Capozzi, 2001 - Google Patents
Evolution-based path planning and management for autonomous vehiclesCapozzi, 2001
View PDF- Document ID
- 1289360404073886728
- Author
- Capozzi B
- Publication year
External Links
Snippet
This dissertation describes an approach to adaptive path planning based on the problem solving capabilities witnessed in nature—namely the influence of natural selection in uncovering solutions to the characteristics of the environment. The competition for survival …
- 238000000034 method 0 abstract description 124
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
- G05D1/0287—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
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- 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
- G05D1/0268—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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Capozzi | Evolution-based path planning and management for autonomous vehicles | |
Ammar et al. | Hybrid metaheuristic approach for robot path planning in dynamic environment | |
Bruce | Real-time motion planning and safe navigation in dynamic multi-robot environments | |
Bouraine et al. | Safe motion planning based on a new encoding technique for tree expansion using particle swarm optimization | |
U Lima et al. | Multi-robot systems | |
Choi et al. | Imitation learning-based unmanned aerial vehicle planning for multitarget reconnaissance under uncertainty | |
Theile et al. | Learning to recharge: UAV coverage path planning through deep reinforcement learning | |
Patnaik et al. | Innovations in robot mobility and control | |
Scherer et al. | Multiple-objective motion planning for unmanned aerial vehicles | |
Slear | AFIT UAV swarm mission planning and simulation system | |
Verma et al. | Computational investigation of environment learning in guidance and navigation | |
Zafar et al. | Optimization of route planning and exploration using multi agent system | |
de Carvalho | Deep reinforcement learning methods for cooperative robotic navigation | |
Veres et al. | Control engineering of autonomous cognitive vehicles-a practical tutorial | |
Ma | Model-based reinforcement learning for cooperative multi-agent planning: exploiting hierarchies, bias, and temporal sampling | |
Lotspeich | Distributed control of a swarm of autonomous unmanned aerial vehicles | |
Lucas et al. | Constrained navigation with mandatory waypoints in uncertain environment | |
Feit et al. | Subgoal planning algorithm for autonomous vehicle guidance | |
Bailey | Design of Environment Aware Planning Heuristics for Complex Navigation Objectives | |
Pyykkönen | Evaluation of swarm path-planning methods for autonomous area patrolling | |
Luo | Efficient deep reinforcement learning in robotic motion planning | |
Nguyen | Apprenticeship Bootstrapping for Autonomous Aerial Shepherding of Ground Swarm | |
Ghelis | Towards an efficient Multi-Robots Search and Rescue strategy | |
Pathmanathan et al. | Reinforcement Learning Based Autonomous Quadcopter Control | |
Milam | Evolution of control programs for a swarm of autonomous unmanned aerial vehicles |