Robust obstacle avoidance in unknown and cramped environments

Feiten, Wendelin; Bauer, Rudolf; Lawitzky, Gisbert

doi:10.1109/robot.1994.351150

Cited by 57 publications

(28 citation statements)

References 11 publications

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“…These ideas have been extended in outdoor systems in which the robot projects candidate paths ahead of itself and then chooses among the corresponding steering actions the one that makes the most progress towards the goal and is obstacle free [6]. If no collision-free steering angle can move the robot towards a goal location, a higher level planner is consulted.…”

Section: Related Workmentioning

confidence: 99%

Learning to Drive Among Obstacles

Hamner

Scherer

Singh

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper reports on an outdoor mobile robot that learns to avoid collisions by observing a human driver operate a vehicle equipped with sensors that continuously produce a map of the local environment. We have implemented steering control that models human behavior in trying to avoid obstacles while trying to follow a desired path. Here we present the formulation for this control system and its independent parameters, and then show how these parameters can be automatically estimated by observation of a human driver. We present results from experiments with a vehicle (both real and simulated) that avoids obstacles while following a prescribed path at speeds up to 4 m/sec. We compare the proposed method with another method based on Principal Component Analysis, a commonly used learning technique. We find that the proposed method generalizes well and is capable of learning from a small number of examples.

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Section: Related Workmentioning

confidence: 99%

Learning to Drive Among Obstacles

Hamner

Scherer

Singh

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…The commands are directions of motion [28,21], sets of speeds [29,30], or sets of trajectories [31,32]. However, these reactive methods are of limited use when the scenario makes it difficult to maneuver the vehicle (usually with high obstacle density) [33], identified some consequences like local trap situations, irregular and oscillating motions, or the impossibility of driving the vehicle towards areas with a high obstacle density or far away from the goal direction.…”

Section: Related Workmentioning

confidence: 99%

Sensor-based robot motion generation in unknown, dynamic and troublesome scenarios

Mínguez

Montano

2005

Robotics and Autonomous Systems

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A sensor-based motion control system was designed to autonomously drive vehicles free of collisions in unknown, troublesome and dynamic scenarios. The system was developed based on a hybrid architecture with three layers (modeling, planning and reaction). The interaction of the modules was based on a synchronous planner-reactor configuration where the planner computes tactical information to direct the reactivity. Our system differs from previous ones in terms of the choice of the techniques implemented in the modules and in the integration architecture. It can achieve robust and reliable navigation in difficult scenarios that are troublesome for many existing methods. Experiments carried out in these scenarios with a real vehicle confirm the effectiveness of this technique.

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“…Similarly, the methods based on polar histograms [6], [26], [27] present the difficulty to navigate among close obstacles due to the tuning of a empirical threshold that has to be modified when the obstacle density changes. Traps due to the U-shape obstacles are not avoided by the methods that use constrained optimizations [11], [23], [10]. This is because the optimization loses the information of the environment structure necessary to solve these situations.…”

Section: Introductionmentioning

confidence: 99%

“…[12], [14], [25], [4], [3], [16]), and in those that solve the problem with a constrained optimization (e.g. [11], [23], [2], [10]) one of the balance terms is the goal heading. Thus, with these methods, directions of motion far away from the goal direction are difficult to obtain (in all the situations where they are required).…”

Section: Introductionmentioning

confidence: 99%

A "divide and conquer" strategy based on situations to achieve reactive collision avoidance in troublesome scenarios

Mínguez

Osuna

Montano

2004

IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004

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Abstract-This paper addresses the reactive collision avoidance for robots that move in arduous environments (i.e. very dense, complex and cluttered). To achieve this goal, the technique simplifies the difficulty of the navigation by a divide and conquer strategy, which is based on identifying navigational situations and applying the corresponding motion laws. The state of the art in reactive navigation still presents classic limitations such as the trap situations due to U-shape obstacles, the difficulty to achieve motion among very close obstacles, to obtain oscillation-free and stable motion, to move over directions far from the goal direction or towards the obstacles, or to tune the heuristic or internal parameters. This paper presents a method that overcomes all these limitations. As a result, navigation with this method is successfully achieved in scenarios where existing techniques present a high degree of difficulty to navigate. Outstanding navigation results are reported using a wheelchair vehicle and a discussion and comparison with existing techniques is provided. I. INTRODUCTIONCurrently, applications such as emergency response, transportation, deminning, exploration, help care or ludic robots yield the motion control to autonomous navigation systems. The mobile devices, provided with these capabilities, greatly improve their performance and the security of the potential occupants or transported objects. Thus in mobile robotics, we are carrying out a vast effort to improve the robustness of the basic skills of the mobility task, since it is clear that they have a large impact over the whole performance of the system. The navigation systems work over the vehicles with full autonomy or supervising the human operation. A good example is a robotic wheelchair, which must support both functionalities: complete autonomy (in high degrees of human disability) or supervising the operator orders (in lower degrees of disability). Furthermore, this vehicle requires motion in unstructured, unknown and dynamic scenarios with a high density of obstacles (e.g. moving among chairs, door traversal, humans moving around etc). The navigation performance is crucial for the normal development of this task (human transportation). This paper focuses on the motion generation aspect, which is the particular relevance here because collisions put in risk the safety of the occupant and sudden motions lead to an uncomfortable behavior. We present in this paper a technique able to achieve this objective.Classically, the motion planning problem is solved by computing a geometrical trajectory avoiding known obstacles (see [15] for a review of techniques). However, the general methods for motion planning are not applicable if the environment is dynamic with a priori unknown behavior, or if it is gradually discovered. Moreover, when both the environment model and

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Robust obstacle avoidance in unknown and cramped environments

Cited by 57 publications

References 11 publications

Learning to Drive Among Obstacles

Learning to Drive Among Obstacles

Sensor-based robot motion generation in unknown, dynamic and troublesome scenarios

A "divide and conquer" strategy based on situations to achieve reactive collision avoidance in troublesome scenarios

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