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1. Robust Trajectory Execution for Multi-robot Teams Using Distributed Real-time Replanning

   https://doi.org/10.1007/978-3-030-05816-6_12  

   Şenbaşlar, Baskın; Hönig, Wolfgang; Ayanian, Nora (October 2018, Distributed Autonomous Robotic Systems)

   Robust trajectory execution is an extension of cooperative collision avoidance that takes pre-planned trajectories directly into account. We propose an algorithm for robust trajectory execution that compensates for a variety of dynamic changes, including newly appearing obstacles, robots breaking down, imperfect motion execution, and external disturbances. Robots do not communicate with each other and only sense other robots’ positions and the obstacles around them. At the high-level we use a hybrid planning strategy employing both discrete planning and trajectory optimization with a dynamic receding horizon approach. The discrete planner helps to avoid local minima, adjusts the planning horizon, and provides good initial guesses for the optimization stage. Trajectory optimization uses a quadratic programming formulation, where all safety-critical parts are formulated as hard constraints. At the low-level, we use buffered Voronoi cells as a multi-robot collision avoidance strategy. Compared to ORCA, our approach supports higher-order dynamic limits and avoids deadlocks better. We demonstrate our approach in simulation and on physical robots, showing that it can operate in real time. 

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2. LEAP-O: Learning to Predict Dynamic Obstacles for Safe Trajectory Planning

   Nguyen, Binh; Nghiem, Truong X; Nguyen, Linh; La, Hung; Nguyen, Thang (May 2026, IEEE)

   Trajectory planning plays a crucial role in autonomous driving and navigation by enabling robots to generate safe paths while minimizing travel costs and avoiding collisions. This paper addresses the issue of predicting dynamic obstacles for safe trajectory planning when prior information is unavailable and detection range is limited. We propose a learning framework using Gaussian Processes (GP) for motion prediction and uncertainty estimation, further enhanced by Recurrent Neural Networks (RNN) for more accurate predictions. In addition, we develop a receding horizon planning method, formulated as a stochastic optimization problem, to ensure safe, collision-free paths with confidence probabilities. Together, these contributions provide a robust framework for adaptive and safe trajectory generation in dynamic environments. Simulations were performed to demonstrate the effectiveness of the proposed strategy, where our approach (combining GP and RNN) outperformed a baseline method that utilized only GP. 

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3. Safe Hierarchical Navigation in Crowded Dynamic Uncertain Environments

   https://doi.org/10.1109/CDC51059.2022.9992674  

   Chen, Hongyi; Feng, Shiyu; Zhao, Ye; Liu, Changliu; Vela, Patricio A. (December 2022, IEEE Conference on Decision and Control)

   This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis and trajectory optimization to achieve collision avoidance with respect to the predicted trajectories of dynamic agents within the sensing and planning horizon and with robustness to agent uncertainty. To address uncertainty over the planning horizon and real-time safety, a fast reactive safe set algorithm (SSA) is adopted, which monitors and modifies the unsafe control during trajectory tracking. Compared to other existing methods, our approach offers theoretical guarantees of safety and achieves collision-free navigation with higher probability in uncertain environments, as demonstrated in scenarios with 20 and 50 dynamic agents. 

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4. Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery

   https://doi.org/10.1109/ICRA46639.2022.9812068  

   Gu, Zhaoyuan; Boyd, Nathan; Zhao, Ye (May 2022, International Conference on Robotics and Automation)

   In this paper, we examine the problem of push recovery for bipedal robot locomotion and present a reactive decision-making and robust planning framework for locomotion resilient to external perturbations. Rejecting perturbations is an essential capability of bipedal robots and has been widely studied in the locomotion literature. However, adversarial disturbances and aggressive turning can lead to negative lateral step width (i.e., crossed-leg scenarios) with unstable motions and self-collision risks. These motion planning problems are computationally difficult and have not been explored under a hierarchically integrated task and motion planning method. We explore a planning and decision-making framework that closely ties linear-temporal-logic-based reactive synthesis with trajectory optimization incorporating the robot’s full-body dynamics, kinematics, and leg collision avoidance constraints. Between the high-level discrete symbolic decision-making and the low-level continuous motion planning, behavior trees serve as a reactive interface to handle perturbations occurring at any time of the locomotion process. Our experimental results show the efficacy of our method in generating resilient recovery behaviors in response to diverse perturbations from any direction with bounded magnitudes. 

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5. RLSS: real-time, decentralized, cooperative, networkless multi-robot trajectory planning using linear spatial separations

   https://doi.org/10.1007/s10514-023-10104-w  

   Şenbaşlar, Baskın; Hönig, Wolfgang; Ayanian, Nora (May 2023, Autonomous Robots)

   Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots’ dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm’s performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments. 

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