adding acc papers

Author: dcherenson

content/papers/2026/2025-strong_r_icra.md

@@ -23,7 +23,7 @@ video:
 # link to pdf (optional)
 pdf: https://arxiv.org/pdf/2510.01144
 # abstract
-abstract: "This work studies resilient leader-follower consensus with a bounded number of adversaries. Existing approaches typically require robustness conditions of the entire network to guarantee resilient consensus. However, the behavior of such systems when these conditions are not fully met remains unexplored. To address this gap, we introduce the notion of partial leader-follower consensus, in which a subset of non-adversarial followers successfully tracks the leader’s reference state despite insufficient robustness. We propose a novel distributed algorithm - the Bootstrap Percolation and Mean Subsequence Reduced (BP-MSR) algorithm --- and establish sufficient conditions for individual followers to achieve consensus via the BP-MSR algorithm in arbitrary time-varying graphs. We validate our findings through simulations, demonstrating that our method guarantees partial leader-follower consensus, even when standard resilient consensus algorithms fail.
+abstract: "This work studies resilient leader-follower consensus with a bounded number of adversaries. Existing approaches typically require robustness conditions of the entire network to guarantee resilient consensus. However, the behavior of such systems when these conditions are not fully met remains unexplored. To address this gap, we introduce the notion of partial leader-follower consensus, in which a subset of non-adversarial followers successfully tracks the leader’s reference state despite insufficient robustness. We propose a novel distributed algorithm - the Bootstrap Percolation and Mean Subsequence Reduced (BP-MSR) algorithm --- and establish sufficient conditions for individual followers to achieve consensus via the BP-MSR algorithm in arbitrary time-varying graphs. We validate our findings through simulations, demonstrating that our method guarantees partial leader-follower consensus, even when standard resilient consensus algorithms fail."
 # bib entry (optional). the |- is used to allow for multiline entry."
 bib:
 ---

content/papers/2026/2026-reroot.md

@@ -0,0 +1,30 @@
+---
+layout: papers
+# specify the title of the paper
+title:  "Autonomy Architectures for Safe Planning in Unknown Environments Under Budget Constraints"
+# specify the date it was published
+date: 2026-05-26
+# list the authors. if a "/people/id" page exists for the person, it will be linked. If not, the author's name is printed exactly as you typed it.
+authors:
+    - dmrc
+    - devanshagrawal
+    - dimitrapanagou
+# give the main figure location, relative to /static/
+image: /images/2026-reroot_field.gif
+# specify the conference or journal that it was published in
+venue: "IEEE ACC 2026"
+# link to publisher site (optional)
+link: 
+# link to arxiv (optional)
+arxiv: https://arxiv.org/abs/2504.03001
+# link to github (optional)
+code: https://github.com/dcherenson/budget-constrained-planning
+# link to video (optional)
+video: 
+# link to pdf (optional)
+pdf: https://arxiv.org/pdf/2504.03001
+# abstract
+abstract: "Mission planning can often be formulated as a constrained control problem under multiple path constraints (i.e., safety constraints) and budget constraints (i.e., resource expenditure constraints). In a priori unknown environments, verifying that an offline solution will satisfy the constraints for all time can be difficult, if not impossible. We present ReRoot, a novel sampling-based framework that enforces safety and budget constraints for nonlinear systems in unknown environments. The main idea is that ReRoot grows multiple reverse RRT* trees online, starting from renewal sets, i.e., sets where the budget constraints are renewed. The dynamically feasible backup trajectories guarantee safety and reduce resource expenditure, which provides a principled backup policy when integrated into the gatekeeper safety verification architecture. We demonstrate our approach in simulation with a fixed-wing UAV in a GNSS-denied environment with a budget constraint on localization error that can be renewed at visual landmarks."
+# bib entry (optional). the |- is used to allow for multiline entry."
+bib:
+---

