sgorbissa, S. Carpin. "Improving the ROS 2 Navigation Stack with Real-Time Local Costmap Updates for Agricultural Applications" Proceedings of the 2024 IEEE International Conference on Robotics and Automation, 17701-17707
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Progress 11/01/22 to 10/31/23
Outputs
Target Audience:This project relies on a collaboration with the Almond Board of California (www.almonds.com). During this third year with conducted field tests in two commercial orchards (one almond, one pistachio) situated in Merced County. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?During the reporting period, this grant has supported one PhD student (Azin Shamshirgaran) between 11/01/2022 and 8/17/2023. Azin Shamshirgaran is the same student who has formerly worked on this grant. In the last part of the reporting period she has not been financially supported by the grant because she had to serve as Teaching Assistant to fulfil her graduation requirements, but her individual research activites continued to be related to the planning algorithms described above. In addition, another PhD student (Carlos Diaz Alvarenga) has devoted six weeks to the planning algorithm described under the "Products" section. Finally, international visiting student Ettore Sani worked over summer to develop the autonomous navigation stack described above. How have the results been disseminated to communities of interest?During this period, we have published three papers at international robotics conference (see section "Products" for details.) In addition, we have also organized a workshop for academics, government, and practitioners at the IEEE/RSJ International Conference on Intelligent Robots and System (October 2023 -- see also section "Other Products".) What do you plan to do during the next reporting period to accomplish the goals?During the next year we anticipate continuing the integration of our efforts with UC Riverside, and perform more field validation.
Impacts
What was accomplished under these goals?
This project is being jointly developed by UC Merced and UC Riverside, and consequently the four objectives are split between the two institutions. The UC Merced team is mostly in charge of objective 3 (planning) and objective 4 (evaluation). During year three we have made progress in three directions. First, we have revised our formerly developed informative path planners using Gaussian Process inference to account for multiple cooperating robots. Our fully distributed method leverages the underlying properties of Gaussian Process regression to promote dispersion using minimal information sharing. Coordination between the agents is obtained with minimal information exchange and by leveraging the mathematical properties of GPs to promote dispersion. The approach is fully distributed and each robot just broadcasts to the rest of the team the limited information consisting of the locations where it has collected data, the value it measured, and its unique identifier---no more than a handful of bytes at a very low frequency. No other communication is required and robots never exchange their individual plans or models. In addition, each robot uses a refinement of our recently developed planner for stochastic orienteering to ensure that it reaches the final location before it runs out energy. Through extensive simulations we demonstrate that this approach ensures robots collect samples in areas leading to a more accurate reconstructions of the underlying unknown scalar field. Second, one of the planners we have developed in the past has been passed to our partner institution UC Riverside and integrated in the fully working system to decide where to sample the next leaf. This end-to-end system has been described in a published paper that will be included in next year's report because it appeared in December 2023. Finally, we have developed a software pipeline based on ROS2 to enable robust autonomous navigation in orchards (almonds, pistachio, etc.). The ROS 2 Navigation Stack (Nav2) has emerged as the standard for navigation, but when used in outdoor environments such as orchards and vineyards, its functionality is notably limited by the presence of obstacles and/or situations not commonly found in indoor settings. One such example is given by tall grass and weeds that can be perceived as obstacles by LiDAR sensors. To overcome these limitations and enable field testing, a new, lightweight approach to address this challenge and improve outdoor robot navigation. More precisely, we developed a system that using a depth camera performs pixel level classification on the images, and in real time injects corrections into the local cost map, thus enabling the robot to traverse areas that would otherwise be avoided. This approach has been implemented and validated on a Clearpath Husky navigating in two different orchards in Merced county.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
A. Shamshirgaran, S. Carpin. "Reconstructing a Spatial Field with an Autonomous Robot Under a Budget Constraint". Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, 8963-8970
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
C. Diaz Alvarenga, S. Carpin. "Track, stop, and eliminate: an algorithm to solve stochastic orienteering problems using MCTS." Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, 9894-9901
- Type:
Book Chapters
Status:
Awaiting Publication
Year Published:
2024
Citation:
L. Booth, S. Carpin. "Distributed estimation of scalar fields with implicit coordination." Distributed Autonomous Robotics Systems (DARS) 2022.
