Path-guided artificial potential fields with stochastic reachable sets for motion planning in highly dynamic environments (original) (raw)

Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field

IEEE Transactions on Robotics, 2017

One of the primary challenges for autonomous robotics in uncertain and dynamic environments is planning and executing a collision free path. Hybrid dynamic obstacles present an even greater challenge as the obstacles can change dynamics without warning and potentially invalidate paths. Artificial Potential Field (APF) based techniques have shown great promise in successful path planning in highly dynamic environments due to their low cost at runtime. We utilize the APF framework for runtime planning but leverage a formal validation method, Stochastic Reachable (SR) sets, to generate accurate potential fields for moving obstacles. A small number of SR sets are computed a priori, then used to generate a potential field which represents the obstacle's stochastic motion for online path planning. Our method is novel and scales well with the number of obstacles, maintaining a relatively high probability of reaching the goal without collision, as compared to other traditional Gaussian APF methods. Here, we demonstrate our method with up to 900 hybrid dynamic obstacles and show that it outperforms the traditional Gaussian APF method by up to 60% in the holonomic case and up to 20% in the unicycle case.

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.

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.

A new APF strategy for path planning in environments with obstacles

Mechanism and Machine Theory, 2005

In this paper, a new general approach for the solution of the path planning problem has been developed in environments with obstacles. On the one hand, this strategy can be applied to mobile robots and on the other hand it can be used to solve the planning of high redundant multibody systems in their Configuration Space. The approach is based on the concept of Artificial Potential Fields (APF) usually associated both with the obstacles defined in the environment and with the target positions. Firstly, a new shape of APF has been arranged, established on a Potential Density, chosen inversely proportional to the Nth power of the distance. Hereafter, a brand new path planning strategy called ''quasi-geodesic method'' has been developed to avoid the obstacles. This method is valid for 2D and 3D environments, working the same manner whether the environment is static or dynamic.

Efficient robotic path planning algorithm based on artificial potential field

International Journal of Electrical and Computer Engineering (IJECE), 2021

Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

IEEE Robotics and Automation Letters

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on optimal motion planning has employed two main strategies to define a safe bound on an obstacle's space: using a polyhedron or a nonlinear differentiable surface. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.

Efficient and safe on-line motion planning in dynamic environments

2009 IEEE International Conference on Robotics and Automation, 2009

This paper presents a new on-line planner for dynamic environments that is based on the concept of Velocity Obstacles (VO). It addresses the issue of motion safety, i.e. avoiding states of inevitable collision, by selecting a proper time horizon for the velocity obstacle. The proper choice of the time horizon ensures that the boundary of the velocity obstacle coincides with the boundary of the set of inevitable collision states. 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. The planner generates a near-time optimal trajectory to the goal by selecting at each time step the velocity that minimizes the time-to-go and is out of the velocity obstacle. The planner takes into account the shape, velocity, and path curvature of the obstacle's trajectory. It is demonstrated for on-line motion planning in very crowded static and dynamic environments.

Path-Planning Solution Based on a Potential Field Non Uniformly Distributed

Robot Control 1991, 1992

Using robots in an environment with obstacles, implies a previous planning of all their ~ovements in .order to avoid possible collisions. To solve this problem, called path-planning. different solutions have been used: Slides, Octrees, Potential Field or V-graph. The methods based on the potential field present advantages because of the simplicity of the algorithm formulation and its implementation. Verbeek proposes the use of the "constrained distance transformation" to solve the path planing problem for a mobile robot in a two dimensional space. This solution can be extended to three or more DOF with a high cost-in time and memory. In this paper, we propose a new method the "self-adjustable filter", based on the convolution filtering concepts and using the octrees concepts to minimize the amount of the memory and the time employed. The method has been applied for a three DOF static robot, using the Cspace concepts in order to consider the possible collision with all the robot's arms. The results spends less time and memory, to obtain a solution with the same precision. This work is included in the EUREKA project "Advanced Mobile Robots for public safety applications". Keywords Robots; collision avoidance, path planning, potential field INTRODUcnON When a robot operates in an environment with obstacles it is necessary to make previous decisions about which trajectory the robot must move along. This problem known by path planning or more exactly by collision avoidance, has been studied by several authors using different methodologies.

Anytime dynamic path-planning with flexible probabilistic roadmaps

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006

Probabilistic roadmaps (PRM) have been demonstrated to be very promising for planning paths for robots with high degrees of freedom in complex 3D workspaces. In this paper we describe a PRM path-planning method presenting three novel features that are useful in various real-world applications. First, it handles zones in the robot workspace with different degrees of desirability. Given the random quality of paths that are calculated by traditional PRM approaches, this provides a mean to specify a sampling strategy that controls the search process to generate better paths by simply annotating regions in the free workspace with degrees of desirability. Second, our approach can efficiently re-compute paths in dynamic environments where obstacles and zones can change shape or move concurrently with the robot. Third, it can incrementally improve the quality of a generated path, so that a suboptimal solution is available when required for immediate action, but get improved as more planning time is affordable.