Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field (original) (raw)
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2015 IEEE International Conference on Robotics and Automation (ICRA), 2015
Highly dynamic environments pose a particular challenge for motion planning due to the need for constant evaluation or validation of plans. However, due to the wide range of applications, an algorithm to safely plan in the presence of moving obstacles is required. In this paper, we propose a novel technique that provides computationally efficient planning solutions in environments with static obstacles and several dynamic obstacles with stochastic motions. Path-Guided APF-SR works by first applying a sampling-based technique to identify a valid, collision-free path in the presence of static obstacles. Then, an artificial potential field planning method is used to safely navigate through the moving obstacles using the path as an attractive intermediate goal bias. In order to improve the safety of the artificial potential field, repulsive potential fields around moving obstacles are calculated with stochastic reachable sets, a method previously shown to significantly improve planning success in highly dynamic environments. We show that Path-Guided APF-SR outperforms other methods that have high planning success in environments with 300 stochastically moving obstacles. Furthermore, planning is achievable in environments in which previously developed methods have failed.
RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles
2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)
We present here a framework to integrate into a motion planning method the interaction zones of a moving robot with its future surroundings, the reachable interaction sets. It can handle highly dynamic scenarios when combined with path planning methods optimized for quasi-static environments. As a demonstrator, it is integrated here with an artificial potential field reactive method and with a Bézier curve path planning. Experimental evaluations show that this approach significantly improves dynamic path planning methods, especially when the speeds of the obstacles are higher than the one of the robot. The presented approach is used together with a global planning approach in order to handle complex static environments in presence of fast-moving obstacles. When the ego vehicle is not holonomic the presented approach is able to take dynamic constraints into account, which improve the prediction accuracy.
Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns
Autonomous Robots, 2013
This paper presents a real-time path planning algorithm that guarantees probabilistic feasibility for autonomous robots with uncertain dynamics operating amidst one or more dynamic obstacles with uncertain motion patterns. Planning safe trajectories under such conditions requires both accurate prediction and proper integration of future obstacle behavior within the planner. Given that available observation data is limited, the motion model must provide generalizable predictions that satisfy dynamic and environmental constraints, a limitation of existing approaches. This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation. Obstacle trajectory GP predictions are conditioned on dynamically feasible paths identified from the reachability analysis, yielding more accurate predictions of future behavior. RR-GP predictions are integrated with a robust path planner, using chance-constrained RRT, to identify probabilistically feasible paths. Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented. Simulations demonstrate that, with this planner, an autonomous vehicle can safely navigate a complex environment in realtime while significantly reducing the risk of collisions with dynamic obstacles.
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 ...
Computation of forward stochastic reach sets: Application to stochastic, dynamic obstacle avoidance
2017 American Control Conference (ACC), 2017
We propose a method to efficiently compute the forward stochastic reach (FSR) set and its probability measure for nonlinear systems with an affine disturbance input, that is stochastic and bounded. This method is applicable to systems with an a priori known controller, or to uncontrolled systems, and often arises in problems in obstacle avoidance in mobile robotics. When used as a constraint in finite horizon controller synthesis, the FSR set and its probability measure facilitate probabilistic collision avoidance, in contrast to methods which presume the obstacles act in a worst-case fashion, and generate hard constraints that cannot be violated. We tailor our approach to accommodate rigid body constraints, and show convexity is assured so long as the rigid body shape of each obstacle is also convex. We extend methods for multi-obstacle avoidance through mixed integer linear programming (with linear robot and obstacle dynamics) to accommodate chance constraints that represent the FSR set probability measure. We demonstrate our method on a rigid-body obstacle avoidance scenario, in which a receding horizon controller is designed to avoid several stochastically moving obstacles while reaching a desired goal. Our approach can provide solutions when approaches that presume a worst-case action from the obstacle fail.
