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Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.


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TL;DR: In this paper, a library of "funnels" along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed is computed.
Abstract: We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real-time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Our approach is to pre-compute a library of "funnels" along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed. We leverage powerful computational machinery from convex optimization (sums-of-squares programming in particular) to compute these funnels. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. We demonstrate and validate our method using extensive hardware experiments on a small fixed-wing airplane avoiding obstacles at high speed (~12 mph), along with thorough simulation experiments of ground vehicle and quadrotor models navigating through cluttered environments. To our knowledge, these demonstrations constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real-time in environments with complex geometric constraints.

160 citations

Posted Content
TL;DR: The approach to solving the motion-planning problem in mobile robot control using neural networks-based technique and the method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks.
Abstract: This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the "free" space using ultrasound range finder data. The second neural network "finds" a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

160 citations

Proceedings ArticleDOI
10 Jun 1997
TL;DR: In this article, a GA-based approach has been proposed for path planning and trajectory planning of an autonomous mobile robot, which has an advantage of adaptivity such that the GA work even if an environment is time-varying or unknown.
Abstract: This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and online motion planning. We first present a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are presented. Then, we discuss extensions of the GA for solving both path planning and trajectory planning simultaneously.

160 citations

Journal ArticleDOI
TL;DR: The behavior-programming control concept is suggested for networked mobile robot systems to avoid disturbances of the Internet latency and the event-driven concept is applied on the robot to switch the behaviors to accommodate the unpredicted mission autonomously.
Abstract: We review the networked mobile robot systems and suggest taxonomy based on the three levels of control commands. The performance analysis result shows that direct control has potential difficulty for implementation due to the unpredicted transmission delay of the network. To tackle this problem, we have suggested the behavior-programming control concept to avoid disturbances of the Internet latency. For this purpose, primitive local intelligence of the mobile robot is grouped into motion planner, motion executor, and motion assistant, where each of a group is treated as an agent. They are integrated by centralized control architecture based on multi-agent concept, communicated through a center information memory. The event-driven concept is applied on the robot to switch the behaviors to accommodate the unpredicted mission autonomously. We have successfully demonstrated experimentally the feasibility and reliability for system through a performance comparison with direct remote control.

160 citations

Journal ArticleDOI
01 Jun 2009
TL;DR: A novel approximate cell-decomposition method in which obstacles, targets, sensor's platform, and FOV are represented as closed and bounded subsets of an Euclidean workspace, and these strategies outperform shortest path, complete coverage, random, and grid search strategies.
Abstract: A methodology is developed for planning the sensing strategy of a robotic sensor deployed for the purpose of classifying multiple fixed targets located in an obstacle-populated workspace. Existing path planning techniques are not directly applicable to robots whose primary objective is to gather sensor measurements using a bounded field of view (FOV). This paper develops a novel approximate cell-decomposition method in which obstacles, targets, sensor's platform, and FOV are represented as closed and bounded subsets of an Euclidean workspace. The method constructs a connectivity graph with observation cells that is pruned and transformed into a decision tree from which an optimal sensing strategy can be computed. The effectiveness of the optimal sensing strategies obtained by this methodology is demonstrated through a mine-hunting application. Numerical experiments show that these strategies outperform shortest path, complete coverage, random, and grid search strategies, and are applicable to nonoverpass capable robots that must avoid targets as well as obstacles.

159 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,512
20223,388
20212,138
20202,668
20192,648
20182,266