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1. Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning

   https://doi.org/10.1109/ICRA46639.2022.9812309  

   Leu, Jessica; Wang, Yebin; Tomizuka, Masayoshi; Di Cairano, Stefano (May 2022, 2022 International Conference on Robotics and Automation (ICRA))

   This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. The hybrid environment predictor performs short-term predictions via an extended Kalman filter and an adaptive observer. It also combines short-term predictions with a driver behavior cost-map to make long-term predictions. The strategic motion planner comprises 1) a model predictive control-based safety controller for trajectory tracking; 2) a search-based retreating planner for finding an evasion path in an emergency; 3) an optimization-based repairing planner for planning a new path when the original path is invalidated. Simulation validation demonstrates the effectiveness of the proposed method in terms of initial planning, motion prediction, safe tracking, retreating in an emergency, and trajectory repairing. 

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2. pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning

   Huang, Chih; Jadhav, Pranav; Plancher, Brian; Kingston, Zachary (June 2026, IEEE)

   Sampling-based motion planning algorithms, like the Rapidly-Exploring Random Tree (RRT) and its widely used variant, RRT-Connect, provide efficient solutions for high-dimensional planning problems faced by real-world robots. However, these methods remain computationally intensive, particularly in complex environments that require many collision checks. To improve performance, recent efforts have explored parallelizing specific components of RRT such as collision checking, or running multiple planners independently. However, little has been done to develop an integrated parallelism approach, co-designed for large-scale parallelism. In this work we present pRRTC, a RRT-Connect based planner co-designed for GPU acceleration across the entire algorithm through parallel expansion and SIMT-optimized collision checking. We evaluate the effectiveness of pRRTC on the MotionBenchMaker dataset using robots with 7, 8, and 14 degrees of freedom (DoF). Compared to the state-of-the-art, pRRTC achieves as much as a 10× speedup on constrained reaching tasks with a 5.4× reduction in standard deviation. pRRTC also achieves a 1.4× reduction in average initial path cost. Finally, we deploy pRRTC on a 14-DoF dual Franka Panda arm setup and demonstrate real-time, collision-free motion planning with dynamic obstacles. We open-source our planner to support the wider community. 

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3. iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization

   https://doi.org/10.1109/IROS58592.2024.10802168  

   Ortiz-Haro, Joaquim; Hönig, Wolfgang; Hartmann, Valentin N; Toussaint, Marc; Righetti, Ludovic (October 2024, IEEE)

   Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic constraints makes the motion planning problem significantly harder, as it requires solving two-value boundary problems (computationally expensive) or propagating random control inputs (uninformative). Alternatively, Iterative Discontinuity Bounded A* (iDb-A*), introduced in our previous study, combines search and optimization iteratively. The search step connects short trajectories (motion primitives) while allowing a bounded discontinuity between the motion primitives, which is later repaired in the trajectory optimization step.Building upon these foundations, in this paper, we present iDb-RRT, a sampling-based kinodynamic motion planning algorithm that combines motion primitives and trajectory optimization within the RRT framework. iDb-RRT is probabilistically complete and can be implemented in forward or bidirectional mode. We have tested our algorithm across a benchmark suite comprising 30 problems, spanning 8 different systems, and shown that iDb-RRT can find solutions up to 10x faster than previous methods, especially in complex scenarios that require long trajectories or involve navigating through narrow passages. 

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4. Safe Bipedal Path Planning via Control Barrier Functions for Polynomial Shape Obstacles Estimated Using Logistic Regression

   https://doi.org/10.1109/ICRA48891.2023.10160671  

   Peng, Chengyang; Donca, Octavian; Castillo, Guillermo; Hereid, Ayonga (May 2023, IEEE)

   Safe path planning is critical for bipedal robots to operate in safety-critical environments. Common path planning algorithms, such as RRT or RRT*, typically use geometric or kinematic collision check algorithms to ensure collision-free paths toward the target position. However, such approaches may generate non-smooth paths that do not comply with the dynamics constraints of walking robots. It has been shown that the control barrier function (CBF) can be integrated with RRT/RRT* to synthesize dynamically feasible collision-free paths. Yet, existing work has been limited to simple circular or elliptical shape obstacles due to the challenging nature of constructing appropriate barrier functions to represent irregularly shaped obstacles. In this paper, we present a CBF-based RRT* algorithm for bipedal robots to generate a collision-free path through space with multiple polynomial-shaped obstacles. In particular, we used logistic regression to construct polynomial barrier functions from a grid map of the environment to represent irregularly shaped obstacles. Moreover, we developed a multi-step CBF steering controller to ensure the efficiency of free space exploration. The proposed approach was first validated in simulation for a differential drive model, and then experimentally evaluated with a 3D humanoid robot, Digit, in a lab setting with randomly placed obstacles. 

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5. ARRT*: A Hybrid Heuristic RRT* Approach for Efficient Path Planning

   https://doi.org/10.1109/ITSC60802.2025.11423446  

   Tasneem, Sameeha; Stas, Mike; Wu, Guoyuan; Barth, Matthew (November 2025, IEEE)

   Path planning algorithms must balance computational efficiency and path optimality, particularly in real-time applications. Rapidly-exploring Random Tree (RRT) and its variants provide fast execution but often generate suboptimal paths, while grid-based algorithms like A* and hybrid A* yield optimal paths at a high computational cost. This paper presents a novel path-planning approach that integrates heuristic functions from A* and hybrid A* into the RRT* framework. By refining node cost evaluation, our method enhances both path quality and computational efficiency. Experimental evaluations and ablation studies demonstrate its generalizability across multiple RRT variants. Furthermore, we explore the impact of different sampling strategies in conjunction with our modified cost function to optimize key metrics such as path length, execution time, and smoothness. As a practical application, we implement this enhanced algorithm in an automated parking system, a key feature in Connected and Automated Vehicles (CAVs). Quantitative results show that ARRT* reduces path length by up to 17% compared to standard RRT*, while also improving computational efficiency and minimizing steering variations, making it more suitable for real-world autonomous navigation in constrained environments. 

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