Adaptive motion planning with artificial potential fields using a prior path
2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM), 2015
Motion planning in an autonomous agent is responsible for providing smooth, safe and efficient navigation. Many solutions for dealing this problem have been offered, one of which is, Artificial Potential Fields (APF). APF is a simple and computationally low cost method which keeps the robot away from the obstacles in environment. However, this approach suffers from trapping in local minima of potential function and then fails to produce motion plans. Furthermore, Oscillation in presence of obstacles or in narrow passages is another disadvantage of the method which makes it unqualified for many planning problems. In this paper we aim to resolve these deficiencies by a novel approach which employs a prior path between origin and goal configuration of the robot. Therefore, the planner guarantees to lead the robot to goal area while the inherent advantages of potential fields remain. For path planning stage, we intend to use randomized sampling methods such as Rapidly-exploring Random Trees (RRT) or its derivatives, however, any path planning approach can be utilized. We have also designed an optimization procedure for evolving the motion plans towards optimal solution. Then genetic algorithm is applied to find smoother, safer and shorter plans. In our experiments, we apply a simulated vehicle in Webots simulator to test and evaluate the motion planner. Our experiments showed our method to enjoy improving the performance and speed in comparison to basic approaches.
Mapping Obstacles to Collision States for On-line Motion Planning in Dynamic Environments
This paper presents a representation of static and moving obstacles, using Velocity Obstacles (VO), for on-line planning in dynamic environments. Each obstacle is mapped to forbidden states by selecting a proper time horizon for the velocity obstacle. The proper choice of the time horizon ensures that the boundary of the mapped obstacle overlaps with the boundary of the set of inevitable collision states (ICS). This time horizon is determined by the minimum time it would take the robot to avoid collision, either by stopping or by passing the respective obstacle. This representation allows safe on-line planning using only one step look ahead. The on-line trajectories favorably compare with the trajectories obtained by a global planner.
Path Planning with Real Time Obstacle Avoidance
International Journal of Computer Applications, 2013
One of the most important areas of research on mobile robots is that of their moving from one point in a space to the other and that too keeping aloof from the different objects in their path i.e. real time obstacle detection. This is the basic problem of path planning through a continuous domain. For this a large number of algorithms have been proposed of which only a few are really good as far as local and global path planning are concerned as to some extent a trade of has to be made between the efficiency of the algorithm and its accuracy. In this project an integrated approach for both local as well as global path planning of a robot has been proposed. The primary algorithm that has been used for path planning is the artificial Potential field approach [1] and a* search algorithm has been used for finding the most optimum path for the robot. Obstacle detection for collision avoidance (a high level planning problem) can be effectively done by doing complex robot operations in real time and distributing the whole problem between different control levels. This project proposes the artificial potential field algorithm not only for static but also for moving obstacles using real time potential field values by creating sub-goals which eventually lead to the main goal of the most optimal complete path found by the A* search algorithm. Apart from these scan line and convex hull techniques have been used to improve the robustness of the algorithm. To some extent the shape and size of a robot has also been taken into consideration. The effectiveness of these algorithms has been verified with a series of simulations.
IEEE/RSJ International Conference on Intelligent Robots and System, 2002
Whenever robots are installed in populated environments, they need appropriate techniques to avoid collisions with unexpected obstacles. Over the past years several reactive techniques have been developed that use heuristic evaluation functions to choose appropriate actions whenever a robot encounters an unforeseen obstacle. Whereas the majority of these approaches determines only the next steering command, some additionally consider sequences of possible poses. However, they generally do not consider sequences of actions in the velocity space. Accordingly, these methods are not able to slow down the robot early enough before it has to enter a narrow passage. In this paper we present a new approach that integrates path planning with sensor-based collision avoidance. Our algorithm simultaneously considers the robot's pose and velocities during the planning process. We employ different strategies to deal with the huge state space that has to be explored. Our method has been implemented and tested on real robots and in simulation runs. Extensive experiments demonstrate that our technique can reliably control mobile robots moving at high speeds.
On-line Planning for Collision Avoidance on the Nominal Path
1998
In this paper a solution to the obstacle avoidance problem for a mobile robot moving in the two-dimensional Cartesian plane is presented. The robot is modelled as a linear timeinvariant dynamic system of finite size enclosed by a circle and the obstacles are modelled as circles travelling along rectilinear trajectories. This work deals with the avoidance problem when the obstacles move in known trajectories. The robot starts its journey on a nominal straight line path with a nominal velocity. When an obstacle is detected to be on a collision course with the robot, the robot must devise a plan to avoid the obstacle whilst minimising a cost index defined as the total sum squared of the magnitudes of the deviations of its velocity from the nominal velocity. The planning strategy adopted here is adjustment of the robot's velocity on the nominal path based on the time of collision between the robot and a moving obstacle, and determination of a desired final state such that its Euclidean distance from the nominal final state is minimal. Obstacle avoidance by deviation from the nominal path in deterministic and random environments is based on the work presented here and is investigated in another paper.