Safe mobile robot motion planning for waypoint sequences in a dynamic environment
23 Apr 2013-pp 181-186
TL;DR: A novel graph structure based on a state × time × goal lattice with hybrid dimensionality which allows the joint planning for multiple goals while incorporating collision risk due to dynamic and static obstacles and computes hybrid solutions which are part trajectory and part path.
Abstract: Safe and efficient path planning for mobile robots in dynamic environments is still a challenging research topic. Most approaches use separate algorithms for global path planning and local obstacle avoidance. Furthermore, planning a path for a sequence of goals is mostly done by planning to each next goal individually. These two strategies generally result in sub-optimal navigation strategies. In this paper, we present an algorithm which addresses these problems in a single combined approach. For this purpose, we model the static and dynamic risk of the environment in a consistent way and propose a novel graph structure based on a state × time × goal lattice with hybrid dimensionality. It allows the joint planning for multiple goals while incorporating collision risk due to dynamic and static obstacles. It computes hybrid solutions which are part trajectory and part path. Finally, we provide some results of our algorithm in action to prove its high quality solutions and real-time capability.
Citations
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12 Jun 2016
TL;DR: The mobile robot is introduced, some universal path planning algorithms of the mobile robot are reviewed, then a fuzzy logic and filter smoothing based on the data from the laser scan sensor is proposed, and the path planning and autonomous obstacle avoidance based on this algorithm is emphasized.
Abstract: The mobile robot has been one of the hottest fields in recent years, it has been widely applied in the most critical and efficient occasions in the industry, meanwhile, path planning becomes a frontier and interesting research topic for the mobile robot. In this paper, firstly mobile robot is introduced, some universal path planning algorithms of the mobile robot are reviewed, then a fuzzy logic and filter smoothing based on the data from the laser scan sensor is proposed, the path planning and autonomous obstacle avoidance based on this algorithm is emphasized because it can automatically find the best path according to the size and position of gaps between the obstacles in the dynamic environment, finally our designed mobile robot and corresponding Android APP are introduced, the path planning and obstacle avoidance algorithms are tested on this mobile robot, the testing results show this algorithm has the advantage of responding quickly, global optimized and hardware resource saved compared with other algorithms, it is suitable for the mobile robot that is designed on the embedded system and it can meet our designed requirement.
19 citations
Cites background from "Safe mobile robot motion planning f..."
...The early algorithm is based on the static environment that the location of the obstacles is fixed and finding out a best path for the mobile robot to reach the destination, such as A* principle which is an assessment system for the cost from the initial point to the destination point [9]-[11]....
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TL;DR: A new algorithm to obtain the accurate moving direction of the mobile robot where the GPS and corresponding sensors are unavailable is proposed and the corresponding real experiment is carried out to prove the effectiveness of the algorithm in the laboratory dynamic environment.
Abstract: This paper proposes a navigation algorithm for the mobile robot which can save lots of running distances, running time and mechanical loss of the mobile robot, usually the real mobile robot is established on the embedded system and works in the indoor and dynamic environment, so this paper proposes a new algorithm to obtain the accurate moving direction of the mobile robot where the GPS and corresponding sensors are unavailable, it also proposes a navigation algorithm for the mobile robot in the dynamic environment based on the PF-SLAM algorithm which combines the Particle Filter and FAST-SLAM algorithm, they are very suitable running on the embedded system because it avoids the large complex computation, finally the simulation of the proposed algorithm is shown in the paper, the corresponding real experiment is carried out to prove the effectiveness of the algorithm for the mobile robot in our laboratory dynamic environment.
19 citations
Cites background from "Safe mobile robot motion planning f..."
...9, according to the Table 1, because the destination is landmark D which is in the 330–30 of the mobile robot and there is an obstacle ahead at this time, the mobile robot will turn left until there is no obstacles ahead and it will move along the CO direction [11]....
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TL;DR: A novel algorithm is presented that uses a hybrid-dimensional multi-resolution state × time lattice to efficiently compute trajectories with an adaptive fidelity according to the environmental requirements to safe and efficient path planning for mobile robots in large dynamic environments.
18 citations
Cites background or methods from "Safe mobile robot motion planning f..."
...In recent work (Petereit et al., 2013) we extended this approach in order to always guarantee at least kinematic feasibility and allow for a consistent modeling of dynamic and static obstacles....
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...In our implementation, we use the approach presented in Petereit et al. (2013), that models the collision risk due to dynamic obstacles in a probabilistic way based on the predicted trajectory of the dynamic obstacles....
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...This process is described in detail in Petereit et al. (2013), however, for the sake of completeness, we restate a short outline of the procedure below....
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15 Sep 2015
TL;DR: This paper names several risks with iterative optimization of vehicle trajectories modeled through the calculus of variations, as used recently in several approaches to automated driving, and demonstrates how these can be addressed by global optimization, while retaining the original, variational optimization goals.
Abstract: This paper names several risks with iterative optimization of vehicle trajectories modeled through the calculus of variations, as used recently in several approaches to automated driving, and demonstrates how these can be addressed by global optimization, while retaining the original, variational optimization goals. A method for transforming variational methods into Hidden Markov Models is applied to multilane scenarios, such as highways or larger city streets. A particular side-effect of this method is the capability of providing a fail-safe emergency trajectory at almost no further computational effort. The proposed global optimization is demonstrated on several real-world situations and compared to a ground truth of human maneuvers.
11 citations
Additional excerpts
...The calculus of variations (cf. [7]) provides a model (here Euler–Lagrange model or ELM for short) from the physical dynamics of particles that uses a functional P of the form P[ξ] = ∫ t2 t1 dt (ξ(t), ξ̇(t), ξ̈(t), ..., d ω /(dt)ωξ(t), t) (1) to assign a total penalty P to a given trajectory ξ…...
