• 한국산업융합학회 논문집
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• 제18권4호
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• pp.191-196
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• 2015

In this paper, we present a path-smoothing algorithm for a robot arm manipulator that finds the path using a joint space-based rapidly-exploring random tree. Unlike other smoothing algorithms which require complex mathematical computation, the proposed path-smoothing algorithm is done using a Gaussian process. To find the optimal hyperparameters of the Gaussian process, we use differential evolution hybridized with opposition-based learning. The simulation result indicates that the Gaussian process whose hyperparameters were optimized by hybrid differential evolution successfully smoothed the path generated by the joint space-based rapidly-exploring random tree.

• 유통과학연구
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• 제13권6호
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• pp.11-15
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• 2015

Purpose - Robot path planning, a constrained optimization problem, has been an active research area with many methods developed to tackle it. This study proposes the use of a Rapidly-exploring Random Tree and Particle Swarm Optimizer algorithm for path planning. Research design, data, and methodology - The grid method is built to describe the working space of the mobile robot, then the Rapidly-exploring Random Tree algorithm is applied to obtain the global navigation path and the Particle Swarm Optimizer algorithm is adopted to obtain the best path. Results - Computer experiment results demonstrate that this novel algorithm can rapidly plan an optimal path in a cluttered environment. Successful obstacle avoidance is achieved, the model is robust, and performs reliably. The effectiveness and efficiency of the proposed algorithm is demonstrated through simulation studies. Conclusions - The findings could provide insights to the validity and practicability of the method. This method makes it is easy to build a model and meet real-time demand for mobile robot navigation with a simple algorithm, which results in a certain practical value for distribution environments.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1324-1342
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• 2020

Various modified algorithms of rapidly-exploring random tree (RRT) have been previously proposed. However, compared to the RRT algorithm for collision avoidance with global and static obstacles, it is not easy to find a collision avoidance and local path re-planning algorithm for dynamic obstacles based on the RRT algorithm. In this study, we propose boundary-RRT*, a novel-algorithm that can be applied to aerial vehicles for collision avoidance and path re-planning in a three-dimensional environment. The algorithm not only bounds the configuration space, but it also includes an implicit bias for the bounded configuration space. Therefore, it can create a path with a natural curvature without defining a bias function. Furthermore, the exploring space is reduced to a half-torus by combining it with simple right-of-way rules. When defining the distance as a cost, the proposed algorithm through numerical analysis shows that the standard deviation (σ) approaches 0 as the number of samples per unit time increases and the length of epsilon ε (maximum length of an edge in the tree) decreases. This means that a stable waypoint list can be generated using the proposed algorithm. Therefore, by increasing real-time performance through simple calculation and the boundary of the configuration space, the algorithm proved to be suitable for collision avoidance of aerial vehicles and replanning of local paths.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제2권12호
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• pp.829-840
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• 2013

본 논문은 Simulink/Stateflow 모델 기반의 테스트 케이스를 자동으로 생성하기 위하여, 휴리스틱 입력 분석을 이용한 Rapidly-exploring Random Tree(RRT) 기법을 제안한다. RRT는 모델 기반 블랙박스 테스트 케이스 생성 시 반드시 해결해야 되는 도달 가능성 문제를 효율적으로 해결할 수 있는 방법이지만, 모델의 내부 상태와 테스트 목표를 고려하지 않고 무작위로 모델의 입력을 생성하기 때문에 테스트 케이스 생성 효율이 떨어지는 단점이 있다. 제안하는 기법에서는 RRT를 확장해나갈 때 필요한 입력을, 모델의 현재 상태에서 만족 할 수 있는 테스트 목표를 분석하고 이를 달성할 수 있는 모델의 입력을 분석 결과에 따라 휴리스틱하게 결정함으로써, RRT의 장점을 보존하면서, 테스트 케이스 생성 효율을 높일 수 있다. 제안된 기법은 자동차에 사용되는 실 부품 ECU의 Simulink/Stateflow 모델을 대상으로 한 실험을 통해 성능이 평가되었으며, 기존 RRT와 비교하여 테스트 케이스 생성 효율이 높은 것을 보였다.

• ETRI Journal
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• 제40권4호
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• pp.471-482
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• 2018

This paper proposes the necessity of a walking period in footstep planning and details situations in which it should be considered. An optimization-based fast footstep planner that takes the walking period into consideration is also presented. This footstep planner comprises three stages. A binary search is first used to determine the walking period. The front stride, side stride, and walking direction are then determined using the modified rapidly-exploring random tree algorithm. Finally, particle swarm optimization (PSO) is performed to ensure feasibility without departing significantly from the results determined in the two stages. The parameters determined in the previous two stages are optimized together through the PSO. Fast footstep planning is essential for coping with dynamic obstacle environments; however, optimization techniques may require a large computation time. The two stages play an important role in limiting the search space in the PSO. This framework enables fast footstep planning without compromising on the benefits of a continuous optimization approach.

