Papers Optimal Motion Planning
“Optimal Motion Planning” 태그가 달린 논문 11편 · 필터 해제
Mixed Bernstein-Fourier Approximants for Optimal Trajectory Generation with Periodic Behavior
Efficient trajectory generation is critical for autonomous systems, yet current numerical methods often struggle to handle periodic behaviors effectively, especially when equidistant time nodes are required. This paper i…
Computational EfficiencyMotion PlanningOptimal Motion PlanningTrajectory PlanningDashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning
Recent innovations in autonomous drones have facilitated time-optimal flight in single-drone configurations, and enhanced maneuverability in multi-drone systems by applying optimal control and learning-based methods. How…
Collision AvoidanceMotion PlanningMulti-agent Reinforcement LearningOptimal Motion PlanningOptimal Motion Planning using Finite Fourier Series in a Learning-based Collision Field
This paper utilizes finite Fourier series to represent a time-continuous motion and proposes a novel planning method that adjusts the motion harmonics of each manipulator joint. Primarily, we sum the potential energy for…
Motion PlanningOptimal Motion PlanningqRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems
This paper presents a sampling-based method for optimal motion planning in non-holonomic systems in the absence of known cost functions. It uses the principle of learning through experience to deduce the cost-to-go of re…
Motion PlanningOptimal Motion Planningreinforcement-learningReinforcement Learning (RL)T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles
In this paper, we develop a new algorithm, called T$^{\star}$-Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T$^{\star}$-Lite algorithm is a significantly faster ver…
Autonomous VehiclesMotion PlanningOptimal Motion PlanningNeural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM). The NCM uses a deep long short-term memory recurrent neural netw…
Motion PlanningOptimal Motion PlanningState EstimationChance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is availabl…
Motion PlanningOptimal Motion PlanningSafe ExplorationCombining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL (subgoal graphs-reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By "rational"…
Motion PlanningOptimal Motion PlanningPathfinderreinforcement-learning+2Safe learning-based optimal motion planning for automated driving
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP…
BIG-bench Machine LearningMotion PlanningOptimal Motion PlanningA novel model-based heuristic for energy optimal motion planning for automated driving
Predictive motion planning is the key to achieve energy-efficient driving, which is one of the main benefits of automated driving. Researchers have been studying the planning of velocity trajectories, a simpler form of m…
Motion PlanningOptimal Motion PlanningIntelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments
The sampling based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT …
Computational EfficiencyMotion PlanningOptimal Motion Planning