The potential field algorithm for reactive robot control (original) (raw)
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Path-Planning for RTS Games Based on Potential Fields
2010
Many games, in particular RTS games, are populated by synthetic humanoid actors that act as autonomous agents. The navigation of these agents is yet a challenge if the problem involves finding a precise route in a virtual world (path-planning), and moving realistically according to its own personality, intentions and mood (motion planning). In this paper we present several complementary approaches recently developed by our group to produce quality paths, and to guide and interact with the navigation of autonomous agents. Our approach is based on a BVP Path Planner that generates potential fields through a differential equation whose gradient descent represents navigation routes. Resulting paths can deal with moving obstacles, are smooth, and free from local minima. In order to evaluate the algorithms, we implemented our path planner in a RTS game engine.
Navigation towards a goal position: from reactive to generalised learned control
Journal of Physics: Conference Series, 2011
The task of navigating to a target position in space is a fairly common task for a mobile robot. It is desirable that this task is performed even in previously unknown environments. One reactive architecture explored before addresses this challenge by dening a hand-coded coordination of primitive behaviours, encoded by the Potential Fields method. Our rst approach to improve the performance of this architecture adds a learning step to autonomously nd the best way to coordinate primitive behaviours with respect to an arbitrary performance criterion. Because of the limitations presented by the Potential Fields method, especially in relation to non-convex obstacles, we are investigating the use of Relational Reinforcement Learning as a method to not only learn to act in the current environment, but also to generalise prior knowledge to the current environment in order to achieve the goal more quickly in a non-convex structured environment. We show the results of our previous eorts in reaching goal positions along with our current research on generalised approaches.
ArXiv, 2016
An autonomous navigation with proven collision avoidance in unknown and dynamic environments is still a challenge, particularly when there are moving obstacles. A popular approach to collision avoidance in the face of moving obstacles is based on model predictive algorithms, which, however, may be computationally expensive. Hence, we adopt a reactive potential field approach here. At every cycle, the proposed approach requires only current robot states relative to the closest obstacle point to find the potential field in the current position; thus, it is more computationally efficient and more suitable to scale up for multiple agent scenarios. Our main contribution here is to write the reactive potential field based motion controller as a hybrid automaton, and then formally verify its safety using differential dynamic logic. In particular, we can guarantee a passive safety property, which means that collisions cannot occur if the robot is to blame, namely a collision can occur only ...
Reactive Planning Simulation in Dynamic Environments with VirtualRobot
Lecture Notes in Computer Science, 2004
This paper describes the architecture of a reactive planning system for dynamic environments, which is specifically designed to deal with robot planning problems. The architecture permits many agents to work simultaneously on the same environment and it is aimed at working with incomplete information. Agents have partial knowledge about the world and data soon becomes obsolete because of the changes in the environment. Our approach is designed to overcome this difficulty through a highly coupled system composed of an incremental planner and an executor. The whole system is integrated into VirtualRobot, a graphical software application, which provides a flexible and open platform to work on robotics. Through VirtualRobot we can incorporate important features into the system as simulation of sensing actions or a monitoring mechanism. Additionally, the planning algorithm is able to work in timelimited situations and use numeric variables. All these features make our planning system be a nice toolkit to deal with reactive robot planning.
Task space motion planning using reactive control
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
In this paper we present an approach to reduce the effort for planning robot motions by shifting the planning problem to a high-level representation. We combine classical sampling-based random tree planning with a reactive controller connecting sampling points with nontrivial trajectories, utilizing redundant DOFs to locally avoid obstacles. While the reactive planner operates locally on a short time scale, the complementary sampling-based method is able to find globally feasible solutions due to its larger preview horizon. Additionally, planning is done in a low-dimensional task space instead of the high-dimensional joint space. Comparing the average planning time and number of tree extensions for several scenarios and planning methods, we demonstrate that this hybrid planning approach is capable of solving a large fraction of planning queries while saving considerable planning time.
A Reactive Anticipation for Autonomous Robot Navigation
Serial and Parallel Robot Manipulators - Kinematics, Dynamics, Control and Optimization, 2012
Nowadays, mobile robots are expected to carry out various tasks in all kinds of application fields ranging from manufacturing plants, transportation, nursing service, resource or underwater exploration. In all these applications, robots should navigate autonomously in uncertain and dynamic environments in order to achieve their goals. So, the most current challenge in the development of autonomous robot control systems is making them respond intelligently to changing environments. Navigation in such environments involves many mechanisms such as: object detection, perception, internal building model, decision making, prediction of the future state of the environment and on-line navigation. To attend its goal safely, the robot should minimise interaction with other actors in order to avoid conflict situations. Generally, this problem comes up when many robots and/or actors would have access to the same space at the same time. In this case, the control of autonomous robotic navigation for conflict resolution has been widely studied. Some researchers have been focused on navigation in dynamic environments, where either reactive systems (producing real time behaviour), deliberative systems (introduce reasoning and need much more time to calculate a suitable decision) or hybrid systems (combine deliberative and reactive approaches) have been used in order to attend a known goal. Typically, reactive systems are used to deal with simple problems (detect an obstacle, go away from an obstacle, follow a wall, etc.). Nevertheless, reactive systems are typically less affected by errors and do not require an explicit model of the environment in order to navigate inside an unknown space. Furthermore, they usually deal only with local information that may be captured at real time. However, in reactive systems, the robot can be derived to a conflict situation with other actors because they ignore prediction and reasoning in the decision process. To face this problem, global planning approaches are used. They consist of elaborating a global plan from beginning state to goal state. These approaches need prior complete information about the state of the environment, so they do not take into account the environment uncertainty. So, in more complex situations, hybrid approaches are used. They combine reactive approaches according to a higher level in order to include anticipation of the state of the environment in the decision process. In these cases, low level control operates in a reactive way (local navigation) whereas high level systems tend to be deliberative. It provides, at each step, a partial moving plan to the robot. But these systems are complex because they need much more time to calculate or to update a suitable trajectory toward a predefined goal. In this situation, it is interesting to introduce prediction
Assessment and Review of the Reactive Mobile Robot Navigation
Al-Rafidain Engineering Journal (AREJ)
Nowadays, the mobile robot can be seen in different fields of engineering and science. The mobile robot can do some tasks that are so difficult or very risky to be performed by a human. Most of the works currently focus on implementing artificially intelligent algorithms and other algorithms that depend on the behaviour of nature. These approaches have been used in mobile robot navigation along uncertain manner. Mobile robot navigation strategies can be divided into two approaches: the classical approach and reactive approach. The classical approach related to static environment, whiles the reactive navigation is based on an unstructured environment. Path planning is one of the most important parts of the navigation system. In this paper, review and assessment of path planning strategies that can concern with the reactive approach are discussed, because it deal with the problem of dynamic environment. Numerous reactive methods have been introduced. Most of these presented works were concerned with simulation and a few of them have shown experimental implementation. Many papers tried to make a combination between two algorithms or more to increase the efficiency. It is concluded that reactive algorithms need more learning phases, complex in design, and require large memory storage.