The paper introduces OrbitalGym, a JAX‑based simulation environment for studying how planning algorithms and system design influence the outcomes of adversarial orbital encounters. It supports multiple agents, diverse state representations, communication modes, and sensor models, and compiles games to batched GPU code for large‑scale parallel simulation. Using OrbitalGym, the authors evaluate several control policies (PD, LQR, MPC, MCTS) across two close‑range games—pursuit‑evasion and lady‑bandit‑guard—showing that propellant availability often dominates in pursuit‑evasion, while policy choice significantly improves defense in the lady‑bandit‑guard scenario.
By Duncan Eddy, Mahdi Al-Husseini, Mykel J. Kochenderfer
arXiv:2610.16522v2 Announce Type: replace-cross
Abstract: Safe robot navigation in unknown, dynamic environments requires more than geometric collision checking: a robot still far from an obstacle ma...
By Bojan Deraji\'c
arXiv:2402.03741v4 Announce Type: replace-cross
Abstract: Recent advancements in multi-agent reinforcement learning (MARL) have opened up vast application prospects, such as swarm control of drones,...
By Oubo Ma, Yuwen Pu, Linkang Du, Yang Dai, Ruo Wang, Xiaolei Liu, Yingcai Wu, Shouling Ji
arXiv:2610.17419v1 Announce Type: cross
Abstract: Autonomous quadrotor flight in cluttered environment is an key capability for tasks like inspection and search. Navigation policies trained in simula...
By Aditya Azad, Arthur Huang, Guanrui Li
The paper introduces a conflict‑predictive variable horizon for distributed model predictive control in multi‑drone collision avoidance. Each drone locally estimates future conflicts using observed positions and confidence funnels, then selects the minimal horizon that covers the farthest predicted conflict, shrinking in clear air and expanding only when necessary. The authors prove that this adaptive horizon preserves recursive feasibility and asymptotic stability for linear models, and demonstrate in simulation that it reduces per‑step solver cost and total computation while maintaining separation on dense benchmarks.
By Linda M\"{u}mken, Michael Schwung, Stefan Lier, Andreas Schwung
The paper presents a robust multi‑agent reinforcement learning framework for small unmanned aircraft systems (sUAS) to maintain separation assurance when GPS data is degraded or spoofed. By modeling state observation corruption as a zero‑sum game, the authors derive a closed‑form adversarial perturbation that eliminates iterative inner optimization and can be evaluated in linear time. Integrating this perturbation into a policy‑gradient MARL algorithm yields a counter‑policy that achieves near‑zero collision rates in high‑density simulations even with up to 35% observation corruption, outperforming non‑adversarial baselines.
By Alex Zongo, Filippos Fotiadis, Ufuk Topcu, Peng Wei
The paper introduces LEAP-CBF, a safety filter that uses Least‑Effort Adversarial Potentials to quantify how much disturbance effort is needed to cause failure in nonlinear dynamical systems. LEAP serves as a control barrier function for the undisturbed system and can be combined with a robust safety filter that tolerates disturbances with bounded cumulative effort. The authors develop a deep reinforcement learning method to construct LEAPs and validate their effectiveness through simulations of multi‑agent systems and hardware experiments on a quadruped and quadrotors.
By Oswin So, Eric Yu, Chuchu Fan
KinoUAV introduces a kinodynamic benchmark for UAV vision-language navigation, featuring a dataset and an agent that generate trajectories under velocity, acceleration, and jerk constraints. The benchmark incorporates directional free-space distance labels to guide safe motion decisions and a Cruise-Seek task that eases target search. Experiments show the KinoUAV-Agent achieves higher success rates with lower yaw and collision rates than existing methods.
By Chenyao Yang, Peijin Wang, Yingchao Feng, Wenhui Diao
arXiv:2611.06802v1 Announce Type: cross
Abstract: Hamilton-Jacobi (HJ) Reachability identifies the unsafe states of a dynamical system from which it can be forced into failure under worst-case distur...
By Kendra Givens, Taylor T. Johnson, Bryan C. Ward, Thomas Beckers
arXiv:2607. 10014v1 Announce Type: cross Abstract: Learning-based separation assurance for small Unmanned Aircraft Systems (sUAS) achieves near-zero collision rates in simulation, but assumes accurate position and velocity information from Global Navigation Satellite Systems (GNSS).
By Alex Zongo, Peng Wei
arXiv:2608. 14135v1 Announce Type: cross Abstract: Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynamics and continuously changing opponent behaviors.
By Wenhao Tang, Tianyang Chen, Zhejun Cui, Boyuan An, Jiayu Chen, Ruize Zhang, Huidong Liu, Tianyue Wu, Qingmin Liao, Fei Gao, Yu Wang, Chao Yu
arXiv:2607. 19306v1 Announce Type: cross Abstract: Autonomous flight in cluttered environments requires a robot to build a geometric map of its surroundings and plan safe, dynamically feasible trajectories, all onboard and in real time.
By Jason Stanley (UC San Diego, La Jolla, USA), Zhirui Dai (UC San Diego, La Jolla, USA), Qihao Qian (UC San Diego, La Jolla, USA), Tzu-Chin Ho (UC San Diego, La Jolla, USA), Tianxing Fan (UC San Diego, La Jolla, USA), Siddharth Saha (Shield AI, San Diego, USA), Christopher Barngrover (Shield AI, San Diego, USA), Ki Myung Brian Lee (UC San Diego, La Jolla, USA), Nikolay Atanasov (UC San Diego, La Jolla, USA)