Collision avoidance in unknown obstacle-cluttered environments may not always be feasible. This paper focuses on an emerging paradigm shift in which potential collisions with the environment can be harnessed instead of being avoided altogether. To this end, we introduce a new sampling-based online planning algorithm that can explicitly handle the risk of colliding with the environment and can switch between collision avoidance and collision exploitation. Central to the planner's capabilities is a novel joint optimization function that evaluates the effect of possible collisions using a reflection model. This way, the planner can make deliberate decisions to collide with the environment if such collision is expected to help the robot make progress toward its goal. To make the algorithm online, we present a state expansion pruning technique that significantly reduces the search space while ensuring completeness. The proposed algorithm is evaluated experimentally with a built-in-house holonomic wheeled robot that can withstand collisions. We perform an extensive parametric study to investigate trade-offs between (user-tuned) levels of risk, deliberate collision decision making, and trajectory statistics such as time to reach the goal and path length.
Clutter Resilient Occlusion Avoidance for Tightly-Coupled Motion-Assisted Detection
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Macau（澳门大学）；Weizmann Institute of Science（魏茨曼科学研究所）；Southern University of Science Technology（南方科技大学）
DreamWaQ++: Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning
Urban Robotics Lab., School of Electrical Engineering, KAIST（韩国科学技术院电子工程系城市机器人实验室）；KRAFTON（KRAFTON公司）；URobotics（URobotics公司）；Laboratory for Information and Decision Systems (LIDS), MIT（麻省理工学院信息与决策系统实验室）
Digital Twin Driven Textile Classification and Foreign Object Recognition in Automated Sorting Systems
Institute of Smart Systems Technologies（智能系统技术研究所）；University of Klagenfurt（克雷格弗特大学）；AAU SAL USE Laboratory（AAU SAL USE实验室）；Silicon Austria Labs（硅 Austria 实验室）
Deep Learning-based Robust Autonomous Navigation of Aerial Robots in Dense Forests
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理空间研究 institute FGI，芬兰国家土地测绘局）
Motion Primitives-based Navigation Planning using Deep Collision Prediction
Norwegian University of Science and Technology（挪威科技大学）
本文提出了一种利用基于学习的碰撞预测网络设计新型导航规划器的方法。神经网络的任务是预测碰撞成本的每个动作序列在一个预定义的运动基元库中的机器人的速度转向角空间，给定的只有当前的深度图像和估计的线速度和角速度的机器人。此外，我们利用Unscented变换来解释机器人部分状态的不确定性，并利用Monte Carlo dropout来解释神经网络模型的不确定性。然后将不确定性感知碰撞成本与全局规划器给出的目标方向相结合，以确定以滚动时域方式执行的最佳动作序列。为了证明该方法，我们开发了一个弹性的小型飞行机器人集成轻量级的传感和计算资源。一组仿真和实验研究，包括现场部署，在混乱和感知挑战性的环境进行评估的预测网络的质量和性能的建议规划。
Towards Obstacle-Avoiding Control of Planar Snake Robots Exploring Neuro-Evolution of Augmenting Topologies
Department of Computer Science and Information Science, BITS Pilani Hyderabad Campus（计算机科学与信息科学系，比斯潘学院海得拉巴校区）；Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus（电气与电子工程系，比斯潘学院海得拉巴校区）
Language-Guided Grasping under Partial Observation for Mobile Manipulation in Field Inspection and Maintenance
Department of Mechanical Engineering, Federal University of Uberlândia（联邦大学伯南迪利亚机械工程系）；Department of Mechanical Engineering, University of São Paulo（圣保罗大学机械工程系）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）；Faculdade Israelita de Ensino e Pesquisa Albert Einstein, Hospital Israelita Albert Einstein（艾伯特·爱因斯坦以色列教学与研究学院，艾伯特·爱因斯坦医院）
Robust control for multi-legged elongate robots in noisy environments
School of Physics, Georgia Institute of Technology（物理系，佐治亚理工学院）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（机器人与智能机器研究所，佐治亚理工学院）；School of Mathematics, Georgia Institute of Technology（数学系，佐治亚理工学院）；Department of Mechanical Engineering, Georgia Institute of Technology（机械工程系，佐治亚理工学院）