content/papers/2026/2026-vtol_adaptive_control.md

@@ -0,0 +1,29 @@
+---
+layout: papers
+# specify the title of the paper
+title:  "Adaptive Control Allocation for Underactuated Time-Scale Separated Non-Affine Systems"
+# specify the date it was published
+date: 2026-05-26
+# list the authors. if a "/people/id" page exists for the person, it will be linked. If not, the author's name is printed exactly as you typed it.
+authors:
+    - dmrc
+    - dimitrapanagou
+# give the main figure location, relative to /static/
+image: /images/2026-vtol_adaptive_control.gif
+# specify the conference or journal that it was published in
+venue: "IEEE ACC 2026"
+# link to publisher site (optional)
+link: 
+# link to arxiv (optional)
+arxiv: https://arxiv.org/abs/2510.07507
+# link to github (optional)
+code: https://github.com/dcherenson/adaptive-control-underactuated
+# link to video (optional)
+video: 
+# link to pdf (optional)
+pdf: https://arxiv.org/pdf/2510.07507
+# abstract
+abstract: "Many robotic systems are underactuated, meaning not all degrees of freedom can be directly controlled due to lack of actuators, input constraints, or state-dependent actuation. This property, compounded by modeling uncertainties and disturbances, complicates the control design process for trajectory tracking. In this work, we propose an adaptive control architecture for uncertain, nonlinear, underactuated systems with input constraints. Leveraging time-scale separation, we construct a reduced-order model where fast dynamics provide virtual inputs to the slower subsystem and use dynamic control allocation to select the optimal control inputs given the non-affine dynamics. To handle uncertainty, we introduce a state predictor-based adaptive law, and through singular perturbation theory and Lyapunov analysis, we prove stability and bounded tracking of reference trajectories. The proposed method is validated on a VTOL quadplane with nonlinear, state-dependent actuation, demonstrating its utility as a unified controller across various flight regimes, including cruise, landing transition, and hover."
+# bib entry (optional). the |- is used to allow for multiline entry."
+bib:
+---

content/papers/2026/2026_dual_gtk_tubes_acc.md

@@ -3,7 +3,7 @@ layout: papers
 # specify the title of the paper
 title:  "A Formal gatekeeper Framework for Safe Dual Control with Active Exploration"
 # specify the date it was published
-date: 2026-01-21
+date: 2026-05-26
 # list the authors. if a "/people/id" page exists for the person, it will be linked. If not, the author's name is printed exactly as you typed it.
 authors:
     - kalebbennaveed
@@ -24,7 +24,7 @@ video:
 # link to pdf (optional)
 pdf: https://arxiv.org/2510.06351
 # abstract
-abstract: "Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, yet ignores uncertainty reduction and leads to overly conservative behavior. Actively reducing uncertainty on-the-fly during a nominal mission defines the dual control problem. Most approaches address this by adding a weighted exploration term to the cost, tuned to trade off the nominal objective and uncertainty reduction, but without formal consideration of when exploration is beneficial. Moreover, safety is enforced in some methods but not in others. We propose a framework that integrates robust planning with active exploration under formal guarantees as follows: The key innovation and contribution is that exploration is pursued only when it provides a verifiable improvement without compromising safety. To achieve this, we utilize our earlier work on gatekeeper as an architecture for safety verification, and extend it so that it generates both safe and informative trajectories that reduce uncertainty and the cost of the mission, or keep it within a user-defined budget. The methodology is evaluated via simulation case studies on the online dual control of a quadrotor under parametric uncertainty.
+abstract: "Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, yet ignores uncertainty reduction and leads to overly conservative behavior. Actively reducing uncertainty on-the-fly during a nominal mission defines the dual control problem. Most approaches address this by adding a weighted exploration term to the cost, tuned to trade off the nominal objective and uncertainty reduction, but without formal consideration of when exploration is beneficial. Moreover, safety is enforced in some methods but not in others. We propose a framework that integrates robust planning with active exploration under formal guarantees as follows: The key innovation and contribution is that exploration is pursued only when it provides a verifiable improvement without compromising safety. To achieve this, we utilize our earlier work on gatekeeper as an architecture for safety verification, and extend it so that it generates both safe and informative trajectories that reduce uncertainty and the cost of the mission, or keep it within a user-defined budget. The methodology is evaluated via simulation case studies on the online dual control of a quadrotor under parametric uncertainty."
 # bib entry (optional). the |- is used to allow for multiline entry."
 bib:
 ---

