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Path planning of an autonomous vehicle as a non-holonomic system is an essential part for many automated driving applications. Parking a car into a parking lot and maneuvering it through a narrow corridor would be a common driving scenarios in an urban environment. In this study a hybrid approach for dynamic path planning is presented which deals with the limited degrees of freedom of a non-holonomic...
Define an environment as a set of convex constraint functions that vary arbitrarily over time and consider a cost function that is also convex and arbitrarily varying. Agents that operate in this environment intend to select actions that are feasible for all times while minimizing the cost's time average. Such action is said optimal and can be computed offline if the cost and the environment are known...
In the Multi-Policy Decision Making (MPDM) framework, a robot's policy is elected by sampling from the distribution of current states, predicting future outcomes through forward simulation, and selecting the policy with the best expected performance. Electing the best plan depends on sampling initial conditions with influential (very high costs) outcomes. Discovering these configurations through random...
As robots make their way into our everyday lives, new behavioral concepts are needed to assure their acceptance as interaction partners. In the presence of humans, robots are required to take safety as well as human comfort into account. This paper presents a novel, planning-based approach for social robot navigation. It uses predicted human trajectories and a social cost function to plan collision-free...
In our previous work [1] we introduced the Anticipative Kinodynamic Planning (AKP): a robot navigation algorithm in dynamic urban environments that seeks to minimize its disruption to nearby pedestrians. In the present paper, we maintain all the advantages of the AKP, and we overcome the previous limitations by presenting novel contributions to our approach. Firstly, we present a multi-objective cost...
It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations,...
This paper proposes a novel model-free inverse reinforcement learning method based on density ratio estimation under the framework of Dynamic Policy Programming. We show that the logarithm of the ratio between the optimal policy and the baseline policy is represented by the state-dependent cost and the value function. Our proposal is to use density ratio estimation methods to estimate the density...
Humans are more successful in planning collision free, continuous trajectories through populated environments than any motion planning algorithm so far. This is due to the fact that they consider the conditionally cooperative, interactive behavior of the surrounding persons, for example the possibility of mutual avoidance maneuvers. In this paper, interaction during navigation is regarded from a game...
In navigation tasks, mobile robots often have to deal with substantial uncertainty due to imperfect actuators and noisy sensor measurements. In this paper, we consider the problem of online trajectory generation for safe navigation in the presence of state uncertainty and the resulting deviations from the desired trajectory. Our approach combines probabilistic estimation of the a priori collision...
In mobile robot navigation, cost functions are a popular approach to generate feasible, safe paths that avoid obstacles and that allow the robot to get from its starting position to the goal position. Alternative ways to navigate around the obstacles typically correspond to different local minima in the cost function. In this paper we present a highly effective approach to overcome such local minima...
In order to act socially compliant with humans, mobile robots need to show several behaviors that require the prediction of people's motion. For example, when a robot avoids a person, it needs to respect the human's personal space [1] and the avoidance behavior needs to be smooth, so that it is understandable to the interaction partner. To achieve this, the robot needs to reason about future paths...
In the near future, robots will exist in our home, office, hospital, school and all other public places. In other words, robots have to work and share environments with human. However, there exist many invisible social rules or social protocols in human society. For example, people usually stand in a line to access certain services. It seems that there is a social force or a “spatial effect” which...
Receding horizon control strategies have proven effective in many control and robotic applications. These methods simulate the state a certain time horizon into the future to choose the optimal trajectory. However, in many cases, such as in mobile robot navigation, the selection of an appropriate time horizon is important as too long of a time horizon can amplify the detrimental effects caused by...
This paper presents a new navigation method for mobile robots, based on direct kinematics model and predictive control approach. It is adaptable to all types of robots taking into account their specific constraints. The research of the optimal trajectory is shifted in the continuous parameters space, which enables the exploitation of all of the robot's capabilities. The use of a stochastic algorithm...
This paper is concerned with the navigation of personal robots in human-populated environments. The behavior of a person among its peers is governed by a number of unspoken social rules, e.g. maintaining an appropriate distance. The primary contribution of this paper is a navigation scheme that is anthropomorphic, i.e. that emulates human behaviors and seeks to adhere to these social rules. Unlike...
Path-tracking by using MPC (model predictive control) is a suitable control science solution for mobile robot navigation applications. Online MPC is reported by using short-term horizons that allow dealing with flexible path-tracking and reactive behaviors. The majority of MPC experimental research developed is based on the fact that the reference trajectory is known beforehand. However, under dynamic...
This paper presents a few important practice-oriented requirements for optimal path planning for the AUV “SLOCUM Glider” as well as solutions using fast graph based algorithms. These algorithms build upon the TVE (time-varying environment) search algorithm. The experience with this algorithm, requirements of real missions along the Newfoundland and Labrador Shelf and the idea to find the optimal departure...
Based on the dynamic window approach (DWA) for robot navigation, this paper presents a local reactive method, called DWA*, for mobile robots to achieve high-speed, smooth, and local-minima-free navigation. The original DWA utilizes only a small part of environmental information to search for a proper motion command in the robot's motion space. Hence, the robot can be easily driven into local-minima...
The analysis of global and local navigation methods for an autonomous mobile robot allowed to select the main lacks of existent methods of navigation. The improved local navigation method based on the use of potential fields for movement taking into account the gradient of direction to the goal is proposed. Also the round of blocking obstacles is foreseen in a method. That allows to the mobile robot...
This paper presents an algorithm for real-time sensor-based motion planning under kinodynamic constraints, in unknown environments. The objective of the trajectory-generation algorithm is to optimise a cost function out to a limited time horizon. The space of control trajectories is searched by expanding a tree using randomised sampling, in a manner similar to an RRT. The algorithm is improved by...
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