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TL;DR: Experimental results are presented for a UAV navigating to a waypoint in a cellular SOP environment, where the MOMP strategy successfully reaches the waypoint, while the naive strategy fails to do so.
Abstract: Navigation of an unmanned aerial vehicle (UAV) to reach a desired waypoint in global navigation satellite system (GNSS)-denied environments is considered. The UAV is assumed to have an unknown initial state and the environment is assumed to possess multiple terrestrial signals of opportunity (SOPs) transmitters with unknown states and one anchor SOP whose states are known. The UAV makes pseudorange measurements to all SOPs to estimate its own states simultaneously with the states of the unknown SOPs. The problem is formulated as a multi-objective motion planning (MOMP) strategy, which guarantees the UAV gets to within a user-specified distance of the waypoint with a user-specified confidence. The MOMP strategy balances two objectives: (i) navigating to the waypoint and (ii) reducing UAVs position estimate uncertainty. It is demonstrated that in such an environment, formulating the waypoint navigation problem in a so-called naive fashion by heading directly to the waypoint results in failing to reach the waypoint due to poor estimability of the environment. In contrast, the MOMP strategy guarantees (in a probabilistic sense) reaching the waypoint. Simulation and experimental results are presented showing that the MOMP strategy achieves the desired objective while the naive strategy fails to do so.
10 citations
Cites background from "Safe mobile robot motion planning f..."
...The waypoint navigation problem has been formulated as an optimal control problem [39], which could be solved by linear quadratic regulator (LQR) [40], genetic algorithm [41], or a combination of local and global planning [42]....
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References
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01 Aug 1996
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Abstract: A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
4,977 citations
TL;DR: In this paper, the authors presented the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design), where the task is to determine control inputs to drive a robot from an unknown position to an unknown target.
Abstract: This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an ...
2,993 citations
"Safe mobile robot motion planning f..." refers background in this paper
...In the past, especially probabilistic approaches like RRTs [3] and PRMs [4] have become established for high-dimensional planning problems....
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TL;DR: The algorithm, which is based on dimensionality reduction and partial Voronoi diagram construction, can be used for computing the DT for a wide class of distance functions, including the L/sub p/ and chamfer metrics.
Abstract: A sequential algorithm is presented for computing the exact Euclidean distance transform (DT) of a k-dimensional binary image in time linear in the total number of voxels N. The algorithm, which is based on dimensionality reduction and partial Voronoi diagram construction, can be used for computing the DT for a wide class of distance functions, including the L/sub p/ and chamfer metrics. At each dimension level, the DT is computed by constructing the intersection of the Voronoi diagram whose sites are the feature voxels with each row of the image. This construction is performed efficiently by using the DT in the next lower dimension. The correctness and linear time complexity are demonstrated analytically and verified experimentally. The algorithm may be of practical value since it is relatively simple and easy to implement and it is relatively fast (not only does it run in O(N) time but the time constant is small). A simple modification of the algorithm computes the weighted Euclidean DT, which is useful for images with anisotropic voxel dimensions. A parallel version of the algorithm runs in O(N/p) time with p processors.
907 citations
"Safe mobile robot motion planning f..." refers methods in this paper
...For this purpose, we first compute the Euclidean distance transform of the grid map which represents the environment using the algorithm described in [10]....
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TL;DR: A detailed analysis of the planner's convergence rate shows that, if the state×time space satisfies a geometric property called expansiveness, then a slightly idealized version of the implemented planner is guaranteed to find a trajectory when one exists, with probability quickly converging to 1, as the number of milestones increases.
Abstract: This paper presents a novel randomized motion planner for robots that must achieve a specified goal under kinematic and/or dynamic motion constraints while avoiding collision with moving obstacles with known trajectories. The planner encodes the motion constraints on the robot with a control system and samples the robot's state × time space by picking control inputs at random and integrating its equations of motion. The result is a probabilistic roadmap of sampled state ×time points, called milestones, connected by short admissible trajectories. The planner does not precompute the roadmap; instead, for each planning query, it generates a new roadmap to connect an initial and a goal state×time point. The paper presents a detailed analysis of the planner's convergence rate. It shows that, if the state×time space satisfies a geometric property called expansiveness, then a slightly idealized version of our implemented planner is guaranteed to find a trajectory when one exists, with probability quickly converg...
815 citations
"Safe mobile robot motion planning f..." refers background in this paper
...To cope with moving obstacles, PRMs have been extended to state× time spaces as well [8] but still suffer from only probabilistic guarantees....
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Proceedings Article•
09 Dec 2003TL;DR: An anytime heuristic search, ARA*, is proposed, which tunes its performance bound based on available search time, and starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows.
Abstract: In real world planning problems, time for deliberation is often limited. Anytime planners are well suited for these problems: they find a feasible solution quickly and then continually work on improving it until time runs out. In this paper we propose an anytime heuristic search, ARA*, which tunes its performance bound based on available search time. It starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows. Given enough time it finds a provably optimal solution. While improving its bound, ARA* reuses previous search efforts and, as a result, is significantly more efficient than other anytime search methods. In addition to our theoretical analysis, we demonstrate the practical utility of ARA* with experiments on a simulated robot kinematic arm and a dynamic path planning problem for an outdoor rover.
711 citations
"Safe mobile robot motion planning f..." refers methods in this paper
...For our implementation of the presented algorithm, we have chosen to use the ARA* algorithm (Anytime Repairing A*, [15])....
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