Lattice-based motion planning is a hybrid planning method where a plan made up of discrete actions simultaneously is a physically feasible trajectory. The planning takes both discrete and continuous aspects into account, for example action pre-conditions and collision-free action-duration in the configuration space. Safe motion planing rely on well-calibrated safety-margins for collision checking. The trajectory tracking controller must further be able to reliably execute the motions within this safety margin for the execution to be safe. In this work we are concerned with introspective learning and reasoning about controller performance over time. Normal controller execution of the different actions is learned using reliable and uncertainty-aware machine learning techniques. By correcting for execution bias we manage to substantially reduce the safety margin of motion actions. Reasoning takes place to both verify that the learned models stays safe and to improve collision checking effectiveness in the motion planner by the use of more accurate execution predictions with a smaller safety margin. The presented approach allows for explicit awareness of controller performance under normal circumstances, and timely detection of incorrect performance in abnormal circumstances. Evaluation is made on the nonlinear dynamics of a quadcopter in 3D using simulation. Video: https://youtu.be/STmZduvSUMM
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Alliance Innovation Lab Silicon Valley（硅谷联盟创新实验室）
Robots deployed in the real world over extended periods of time need to reason about unexpected failures, learn to predict them, and to proactively take actions to avoid future failures. Existing approaches for competence-aware planning are either model-based, requiring explicit enumeration of known failure modes, or purely statistical, using state- and location-specific failure statistics to infer competence. We instead propose a structured model-free approach to competence-aware planning by reasoning about plan execution failures due to errors in perception, without requiring a priori enumer…
Enhancing Social Robot Navigation with Integrated Motion Prediction and Trajectory Planning in Dynamic Human Environments
School of Information Science, Japan Advanced Institute of Science and Technology（日本先进科学技术学院信息科学学院）；University of Engineering and Technology, Vietnam National University Hanoi（越南国家大学河内工程技术大学）
Gaits engineered for snake-like robots to rotate in-place instrumentally fill a gap in the set of locomotive gaits that have traditionally prioritized translation. This paper designs a Turn-in-Place gait and demonstrates the ability of a shape-centric modeling framework to capture the gait's locomotive properties. Shape modeling for turning involves a time-varying continuous body curve described by a standing wave. Presumed viscous robot-ground frictional interactions lead to body dynamics conditioned on the time-varying shape model. The dynamic equations describing the Turn-in-Place gait are …
CSDO: Enhancing Efficiency and Success in Large-Scale Multi-Vehicle Trajectory Planning
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 mobility学院）；Department of Civil and Environmental Engineering, University of Michigan（密歇根大学土木与环境工程系）
Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（卢蒙巴大学））；Federal Research Center ``Computer Science and Control'' of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）；National Research University Higher School of Economics（国立研究型大学高等经济学院）
We introduce and empirically evaluate two techniques aimed at enhancing the performance of multi-robot prioritized path planning. The first technique is the deterministic procedure for re-scheduling (as opposed to well-known approach based on random restarts), the second one is the heuristic procedure that modifies the search-space of the individual planner involved in the prioritized path finding.
Massachusetts Institute of Technology（麻省理工学院）；CTU in Prague（布拉格捷克理工大学）
Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is revie…
Prediction-Driven Motion Planning: Route Integration Strategies in Attention-Based Prediction Models
Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT)（测量与控制系统研究所，卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（信息技术研究所以）
DK-RRT: Deep Koopman RRT for Collision-Aware Motion Planning of Space Manipulators in Dynamic Debris Environments
University of California, Irvine（加州大学尔湾分校）；Illinois Institute of Technology（伊利诺伊理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Davis（加州大学戴维斯分校）；University of California, Berkeley（加州大学伯克利分校）
Framework for Robust Motion Planning of Tethered Multi-Robot Systems in Marine Environments
School of Engineering & Physical Sciences, Heriot-Watt University（工程与物理科学学院，赫瑞斯泰特大学）；School of Mathematics & Computer Sciences, Heriot-Watt University（数学与计算机科学学院，赫瑞斯泰特大学）
Massachusetts Institute of Technology（麻省理工学院）；Instituto Superior Técnico（高等技术学院）
Robotic systems often operate with uncertainties in their dynamics, for example, unknown inertial properties. Broadly, there are two approaches for controlling uncertain systems: design robust controllers in spite of uncertainty, or characterize a system before attempting to control it. This paper proposes a middle-ground approach, making trajectory progress while also accounting for gaining information about the system. More specifically, it combines excitation trajectories which are usually intended to optimize information gain for an estimator, with goal-driven trajectory optimization metri…
Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC) – Barcelona Tech（加泰罗尼亚理工大学巴塞罗那科技学院工业与控制工程研究所）
Physics-based motion planning is a challenging task, since it requires the computation of the robot motions while allowing possible interactions with (some of) the obstacles in the environment. Kinodynamic motion planners equipped with a dynamic engine acting as state propagator are usually used for that purpose. The difficulties arise in the setting of the adequate forces for the interactions and because these interactions may change the pose of the manipulatable obstacles, thus either facilitating or preventing the finding of a solution path. The use of knowledge can alleviate the stated dif…
IntelliMove: Enhancing Robotic Planning with Semantic Mapping
S.A.S. euroDAO（S.A.S. 欧元道）；LIRMM（蒙彼利埃计算机科学、机器人学、微电子与微电子实验室）；CNRS（法国国家科学研究中心）；Université de Montpellier（蒙彼利埃大学）；Université de Picardie Jules Verne（儒勒·凡尔纳皮卡第大学）
Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；NVIDIA Research, NVIDIA Corporation（英伟达研究院，英伟达公司）；Department of Aeronautics and Astronautics, Stanford University（斯坦福大学航空航天系）
IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning
The Hong Kong University of Science and Technology（香港科技大学）；HKUST (GZ)（香港科技大学（广州））；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）；Unity Drive Innovation Technology Co. Ltd.（元戎启行创新科技有限公司）
Enhancing Security in Multi-Robot Systems through Co-Observation Planning, Reachability Analysis, and Network Flow
Department of Systems Engineering, Boston University（系统工程系，波士顿大学）；Faculty of Mechanical Engineering and Systems Engineering, Boston University（机械工程与系统工程学院，波士顿大学）
Enhancing UAV Search under Occlusion using Next Best View Planning
Swarm Exploration Group, Department of Communication Systems, Institute for Communications and Navigation, German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR)（德国航空航天中心（德意志航天中心））
