Desiderio, 2021 - Google Patents
Development of a modified A-Star Algorithm for path planningDesiderio, 2021
View PDF- Document ID
- 4411840685632632455
- Author
- Desiderio F
- Publication year
External Links
Snippet
Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and …
- 238000004422 calculation algorithm 0 title abstract description 179
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0225—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving docking at a fixed facility, e.g. base station or loading bay
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
- G05D1/0044—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement by providing the operator with a computer generated representation of the environment of the vehicle, e.g. virtual reality, maps
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D2201/00—Application
- G05D2201/02—Control of position of land vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sharma et al. | Recent advances in motion and behavior planning techniques for software architecture of autonomous vehicles: A state-of-the-art survey | |
Stahl et al. | Multilayer graph-based trajectory planning for race vehicles in dynamic scenarios | |
Zhang et al. | Hybrid trajectory planning for autonomous driving in highly constrained environments | |
Souissi et al. | Path planning: A 2013 survey | |
Zghair et al. | A one decade survey of autonomous mobile robot systems | |
Bautista-Montesano et al. | Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach | |
Reda et al. | Path planning algorithms in the autonomous driving system: A comprehensive review | |
Gu | Improved trajectory planning for on-road self-driving vehicles via combined graph search, optimization & topology analysis | |
Feng et al. | Decision-making and path planning for highway autonomous driving based on spatio-temporal lane-change gaps | |
Chen et al. | Path Planning for Autonomous Vehicle Based on a Two‐Layered Planning Model in Complex Environment | |
Spanogiannopoulos et al. | Sampling-based non-holonomic path generation for self-driving cars | |
Ziegler et al. | Anytime tree-based trajectory planning for urban driving | |
Patel et al. | Ref: A rapid exploration framework for deploying autonomous mavs in unknown environments | |
Huy et al. | A practical and optimal path planning for autonomous parking using fast marching algorithm and support vector machine | |
Elmi et al. | Autonomous vehicle path planning using mpc and apf | |
Chen et al. | From topological map to local cognitive map: a new opportunity of local path planning | |
Batkovic | Enabling Safe Autonomous Driving in Uncertain Environments | |
Desiderio | Development of a modified A-Star Algorithm for path planning | |
Smit et al. | Informed sampling-based trajectory planner for automated driving in dynamic urban environments | |
Ventura | Safe and flexible hybrid control architecture for the navigation in formation of a group of vehicles | |
Gautam et al. | An Overview of Motion-Planning Algorithms for Autonomous Ground Vehicles with Various Applications | |
Villagra et al. | Motion planning | |
Jin et al. | A practical sampling-based motion planning method for autonomous driving in unstructured environments | |
Qiu | A Review of Motion Planning for Urban Autonomous Driving | |
Moradi | A non-linear mpc local planner for tractor-trailer vehicles in forward and backward maneuvering |