Integrating vision-based motion planning with defeasible decision making for the Khepera robot

2008 ◽  
Author(s):  
Edgardo Ferretti ◽  
Roberto Kiessling ◽  
Alejandro Silnik ◽  
Ricardo Petrino ◽  
Marcelo Errecalde
Keyword(s):  
Decision Making ◽  
Motion Planning

Decision-Making Under Model Uncertainty of Damaged Aircraft Systems

2009 ◽  
Author(s):  
Israel Lopez ◽  
Nesrin Sarigul-Klijn
Keyword(s):  
Decision Making ◽  
Motion Planning ◽  
Trajectory Planning ◽  
Evidence Theory ◽  
Target Position ◽  
Landing Site ◽  
Assessment Process ◽  
Diagnosis And Prognosis ◽  
Human Decision ◽  
Aircraft Trajectory

When in-flight failures occur, rapid and precise decision-making under imprecise information is required in order to regain and maintain control of the aircraft. To achieve planned aircraft trajectory and complete landing safely, the uncertainties in vehicle parameters of the damaged aircraft need to be learned and incorporated at the level of motion planning. Uncertainty is a very important concern in recovery of damaged aircraft since it can cause false diagnosis and prognosis that may lead to further performance degradation and mission failure. The mathematical and statistical approaches to analyzing uncertainty are first presented. The damaged aircraft is simulated via a simplified kinematics model. The different sources and perspectives of uncertainties under a damage assessment process and post-failure trajectory planning are presented and classified. The decision-making process for an emergency motion planning to landing site is developed via the Dempster-Shafer evidence theory. The objective of the trajectory planning is to arrive at a target position while maximizing the safety of the aircraft under uncertain conditions. Simulations are presented for an emergency motion planning and landing that takes into account aircraft dynamics, path complexity, distance to landing site, runway characteristics, and subjective human decision.

scholarly journals A Survey of Cooperative Driving between Auxiliary Autonomous System and Human Driver

2018 ◽  
Vol 160 ◽  
pp. 05001
Author(s):  
Qijie Zou ◽  
Haoyu Li ◽  
Rubo Zhang ◽  
Tengda Pei
Keyword(s):  
Decision Making ◽  
Motion Planning ◽  
Autonomous System ◽  
Full Range ◽  
Intelligent Vehicle ◽  
Unexpected Events ◽  
Cooperative Driving ◽  
Current State ◽  
Human Driver ◽  
Computational Ability

The cooperative driving is a main direction of intelligent vehicle development since safety-critical tasks must be executed by human and autonomous system. An intelligent vehicle is equipped with a full range of sensors, which are larger and faster than the perception and computational ability of a human driver, while the latter has a comprehensive ability to adapt to unexpected events. According to their respective advantages, the cooperative driving between human and autonomous system can have new synergies. The emphasis of this paper is to survey the current state of the art of cooperative driving, with a specific focus on decision-making and motion planning levels and correlative algorithms. Such researches enable the autonomous system to compensate the human driver in dangerous or uncomfortable circumstance. This paper provides insights into the scope of decision-making and motion planning for cooperative driving, as well as the shortcomings and tendencies.

Predictive Motion Planning for Autonomous Vehicles With Geometric Constraints via Convex Optimization

2020 ◽  
Author(s):  
Xiaoyuan Zhu ◽  
Jian Chen ◽  
Yan Ma ◽  
Jianqiang Deng ◽  
Yuexuan Wang
Keyword(s):  
Decision Making ◽  
Motion Planning ◽  
Autonomous Vehicles ◽  
Linear Constraints ◽  
Geometric Constraints ◽  
Dynamic Constraints ◽  
The Road ◽  
Moving Obstacles ◽  
Planning Algorithm ◽  
Decision Module

Abstract In this paper, we propose an MPC-based motion planning algorithm, including a decision-making module, an obstacle-constraints generating module, and an MPC-based planning module. The designed decision module effectively distinguishes between structured and unstructured roads and processes them separately, so that the algorithm is more robust in different environments. Besides, the movement of obstacles is considered in the decision-making and obstacle constraints generating module. By processing obstacles with lateral and longitudinal speed separately, obstacle avoidance can be done in scenarios with moving obstacles, including moving obstacles crossing the road. Instead of treating the vehicle as a mass point, we explicitly consider the geometric constraints by modeling the vehicle as three intersecting circles when generating obstacle constraints. This ensures that the vehicle is collision-free in motion planning, especially when the vehicle turns. For non-convex obstacle constraints, we propose an algorithm that generates up to two alternative linear constraints to convexify the obstacle constraints for improving computational efficiency. In MPC, we consider the vehicle kino-dynamic constraints and two generated linear constraints. Therefore, the proposed method can achieve better real-time performance and can be applied to more complicated traffic scenarios with moving obstacles. Simulation results in three different scenarios show that motion planning can achieve satisfactory performance in both structured and unstructured roads with moving obstacles.

Modeling of an Object Manipulation Motion Planner and Grasping Rules

2013 ◽  
Vol 278-280 ◽  
pp. 664-672
Author(s):  
Pavel Dzitac ◽  
Md Mazid Abdul
Keyword(s):  
Decision Making ◽  
Motion Planning ◽  
Object Manipulation ◽  
Motion Controller ◽  
Robotic Grasping ◽  
Grasping And Manipulation ◽  
Planning Strategies

This paper presents the development of a Motion Planning Module for object manipulation, which is a part of previously developed robotic grasping and manipulation controller. The Motion Planning Module consists of a sensing processor, decision making module, instinctive controller, motion planner and a planned motion controller. Details related to the design and modelling of the motion planning module have been offered. Results of experiments on human grasping rule, suitable for the grasping and manipulation controller, have been discussed. The output of this research may be useful to those developing motion planning strategies for their grasping and manipulation controllers.

Intelligent Decision-Making and Motion Planning for Automated Vehicles

2021 ◽  
pp. 3-36
Author(s):  
Rick Voßwinkel ◽  
Maximilian Gerwien ◽  
Alexander Jungmann ◽  
Frank Schrödel
Keyword(s):  
Decision Making ◽  
Motion Planning ◽  
Automated Vehicles ◽  
Intelligent Decision Making ◽  
Intelligent Decision

Integrated Structural Damage Assessment, Motion Planning, and Decision-Making for Distressed Aircraft Under Uncertainty

2009 ◽  
Author(s):  
Israel Lopez ◽  
Nesrin Sarigul-Klijn
Keyword(s):  
Decision Making ◽  
Motion Planning ◽  
Trajectory Planning ◽  
Damage Assessment ◽  
Structural Damage ◽  
Kinematic Model ◽  
Evidence Theory ◽  
Target Position ◽  
Landing Site ◽  
Assessment Process

Aircraft navigation can be safely accomplished by properly addressing the following: decision-making, obstacle perception, aircraft state estimation, and aircraft control. To develop a monolithic navigational system is probably an impossible task; instead a hierarchical decomposition is presented, which breaks down the safe recovery and landing of distressed aircraft into sub-problems that maximize the probability that the overall objective is achieved. Navigational performance is often hinder by in-flight damage or failures, which often results in mission failure and an inability to guide the aircraft to a safe landing. Uncertainty is a very important concern in recovery of damaged aircraft since it can cause infeasibilities, false diagnosis and prognosis causing further performance degradation and mission failure. The damaged aircraft is simulated via a simplified kinematic model. The different sources and perspectives of uncertainties in the damage assessment process and post-failure trajectory planning are presented and classified. The decision-making process for an emergency motion planning and landing is developed via the Dempster-Shafer evidence theory. The objective of the trajectory planning is to arrive at a target position while maximizing the safety of the aircraft given uncertain conditions. Simulations are presented for an emergency motion planning and landing that takes into account aircraft dynamics, path complexity, distance to landing site, runway characteristics, and subjective human decision.

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