Robot motion planning is a challenging domain as it involves dealing with high-dimensional and continuous search space. In past decades, a wide variety of planning algorithms have been developed to tackle this problem, sometimes in isolation without comparing to each other. In this study, we benchmark two such prominent types of algorithms: OMPL's sampling-based RRT-Connect and SMPL's search-based ARA* with motion primitives. To compare these two fundamentally different approaches fairly, we adapt them to ensure the same planning conditions and benchmark them on the same set of planning scenarios. Our findings suggest that sampling-based planners like RRT-Connect show more consistent performance across the board in high-dimensional spaces, whereas search-based planners like ARA* have the capacity to perform significantly better when used with a suitable action-space sampling scheme. Through this study, we hope to showcase the effort required to properly benchmark motion planners from different paradigms thereby contributing to a more nuanced understanding of their capabilities and limitations. The code is available at https://github.com/gsotirchos/benchmarking_planners
Sparse-Graph-Enabled Formation Planning for Large-Scale Aerial Swarms
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院工业控制国家重点实验室（ cyber-systems and control研究所））；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
Courant Institute of Mathematical Sciences（库朗数学科学研究所）；Tandon School of Engineering（坦登工程学院）；New York University（纽约大学）
Autonomous drone swarms are a burgeoning technology with significant applications in the field of mapping, inspection, transportation and monitoring. To complete a task, each drone has to accomplish a sub-goal within the context of the overall task at hand and navigate through the environment by avoiding collision with obstacles and with other agents in the environment. In this work, we choose the task of optimal coverage of an environment with drone swarms where the global knowledge of the goal states and its positions are known but not of the obstacles. The drones have to choose the Points o…
Optimal motion planning involves obstacles avoidance where path planning is the key to success in optimal motion planning. Due to the computational demands, most of the path planning algorithms can not be employed for real-time based applications. Model-based reinforcement learning approaches for path planning have received certain success in the recent past. Yet, most of such approaches do not have deterministic output due to the randomness. We analyzed several types of reinforcement learning-based approaches for path planning. One of them is a deterministic tree-based approach and other two …
A Comparative Study of Floating-Base Space Parameterizations for Agile Whole-Body Motion Planning
Laboratory of Automation and Robotics (LAR) in the Department of Electrical & Computer Engineering, University of Patras（帕尔蒂亚大学电气与计算机工程系自动化与机器人实验室）；Archimedes/Athena RC（阿基米德/雅典娜研究中心）；Athena - Research and Innovation Center in Information, Communication and Knowledge Technologies（雅典娜信息、通信和知识技术研究与创新中心）
A Comparative Study of Rapidly-exploring Random Tree Algorithms Applied to Ship Trajectory Planning and Behavior Generation
Autoship Centre for Research-based Innovation (SFI), Norwegian University of Science and Technology (NTNU)（Autoship 基于研究的创新中心（SFI）、挪威科技大学（NTNU））；Det Norske Veritas (DNV)（挪威船级社（DNV））
FZI Research Center for Information Technology（FZI信息技术研究中心）
We present a biologically inspired approach for path planning with dynamic obstacle avoidance. Path planning is performed in a condensed configuration space of a robot generated by self-organizing neural networks (SONN). The robot itself and static as well as dynamic obstacles are mapped from the Cartesian task space into the configuration space by precomputed kinematics. The condensed space represents a cognitive map of the environment, which is inspired by place cells and the concept of cognitive maps in mammalian brains. The generation of training data as well as the evaluation were perform…
This paper presents a deep-learning based CPP algorithm, called Coverage Path Planning Network (CPPNet). CPPNet is built using a convolutional neural network (CNN) whose input is a graph-based representation of the occupancy grid map while its output is an edge probability heat graph, where the value of each edge is the probability of belonging to the optimal TSP tour. Finally, a greedy search is used to select the final optimized tour. CPPNet is trained and comparatively evaluated against the TSP tour. It is shown that CPPNet provides near-optimal solutions while requiring significantly less …
Istituto Italiano di Tecnologia（意大利技术研究院）；University of Edinburgh（爱丁堡大学）；University of Oxford（牛津大学）
Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitud…
This paper proposes a novel online motion planning approach to robot navigation based on nonlinear model predictive control. Common approaches rely on pure Euclidean optimization parameters. In robot navigation, however, state spaces often include rotational components which span over non-Euclidean rotation groups. The proposed approach applies nonlinear increment and difference operators in the entire optimization scheme to explicitly consider these groups. Realizations include but are not limited to quadratic form and time-optimal objectives. A complex parking scenario for the kinematic bicy…
Flatness-based trajectory planning for 3D overhead cranes with friction compensation and collision avoidance
Departamento de Informatica e Ingenieria de Sistemas (DIIS)（信息与系统工程系（DIIS））；Instituto de Investigacion en Ingenieria de Aragon (I3A)（阿列尼亚工程研究所（I3A））；Universidad de Zaragoza（萨拉戈塔大学）
In many environmental monitoring scenarios, the sampling robot needs to simultaneously explore the environment and exploit features of interest with limited time. We present an anytime multi-objective informative planning method called Pareto Monte Carlo tree search which allows the robot to handle potentially competing objectives such as exploration versus exploitation. The method produces optimized decision solutions for the robot based on its knowledge (estimation) of the environment state, leading to better adaptation to environmental dynamics. We provide algorithmic analysis on the critic…
Furhat Robotics（Furhat机器人公司）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Gaze cues play an important role in human communication and are used to coordinate turn-taking and joint attention, as well as to regulate intimacy. In order to have fluent conversations with people, social robots need to exhibit human-like gaze behavior. Previous Gaze Control Systems (GCS) in HRI have automated robot gaze using data-driven or heuristic approaches. However, these systems tend to be mainly reactive in nature. Planning the robot gaze ahead of time could help in achieving more realistic gaze behavior and better eye-head coordination. In this paper, we propose and implement a nove…