content/papers/2026/2026_non_uniform_exp_acc.md

@@ -3,7 +3,7 @@ layout: papers
 # specify the title of the paper
 title:  "Multi-Robot Allocation for Information Gathering in Non-Uniform Spatiotemporal Environments"
 # specify the date it was published
-date: 2026-01-21
+date: 2026-05-26
 # list the authors. if a "/people/id" page exists for the person, it will be linked. If not, the author's name is printed exactly as you typed it.
 authors:
     - kalebbennaveed

content/papers/2026/2026_stein_clarity_L4DC.md

@@ -3,7 +3,7 @@ layout: papers
 # specify the title of the paper
 title:  "Provably Safe Stein Variational Clarity-Aware Informative Planning"
 # specify the date it was published
-date: 2026-01-22
+date: 2026-06-17
 # list the authors. if a "/people/id" page exists for the person, it will be linked. If not, the author's name is printed exactly as you typed it.
 authors:
     - kalebbennaveed
@@ -25,7 +25,7 @@ video:
 # link to pdf (optional)
 pdf: https://arxiv.org/2511.09836
 # abstract
-abstract: "Autonomous robots are increasingly deployed for information-gathering tasks in environments that vary across space and time. Planning informative and safe trajectories in such settings is challenging because information decays when regions are not revisited. Most existing planners model information as static or uniformly decaying, ignoring environments where the decay rate varies spatially; those that model non-uniform decay often overlook how it evolves along the robot's motion, and almost all treat safety as a soft penalty. In this paper, we address these challenges. We model uncertainty in the environment using clarity, a normalized representation of differential entropy from our earlier work that captures how information improves through new measurements and decays over time when regions are not revisited. Building on this, we present Stein Variational Clarity-Aware Informative Planning, a framework that embeds clarity dynamics within trajectory optimization and enforces safety through a low-level filtering mechanism based on our earlier gatekeeper framework for safety verification. The planner performs Bayesian inference-based learning via Stein variational inference, refining a distribution over informative trajectories while filtering each nominal Stein informative trajectory to ensure safety. Hardware experiments and simulations across environments with varying decay rates and obstacles demonstrate consistent safety and reduced information deficits.
+abstract: "Autonomous robots are increasingly deployed for information-gathering tasks in environments that vary across space and time. Planning informative and safe trajectories in such settings is challenging because information decays when regions are not revisited. Most existing planners model information as static or uniformly decaying, ignoring environments where the decay rate varies spatially; those that model non-uniform decay often overlook how it evolves along the robot's motion, and almost all treat safety as a soft penalty. In this paper, we address these challenges. We model uncertainty in the environment using clarity, a normalized representation of differential entropy from our earlier work that captures how information improves through new measurements and decays over time when regions are not revisited. Building on this, we present Stein Variational Clarity-Aware Informative Planning, a framework that embeds clarity dynamics within trajectory optimization and enforces safety through a low-level filtering mechanism based on our earlier gatekeeper framework for safety verification. The planner performs Bayesian inference-based learning via Stein variational inference, refining a distribution over informative trajectories while filtering each nominal Stein informative trajectory to ensure safety. Hardware experiments and simulations across environments with varying decay rates and obstacles demonstrate consistent safety and reduced information deficits."
 # bib entry (optional). the |- is used to allow for multiline entry."
 bib:
 ---