• 로봇학회논문지
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• 제7권2호
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• pp.150-159
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• 2012

In this paper, we propose a cost-aware Rapidly-exploring Random Tree (RRT) path planning algorithm for mobile robots. A mobile robot is presented with a cost map of the field of interest and assigned to move from one location to another. As a robot moves, the robot is penalized by the cost at its current location according to the cost map. The overall cost of the robot is determined by the trajectory of the robot. The goal of the proposed cost-aware RRT algorithm is to find a trajectory with the minimal cost. The cost map of the field can represent environmental parameters, such as temperature, humidity, chemical concentration, wireless signal strength, and stealthiness. For example, if the cost map represents packet drop rates at different locations, the minimum cost path between two locations is the path with the best possible communication, which is desirable when a robot operates under the environment with weak wireless signals. The proposed cost-aware RRT algorithm extends the basic RRT algorithm by considering the cost map when extending a motion segment. We show that the proposed algorithm gives an outstanding performance compared to the basic RRT method. We also demonstrate that the use of rejection sampling can give better results through extensive simulation.

• 제어로봇시스템학회논문지
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• 제21권3호
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• pp.257-264
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• 2015

This paper proposes an adaptive RRT (Rapidly-exploring Random Tree) for path planning of high DOF multibody robotic system. For an efficient path planning in high-dimensional configuration space, the proposed algorithm adaptively selects the robot bodies depending on the complexity of path planning. Then, the RRT grows only using the DOFs corresponding with the selected bodies. Since the RRT is extended in the configuration space with adaptive dimensionality, the RRT can grow in the lower dimensional configuration space. Thus the adaptive RRT method executes a faster path planning and smaller DOF for a robot. We implement our algorithm for path planning of 19 DOF robot, AMIRO. The results from our simulations show that the adaptive RRT-based path planner is more efficient than the basic RRT-based path planner.

• 로봇학회논문지
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• 제13권2호
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• pp.142-149
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• 2018

$RRT^*$ (Rapidly exploring Random $Tree^*$) based algorithms are widely used for path planning. Informed $RRT^*$ uses $RRT^*$ for generating an initial path and optimizes the path by limiting sampling regions to the area around the initial path. $RRT^*$ algorithms have several limitations such as slow convergence speed, large memory requirements, and difficulties in finding paths when narrow aisles or doors exist. In this paper, we propose an algorithm to deal with these problems. The proposed algorithm applies the image skeletonization to the gridmap image for generating an initial path. Because this initial path is close to the optimal cost path even in the complex environments, the cost can converge to the optimum more quickly in the proposed algorithm than in the conventional Informed $RRT^*$. Also, we can reduce the number of nodes and memory requirement. The performance of the proposed algorithm is verified by comparison with the conventional Informed $RRT^*$ and Informed $RRT^*$ using initial path generated by $A^*$.

• 한국군사과학기술학회지
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• 제15권4호
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• pp.424-434
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• 2012

This paper presents a path planning algorithm of the unmanned aerial vehicle for the neutralization of the enemy firepower. The long range firepower of the ememy is usually located at the rear side of the mountain which is difficult to bomb. The path planner not only consider the differential constraints of the Unmanned Aerial Vehicle (UAV) but also consider the final approaching angle constraint. This problem is easily solved by incorporating the analytical upper bounded continuous curvature path smoothing algorithm into the Rapidly Exploring Random Tree (RRT) planner. The proposed algorithm can build a feasible path satisfying the kinematic constraints of the UAV on the fly. In addition, the curvatures of the path are continuous over the whole path. Simulation results show that the proposed algorithm can generate a feasible path of the UAV for the bombing mission regardless of the posture of the tunnel.

• 로봇학회논문지
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• 제14권4호
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• pp.285-292
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• 2019

Path planning is an important problem to solve in robotics and there has been many related studies so far. In the previous research, we proposed the Heterogeneous-Ants-Based Path Planner (HAB-PP) for the global path planning of mobile robots. The conventional path planners using grid map had discrete state transitions that constrain the only movement of an agent to multiples of 45 degrees. The HAB-PP provided the smoother path using the heterogeneous ants unlike the conventional path planners based on Ant Colony Optimization (ACO) algorithm. The planner, however, has the problem that the optimization of the path once found is fast but it takes a lot of time to find the first path to the goal point. Also, the HAB-PP often falls into a local optimum solution. To solve these problems, this paper proposes an improved ant-inspired path planner using the Rapidly-exploring Random Tree-star ($RRT^*$). The key ideas are to use $RRT^*$ as the characteristic of another heterogeneous ant and to share the information for the found path through the pheromone field. The comparative simulations with several scenarios verify the performance of the improved HAB-PP.