The field of autonomous systems is witnessing significant developments in motion planning and control, with a focus on improving efficiency, adaptability, and safety. Researchers are exploring innovative approaches to address the challenges of navigating complex environments, handling nonholonomic constraints, and ensuring robustness in dynamic scenarios. Noteworthy papers in this area include the proposal of a curvature-constrained vector field for motion planning of nonholonomic robots, which demonstrates improved performance in simulations and real-world experiments. Another notable work presents a dynamic objective MPC for motion planning of seamless docking maneuvers, showcasing a unified approach that combines the advantages of model predictive contouring control and Cartesian MPC. These advancements have the potential to impact various applications, including autonomous vehicles, robotics, and logistics. Highlighted papers include: The Curvature-Constrained Vector Field paper, which presents a novel framework for co-developing vector fields and control laws for nonholonomic robots. The Dynamic Objective MPC paper, which proposes a unified approach for motion planning of seamless docking maneuvers using model predictive control.
Advancements in Motion Planning and Control for Autonomous Systems
Sources
Curvature-Constrained Vector Field for Motion Planning of Nonholonomic Robots
Dynamic Objective MPC for Motion Planning of Seamless Docking Maneuvers
Gradient Field-Based Dynamic Window Approach for Collision Avoidance in Complex Environments
CSF: Fixed-outline Floorplanning Based on the Conjugate Subgradient Algorithm Assisted by Q-Learning
Energy Efficient Planning for Repetitive Heterogeneous Tasks in Precision Agriculture
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference
Segmented Trajectory Optimization for Autonomous Parking in Unstructured Environments
Optimized Path Planning for Logistics Robots Using Ant Colony Algorithm under Multiple Constraints
Real-Time Model Predictive Control for the Swing-Up Problem of an Underactuated Double Pendulum
Lazy-DaSH: Lazy Approach for Hypergraph-based Multi-robot Task and Motion Planning
Exploring Adversarial Obstacle Attacks in Search-based Path Planning for Autonomous Mobile Robots
A Corrector-aided Look-ahead Distance-based Guidance for Reference Path Following with an Efficient Midcourse Guidance Strategy
Real-Time LaCAM
Conformal Slit Mapping Based Spiral Tool Trajectory Planning for Ball-end Milling on Complex Freeform Surfaces
Overcoming Dynamic Environments: A Hybrid Approach to Motion Planning for Manipulators
Learning global control of underactuated systems with Model-Based Reinforcement Learning
Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning