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Dynamic on-road driving scenarios require robust methods for planning a safe and feasible vehicle motion coping with both static and dynamic obstacles. Many of the different approaches which have been proposed to tackle this challenge are based on optimal control and employ local continuous or discrete optimization schemes. While discrete methods possess the ability to find reasonable solutions in...
This paper presents a robust motion planning methodology for autonomous tracked vehicles navigating in a rough and unknown environment. Two fields of study are dealt with in this paper: motion planning and slip estimation. For the motion planner, the CC-RRT* algorithm is combined with LQG-MP. The motion planner uses a chance-constrained approach and considers the role of compensator in the planning...
We address the problem of verifying motion plans for aerial robots in uncertain and partially-known environments. Thereby, the initial state of the robot is uncertain due to errors from the state estimation and the motion is uncertain due to wind disturbances and control errors caused by sensor noise. Since the environment is perceived at runtime, the verification of partial motion plans must be performed...
A mathematical model of agent-based decision-making of Autonomous Surface Vehicles (ASVs) is presented with extensive path planning and abstraction capabilities of the environment. The aim is to enable the ASV to pass through heavy harbour traffic to an intended destination safely and efficiently. While predicting the future path of other vehicles and path planning can be carried out by known methods,...
Two ideas have gained traction in research in the robotics planning community. Activity planning has become popular where a library of predefined manipulation of the vehicle state is accessible, and is commonly used for missions with complex goal specifications. Another focus has been chance-constrained programming as a method of providing robust motion planning, in which the probability of failure...
This paper addresses the problem of large scale multi-agent motion planning in the presence of various uncertainties and under limited communication bandwidth. Obtaining an optimal solution while simultaneously addressing all the issues is a difficult problem. Towards this, we develop a decentralized motion planner that combines probabilistic approaches including the rapidly-exploring random tree...
The deployment of unmanned aerial vehicles (UAV) is increasingly commonplace. UAVs support military forces by flying over dangerous zones mainly for surveillance missions. Route planning for UAVs is therefore a critical problem. With many side constraints such as visitation time requirements, mission priorities, and vehicle capabilities, route planning is a hard problem. Heuristic approaches have...
We present a general notion of robust controller specification and a mechanism for sequentially composing them. These specifications form tubular abstractions of the trajectories of a system in different control modes, and are motivated by the techniques available for certifying the performance of low-level controllers. The notion of controller specification provides a rigorous interface for connecting...
Autonomous landing for Unmanned Aerial Vehicle is an important direction in the field of UAV research. Currently, relative GNC methods for aerial vehicle landing including GPS (Global Position System), INS (Inertial Navigation System) and ILS (instrument landing system) can't fully satisfy the requirements for UAV autonomous landing on the runway or un-cooperation environment. With the gradual progress...
This paper discusses opportunities to parallelize graph based path planning algorithms in a time varying environment. Parallel architectures have become commonplace, requiring algorithm to be parallelized for efficient execution. An additional focal point of this paper is the inclusion of inaccuracies in path planning as a result of forecast error variance, accuracy of calculation in the cost functions...
Past research projects on intelligent vehicles have already led to the development of a large number of Advanced Driver Assistance Systems. The current research focus is now shifting towards integration and adaptive automation systems that share the control between driver and the machine. Artificial co-drivers can be used for this scope, as tutors to provide holistic support to the driver. However...
This paper presents a novel and computationally inexpensive method for motion planning of autonomous parallel parking of four wheeled nonholonomic vehicles. The proposed method makes use of Trajectory Shaping Guidance, given in [1] and [2], which was originally developed for missiles to hit the target at a given angle. This paper uses a modified version of Trajectory Shaping Guidance (TSG) for path...
This paper presents a pseudospectral (PS) optimal control algorithm for the autonomous motion planning of a fleet of unmanned ground vehicles (UGVs). The UGVs must traverse an obstacle-cluttered environment while maintaining robustness against possible collisions. The generality of the algorithm comes from a binary logic that modifies the cost function for various motion planning modes. Typical scenarios...
In recent years, the Department of Defense and NASA have imposed significant resources on developing advanced launch technologies. Reusable launch vehicle (RLV) program is one of such efforts to take the place of space shuttles for RLVs. Our previous research proposed a scheme of motion primitives (MPs) and neighboring optimal control (NOC) to deal with on-line failure recovery for an RLV during approach...
This paper describes a general framework for the study of multiple vehicle, time-coordinated path following (TC-PF) control problems. An example is the situation where a group of vehicles is tasked to maneuver and arrive at pre-assigned final positions at the same time in a collision-free manner, while reducing some optimality criterion. The time of arrival is not fixed a priori, and the vehicles...
In this paper, a computationally effective trajectory generation algorithm of omnidirectional mobile robots is proposed. The algorithm plans a reference path based on Bezier curves, which meet obstacle avoidance criteria. Then the algorithm solves the problem of motion planning for the robot to track the path in a short travel time while satisfying dynamic constraints and robustness to noise. Accelerations...
The paper describes the software architecture of an autonomous mobile outdoor robot (AMOR). The robot is intended for robust operation in heavy terrain under arbitrary weather condition. The software is implemented in a layered architecture, possessing behaviour based and model based components. The multitude of different sensors is unified by a virtualisation layer, which allows easy reconfiguration...
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