NAVIGATION AMONG MOVABLE OBSTACLES: REAL-TIME REASONING IN COMPLEX ENVIRONMENTS

2005 ◽  
Vol 02 (04) ◽  
pp. 479-503 ◽  
Author(s):  
MIKE STILMAN ◽  
JAMES J. KUFFNER
Keyword(s):  
Mobile Robots ◽  
State Space ◽  
Real Time ◽  
Heuristic Search ◽  
Free Space ◽  
Single Agent ◽  
Complex Environments ◽  
Moving Obstacles ◽  
Large Numbers ◽  
Object Domain

In this paper, we address the problem of Navigation Among Movable Obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path. This paper presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multi-object domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.

scholarly journals Scrubbing During Learning In Real-time Heuristic Search

2016 ◽  
Vol 57 ◽  
pp. 307-343 ◽  
Author(s):  
Nathan R. Sturtevant ◽  
Vadim Bulitko
Keyword(s):  
State Space ◽  
Real Time ◽  
Lower Bounds ◽  
Heuristic Search ◽  
Search Algorithm ◽  
The State ◽  
Worst Case ◽  
Heuristic Search Algorithm ◽  
Practical Performance ◽  
Theoretical Results

Real-time agent-centered heuristic search is a well-studied problem where an agent that can only reason locally about the world must travel to a goal location using bounded computation and memory at each step. Many algorithms have been proposed for this problem and theoretical results have also been derived for the worst-case performance with simple examples demonstrating worst-case performance in practice. Lower bounds, however, have not been widely studied. In this paper we study best-case performance more generally and derive theoretical lower bounds for reaching the goal using LRTA*, a canonical example of a real-time agent-centered heuristic search algorithm. The results show that, given some reasonable restrictions on the state space and the heuristic function, the number of steps an LRTA*-like algorithm requires to reach the goal will grow asymptotically faster than the state space, resulting in ``scrubbing'' where the agent repeatedly visits the same state. We then show that while the asymptotic analysis does not hold for more complex real-time search algorithms, experimental results suggest that it is still descriptive of practical performance.

Real-time obstacle avoidance for multiple mobile robots

Robotica ◽  
2009 ◽  
Vol 27 (2) ◽  
pp. 189-198 ◽  
Author(s):  
Farbod Fahimi ◽  
C. Nataraj ◽  
Hashem Ashrafiuon
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Panel Method ◽  
The Other ◽  
Harmonic Potential ◽  
Current Position ◽  
Multiple Mobile Robots ◽  
Moving Obstacles ◽  
Harmonic Potential Field

SUMMARYAn efficient, simple, and practical real time path planning method for multiple mobile robots in dynamic environments is introduced. Harmonic potential functions are utilized along with the panel method known in fluid mechanics. First, a complement to the traditional panel method is introduced to generate a more effective harmonic potential field for obstacle avoidance in dynamically changing environments. Second, a group of mobile robots working in an environment containing stationary and moving obstacles is considered. Each robot is assigned to move from its current position to a goal position. The group is not forced to maintain a formation during the motion. Every robot considers the other robots of the group as moving obstacles and hence the physical dimensions of the robots are also taken into account. The path of each robot is planned based on the changing position of the other robots and the position of stationary and moving obstacles. Finally, the effectiveness of the scheme is shown by modeling an arbitrary number of mobile robots and the theory is validated by several computer simulations and hardware experiments.

scholarly journals Distributional Metareasoning for Heuristic Search

2021 ◽  
Author(s):  
Tianyi Gu
Keyword(s):  
State Space ◽  
Real Time ◽  
Real World ◽  
Heuristic Search ◽  
Time Pressure ◽  
Autonomous Systems ◽  
Optimal Solutions ◽  
Algorithmic Approach ◽  
Heuristic Search Methods ◽  
Computational Resources

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this work, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.

Method for Detecting Moving Obstacles Using Passive 3D Visual Sensor

1993 ◽  
Vol 5 (4) ◽  
pp. 388-400
Author(s):  
Jun'ichi Takeno ◽  
◽  
Naoto Mizuguchi ◽  
Sakae Nishiyama ◽  
Kanehiro Sorimachi ◽  
...  
Keyword(s):  
Visual System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Visual Sensor ◽  
Real Time Processing ◽  
Moving Obstacles ◽  
The Right ◽  
Moving Obstacle ◽  
Auto Focusing

Of primary importance for mobile robots is their smooth movement to the targeted destination. To achieve this purpose, mobile robots must be able to detect a person in their environment, another mobile robot, or an object not described in the map and to avoid collision with it. Recognizing the strong need for providing robots with a visual system to evade obstacles, the authors first developed a real-time visual system to detect a moving obstacle and then studied the possibility of avoiding collisions by mounting the system on a mobile robot. The visual sensor used in this system is a passive optical stereo without any mechanical moving parts. Using a special slit patten, the sensor is configured in order to split the two images obtained by individual cameras place on the right and left and to project the split images onto one CCD sensor, providing approximately 200 auto-focusing subsystems. The sub-systems can operate independently of one another, enabling real-time processing. This paper reports on a visual sensor, a solution to the measurement accuracy problem concerning the detection of moving obstacles using the sensor, and visual system experiments on real-time detection of an actually moving object using the sensor.

Recognizing Moving Obstacles for Robot Navigation using Real-time Omnidirectional Stereo Vision

2002 ◽  
Vol 14 (2) ◽  
pp. 147-156 ◽  
Author(s):  
Hiroshi Koyasu ◽  
◽  
Jun Miura ◽  
Yoshiaki Shirai
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Free Space ◽  
Dynamic Environment ◽  
Range Data ◽  
Moving Obstacles ◽  
Map Generation ◽  
On Line ◽  
Time Required ◽  
Omnidirectional Stereo Vision

We describe a method to recognize moving obstacles in a wide view and in real time required when a mobile robot moves in a dynamic environment. Our method uses an omnidirectional stereo vision composed of a pair of vertically-aligned omnidirectional cameras and a PC cluster to obtain panoramic range information of 360 degrees in real time. From this range information, the robot on-line generates a free space map of the surrounding environment, and extracts objects in free space as candidates for moving obstacles. The robot makes time correspondence of candidates and estimates their position and velocity using the Kalman filter. To reduce the effect of odometry error to map generation, egomotion is estimated by comparing current and previous range data. We demonstrate the effectiveness of our method by on-line experiments.

Sign in / Sign up

Export Citation Format

Share Document