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Progress 11/01/21 to 10/31/22
Outputs
Target Audience:This project relies on a strong collaboration with the Almond Board of California (www.almonds.com). During this second year we have engaged with scientists and practioners from the board to keep our research aligned with the interests of the growers they represent. In particular, on May 13, 2022 we have visited an almond orchard located near Merced (CA) and together with the owner we actively took part in a complete set of pressure chamber measurements throughout the orchard. This process has allowed us to refine our approach for the selection of the sampling locations. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?This grant is fully supporting one PhD student (Azin Shamshirgaran) who started in January 2020. Various undergraduate students have also been involved as lab assistants in support of this project. How have the results been disseminated to communities of interest?During this period, we have published three papers at international robotics conference (see section "Products" for details.) What do you plan to do during the next reporting period to accomplish the goals?During the next year, we will integrate the device developed at UC Riverside with the robots currently present at UC Merced, and commence field validation (objective 4). While this was anticipated to be done in year two, we are postponing this goal to year three to take advantage of a new manipulator (TM-900) we have secured at no additional cost to this project. With our commercial partner we will continue to engage almond growers to get domain feedback about our proposed solution. In year three we anticipate testing our sampling algorithms in the field.
Impacts
What was accomplished under these goals?
This project is being jointly developed by UC Merced and UC Riverside, and consequently the four objectives are split between the two institutions. The UC Merced team is mostly in charge of objective 3 (planning) and objective 4 (evaluation). As the partner institution is still working towards finalizing the design and implementation of the robotized pressure chamber, objective 4 has not been pursued yet, and we have mostly been concerned with the planning aspect. With regard to the planning problem tackled at UC Merced, in year two we have studied the information path planning problem (IPP) for a single robot in a stochastic environment with static obstacles subject to a preassigned constraint on the distance it can travel. Given a set of candidate sampling locations, i.e., the location of the sentinel trees where measurements should be made, the objective is to determine a path for the robot that allows to visit as many sampling locations as possible to accurately reconstruct an unknown underlying scalar field while not exceeding the assigned travel budget. Owing to the fact that Gaussian Processes (aka kriging) are one of the leading approaches for modeling physical phenomena such as soil moisture, our algorithm embraces this representation and builds upon it. More specifically, our algorithm balances exploration and exploitation to determine a sequence of locations ensuring that a preassigned final site is reached before the budget is consumed. Using mutual information as a reward criterion, as well as a generative model to predict consumed energy, the algorithm iteratively determines where to sample next, and when to end the mission. Our findings are validated in simulation in various scenarios and lead to a better reconstruction with less failures when compared with other methods. In our formulation, "better reconstruction" means a reconstruction with smaller error measured in terms of mean squared error (MSE).
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
" A. Shamshirgaran, S. Carpin. "Reconstructing a Spatial Field with an Autonomous Robot Under a Budget Constraint". Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, 8963-8970
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
" T. Thayer, S. Carpin. "Solving Stochastic Orienteering Problems with Chance Constraints Using Monte Carlo Tree Search". Proceedings of the 2022 IEEE International Conference on Automation Science and Engineering, 1170-1177
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
S. Carpin. "Scheduling problems for robotics in precision agriculture". Proceedings of the 2022 IEEE International Symposium on Circuits and Systems", 1357-1361
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Progress 11/01/20 to 10/31/21
Outputs
Target Audience:This project relies on a strong collaboration with the Almond Board of California (www.almonds.com). During this first year we have engaged with scientists and practioners from the board to keep our research aligned with the interests of the growers they represent. More precisely, on May 25, 2021 we have visited an almond orchard located near Modesto (CA) and spent a day with board members performing measurements of leaf water potential using a pressure chamber. This has informed the design of the mechanism being developed at UC Riverside. We have also regularly met with board members to update them about our progress and collect their feedback about our iterative design process. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?
