Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach
Institute of Flight Systems, German Aerospace Center (DLR)（德国航空航天中心飞行系统研究所）；Institute for Production Technology and Systems, Leuphana University of Lueneburg（吕讷堡勒法纳大学生产技术与系统研究所）
Philipp Schitz, Johann C. Dauer, Paolo Mercorelli
备注：
Submitted to L-CSS with CDC option
发表信息：
IEEE Control Systems Letters, vol. 8, pp. 1907-1912, 2024
Implementation of Model Predictive Control (MPC) on hardware with limited computational resources remains a challenge. Especially for long-distance maneuvers that require small sampling times, the necessary horizon lengths prevent its application on onboard computers. In this paper, we propose a computationally efficient tubebased shrinking horizon MPC that is scalable to long prediction horizons. Using move blocking, we ensure that a given number of decision inputs is efficiently used throughout the maneuver. Next, a method to substantially reduce the number of constraints is introduced. The approach is demonstrated with a helicopter landing on an inclined platform using a prediction horizon of 300 steps. The constraint reduction decreases the computation time by an order of magnitude with a slight increase in trajectory cost.
Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（电气工程与计算机科学系，麻省理工学院）；Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（航空宇航系，麻省理工学院）
Hierarchical Planning of Dynamic Movements without Scheduled Contact Sequences
Department of Advanced Robotics, Istituto Italiano di Tecnologia（意大利技术研究院先进机器人部）；Robot Learning and Interaction Group, Idiap Research Institute（伊迪普研究所机器人学习与交互小组）
fortiss GmbH（福蒂斯有限公司）；Technische Universität München（慕尼黑工业大学）
Motion planning for urban environments with numerous moving agents can be viewed as a combinatorial problem. With passing an obstacle before, after, right or left, there are multiple options an autonomous vehicle could choose to execute. These combinatorial aspects need to be taken into account in the planning framework. We address this problem by proposing a novel planning approach that combines trajectory planning and maneuver reasoning. We define a classification for dynamic obstacles along a reference curve that allows us to extract tactical decision sequences. We separate longitudinal and…
Bipedal robots have received much attention because of the variety of motion maneuvers that they can produce, and the many applications they have in various areas including rehabilitation. One of these motion maneuvers is walking. In this study, we presented a framework for the trajectory optimization of a 5-link (planar) Biped Robot using hybrid optimization. The walking is modeled with two phases of single-stance (support) phase and the collision phase. The dynamic equations of the robot in each phase are extracted by the Lagrange method. It is assumed that the robot heel strike to the groun…
In this article, we present a control strategy for the problem of safe autonomous docking for a planar floating platform (Slider) that emulates the movement of a satellite. Employing the proposed strategy, Slider approaches a docking port with the right orientation, maintaining a safe distance, while always keeping a visual lock on the docking port throughout the docking maneuver. Control barrier functions are designed to impose the safety, direction of approach and visual locking constraints. Three control inputs of the Slider are shared among three barrier functions in enforcing the constrai…
When mobile robots maneuver near people, they run the risk of rudely blocking their paths; but not all people behave the same around robots. People that have not noticed the robot are the most difficult to predict. This paper investigates how mobile robots can generate acceptable paths in dynamic environments by predicting human behavior. Here, human behavior may include both physical and mental behavior, we focus on the latter. We introduce a simple safe interaction model: when a human seems unaware of the robot, it should avoid going too close. In this study, people around robots are detecte…
University of Bonn（波恩大学）；Aptiv Services Deutschland GmbH（安波福服务德国有限公司）
The ability to predict the future movements of other vehicles is a subconscious and effortless skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for autonomous cars has gained a lot of attention in recent years. It is, however, still a hard task to achieve human-level performance. Interdependencies between vehicle behaviors and the multimodal nature of future intentions in a dynamic and complex driving environment render trajectory prediction a challenging problem. In this work, we propose a new, data-driven approach for predicting the motion of vehicles in …
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Utah（犹他大学）
The sequence in which a complex product is assembled directly impacts the ease and efficiency of the assembly process, whether executed by a human or a robot. A sequence that gives the assembler the greatest freedom of movement is therefore desirable. Our main contribution is an expression of obstruction relationships between parts as a disassembly interference graph (DIG). We validate this heuristic by developing a disassembly sequence planner that partitions assemblies in a way that prioritizes access to parts, resulting in plans that are comparable in efficiency to two state-of-the-art asse…
Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles
Cognitive Modeling Center（认知建模中心）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Federal Research Center “Computer Science and Control”（计算机科学与控制联邦研究中心）；Russian Academy of Sciences（俄罗斯科学院）；AIRI
An important topic in the autonomous driving research is the development of maneuver planning systems. Vehicles have to interact and negotiate with each other so that optimal choices, in terms of time and safety, are taken. For this purpose, we present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver. Our model is trained with a novel implementation of A3C, which we will call Delayed A3C (D-A3C), in a synthetic envi…
