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1. Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments

   https://doi.org/10.1177/1729881421999587  

   Folsom, Larkin; Ono, Masahiro; Otsu, Kyohei; Park, Hyoshin (March 2021, International Journal of Advanced Robotic Systems)

   null (Ed.)

   Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route. 

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2. Occasionally Observed Piecewise-Deterministic Markov Processes

   https://doi.org/10.1007/s42967-025-00500-7  

   Gee, Marissa; Vladimirsky, Alexander (September 2025, Communications on Applied Mathematics and Computation)

   Not AvailablePiecewise-Deterministic Markov processes (PDMPs) are often used to model abrupt changes in the global environment or capabilities of a controlled system. This is typically done by considering a set of “operating modes” (each with its own system dynamics and performance metrics) and assuming that the mode can switch stochastically, while the system state evolves. Such models have a broad range of applications in engineering, economics, manufacturing, robotics, and biological sciences. Here, we introduce and analyze an “occasionally observed” version of mode-switching PDMPs. We show how such systems can be controlled optimally if the planner is not alerted to mode switches as they occur but may instead have access to infrequent mode observations. We first develop a general framework for handling this through dynamic programming on a higher dimensional mode-belief space. While quite general, this method is rarely practical due to the curse of dimensionality. We then discuss assumptions that allow for solving the same problem much more efficiently,with the computational costs growing linearly (rather than exponentially) with the number of modes. We use this approach to derive Hamilton-Jacobi-Bellman (HJB) PDEs and quasi-variational inequalities encoding the optimal behavior for a variety of planning horizons (fixed, infinite, indefinite, and random) and mode-observation schemes (at fixed times or on demand). We discuss the computational challenges associated with each version and illustrate the resulting methods on test problems from surveillance-evading path planning. We also include an example based on robotic navigation: a Mars rover that minimizes the expected time to target while accounting for the possibility of unobserved/incremental damages and dynamics-altering breakdowns. 

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3. Integral reinforcement learning‐based approximate minimum time‐energy path planning in an unknown environment

   https://doi.org/10.1002/rnc.5122  

   He, Chenyuan; Wan, Yan; Gu, Yixin; Lewis, Frank_L (October 2020, International Journal of Robust and Nonlinear Control)

   Summary Path planning is a fundamental and critical task in many robotic applications. For energy‐constrained robot platforms, path planning solutions are desired with minimum time arrivals and minimal energy consumption. Uncertain environments, such as wind conditions, pose challenges to the design of effective minimum time‐energy path planning solutions. In this article, we develop a minimum time‐energy path planning solution in continuous state and control input spaces using integral reinforcement learning (IRL). To provide a baseline solution for the performance evaluation of the proposed solution, we first develop a theoretical analysis for the minimum time‐energy path planning problem in a known environment using the Pontryagin's minimum principle. We then provide an online adaptive solution in an unknown environment using IRL. This is done through transforming the minimum time‐energy problem to an approximate minimum time‐energy problem and then developing an IRL‐based optimal control strategy. Convergence of the IRL‐based optimal control strategy is proven. Simulation studies are developed to compare the theoretical analysis and the proposed IRL‐based algorithm. 

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4. Using evidence of student thinking as resources in a digital collaborative platform

   Park, Sunyoung; Edson, Alden J (August 2024, Mathematics education research and practice)

   Learning mathematics in a student-centered, problem-based classroom requires students to develop mathematical understanding and reasoning collaboratively with others. Despite its critical role in students’ collaborative learning in groups and classrooms, evidence of student thinking has rarely been perceived and utilized as a resource for planning and teaching. This is in part because teachers have limited access to student work in paper-and-pencil classrooms. As an alternative approach to making student thinking visible and accessible, a digital collaborative platform embedded with a problem-based middle school mathematics curriculum is developed through an ongoing design-based research project (Edson & Phillips, 2021). Drawing from a subset of data collected for the larger research project, we investigated how students generated mathematical inscriptions during small group work, and how teachers used evidence of students’ solution strategies inscribed on student digital workspaces. Findings show that digital flexibility and mobility allowed students to easily explore different strategies and focus on developing mathematical big ideas, and teachers to foreground student thinking when facilitating whole-class discussions and planning for the next lesson. This study provides insights into understanding mathematics teachers’ interactions with digital curriculum resources in the pursuit of students’ meaningful engagement in making sense of mathematical ideas. 

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5. Toward Safety-Aware Informative Motion Planning for Legged Robots

   Teng, Sangli; Gong, Yukai; Grizzle, Jessy; Ghaffari, Maani (March 2021, ArXivorg)

   null (Ed.)

   This paper reports on developing an integrated framework for safety-aware informative motion planning suitable for legged robots. The information-gathering planner takes a dense stochastic map of the environment into account, while safety constraints are enforced via Control Barrier Functions (CBFs). The planner is based on the Incrementally-exploring Information Gathering (IIG) algorithm and allows closed-loop kinodynamic node expansion using a Model Predictive Control (MPC) formalism. Robotic exploration and information gathering problems are inherently path-dependent problems. That is, the information collected along a path depends on the state and observation history. As such, motion planning solely based on a modular cost does not lead to suitable plans for exploration. We propose SAFE-IIG, an integrated informative motion planning algorithm that takes into account: 1) a robot’s perceptual field of view via a submodular information function computed over a stochastic map of the environment, 2) a robot’s dynamics and safety constraints via discrete-time CBFs and MPC for closedloop multi-horizon node expansions, and 3) an automatic stopping criterion via setting an information-theoretic planning horizon. The simulation results show that SAFE-IIG can plan a safe and dynamically feasible path while exploring a dense map. 

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