Nothing Reported
How have the results been disseminated to communities of interest?During this persiod, we have published two papers at a top international robotics conference (IROS -- see section "Products") and we have submitted two mor to another top international robotics conference (ICRA -- see section "Products"). What do you plan to do during the next reporting period to accomplish the goals?During the next (second) year, we will integrate the device developed at UC Riverside with the robots currently present at UC Merced, and commence field validation (objective 4). With our commercial partner (Almond Board of California), we are scheduling meetings with almong growers to get domain expertise about how they select the sentinel trees to monitor during the growing season to determine water potential. Our objective for the next year are therefore: - integrate the pressure chamber with the mobile robot; - integrate our planning algorithms with inputs provided by almond growers regarding "interesting" regions to sample; - commence field validation.
Impacts
What was accomplished under these goals?
This project is being jointly developed by UC Merced and UC Riverside, and consequently the four objectives are split between the two institutions. The UC Merced team is mostly in charge of objective 3 (planning) and objective 4 (evaluation). As the partner institution is still working towards finalizing the design and implementation of the robotized pressure chamber, objective 4 has not been pursued during the first year, and we have mostly been concerned with the planning aspect. Water potential measurement is currently implemented using very sparse sampling, and the underlying objective of this project is to develop a robotic system that can scale up the efficiency of this process. One of the complicating factors in this process is that measurements must be taken during a restricted temporal window -- typically between 11am and 3pm. Additionally, robots have limited battery life and therefore it is necessary to solve an optimization problem where the input is a set of locations where data should be acquired (sentinel trees) as well as a temporal deadline and an energy budget. The output is a schedule (or route) that determines the subset of sentinel trees that should be visited and the ordering. A further complicating aspect is that the time necessary to collect a sample is not deterministic, but is rather influenced by numerous factors (e.g., it may take quite some time for the robot to be able to collect a leaf). In the four papers produced during this period (two published and two submitted), we have explored different techniques to solve this optimization problem. The two papers appeared at IROS refine a previous planning approach that cast this problem as an instance of the orienteering problem and solve them using a method based on constrained Markov Decision Process. The emphasis of these two works have been in decreasing the computational costs and make the methods more robust to unexpected events occurring during execution (e.g., large deviations from the expected costs). In addition, we have developed two new approaches to solve the aforementioned optimization problem. The first relies on reinforcement learning and tackles the problem of learning from simulated experience how to select the next location to visit. The second instead relies on Monte-Carlo tree search, and tackles the problem of dynamic replanning when low probability events occur (e.g., abnormally high times to complete an operation). Both these new methods have been described in two papers submitted to the 2022 ICRA conference. @font-face { panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic- mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0;}@font-face { panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic- mso-font-pitch:variable; mso-font-signature:-536859905 -1073732485 9 0 511 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0in; mso-pagination:widow-orphan; ; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast- mso-fareast-theme-font:minor-latin; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi- mso-bidi-theme-font:minor-bidi;}p {mso-style-noshow:yes; mso-style-priority:99; mso-margin-top-alt:auto; margin-right:0in; mso-margin-bottom-alt:auto; margin-left:0in; mso-pagination:widow-orphan; ; mso-fareast-}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast- mso-fareast-theme-font:minor-latin; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi- mso-bidi-theme-font:minor-bidi;}div.WordSection1 {page:WordSection1;}
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
T. Thayer, S. Carpin. "A Resolution Adaptive Algorithm for the Stochastic Orienteering Problem with Chance Constraints" Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, 6388-6395
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
T. Thayer, S. Carpin. "A Fast Algorithm for Stochastic Orienteering with Chance Constraints." Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, 7398-7945
- Type:
Conference Papers and Presentations
Status:
Submitted
Year Published:
2021
Citation:
A. Shamishirgaran, S. Carpin. "Spatial field learning with an autonomous robot". Proceedings of the 2022 IEEE International Conference on Robotics and Automation.
- Type:
Conference Papers and Presentations
Status:
Submitted
Year Published:
2021
Citation:
T. Thayer, S. Carpin. "Solving Stochastic Orienteering Problems with Chance Constraints Using Monte Carlo Tree Search". Proceedings of the 2022 IEEE International Conference on Robotics and Automation.
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