• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.317-328
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• 2000

This paper presents an effective approach to collision-free motion planning of a robot in the work-space including time-varying obstacles. The free arc is defined as a set composed of the configuration points of the robot satisfying collision-free motion constraint at each sampling time. We represent this free arc with respect to the new coordinate frame centered at the goal configuration and there for the collision-free path satisfying motion constraint is obtained by connecting the configuration points of the free arc at each sampling mined from the sequence of free arcs the optimality is determined by the performance index. Therefore the complicated collision-free motion planning problem of a robot is transformed to a simplified SUB_Optimal Collision Avoidance Problem(SOCAP). We analyze the completeness of the proposed approach and show that it is partly guaranteed using the backward motion. Computational complexity of our approach is analyzed theoretically and practical computation time is compared with that of the other method. Simulation results for two cally and practical computation time is compared with that of the other method. Simulation results for two SCARA robot manipulators are presented to verify the efficacy of the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.629-634
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• 1995

The purpose of this paper is to develop a method of Collision-Free Path Planning (CFPP) for an articulated robot. First, the configuration of the robot is formed by a set of robot joint angles derived fromm robot inverse kinematics. The joint space that is made of the joint angle set, forms a Configuration space (Cspace). Obstacles in the robot workcell are also transformed and mapped into the Cspace, which makes Cobstacles in the Cspace. (The Cobstacles represented in the Cspace is actually the configurations of the robot causing collision.) Secondly, a connected graph, a kind of roadmap, is constructed from the free configurations in the 3 dimensional Cspace, where the configurations are randomly sampled form the free Cspace. Thirdly, robot paths are optimally in order to minimize of the sum of joint angle movements. A path searching algorithm based on A is employed in determining the paths. Finally, the whole procedures for the CFPP method are illustrated with a 3 axis articulated robot. The main characteristics of the method are; 1) it deals with CFPP for an articulated robot in a 3-dimensional workcell, 2) it guarantees finding a collision free path, if such a path exists, 3) it provides distance optimization in terms of joint angle movements. The whole procedures are implemented by C on an IBM compatible 486 PC. GL (Graphic Library) on an IRIS CAD workstation is utilized to produce fine graphic outputs.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.990-995
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• 1990

A minimum-time trajectory planning for two robot arms with designated paths and coordination is proposed. The problem considered in this paper is a subproblem of hierarchically decomposed trajectory planning approach for multiple robots : i) path planning, ii) coordination planning, iii) velocity planning. In coordination planning stage, coordination space, a specific form of configuration space, is constructed to determine collision region and collision-free region, and a collision-free coordination curve (CFCC) passing collision-free region is selected. In velocity planning stage, normal dynamic equations of the robots, described by joint angles, velocities and accelerations, are converted into simpler forms which are described by traveling distance along collision-free coordination curve. By utilizing maximum allowable torques and joint velocity limits, admissible range of velocity and acceleration along CFCC is derived, and a minimum-time velocity planning is calculated in phase plane. Also the planning algorithm itself is converted to simple numerical iterative calculation form based on the concept of neural optimization network, which gives a feasible approximate solution to this planning problem. To show the usefulness of proposed method, an example of trajectory planning for 2 SCARA type robots in common workspace is illustrated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.519-522
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• 1997

In this study, the collision free path planning method of the articulated manipulator using sequential search is proposed. This method is to find the joint path of the manipulator with many degrees of freedom from the distal joint to the proximal one. To do this, the initial work space of the gantry manipulator, which is a remote maintenance equipment of the radioactive environment, is defined from the condition that the distal joint configuration is determined by the posture of maintenance. Then, 2-dimensional configuration space with the obstacle area is represented and the collision free path of manipulator is searched in the configuration space. And, this method is verified using the graphic simulation in virtual workcell for the spent fuel disassembling processes. The result of this study can be effectively used in implementing the maintenance processes for the hot cell equipment and enhance the reliability of the spent fuel management.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.545-550
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• 1991

Recently, the use of robots for painting operations has received much attention, as it is a powerful means for automation and quality improvement. Collision-avoidance is a key issue in the path planning for painting operations. In this paper, we develop a computationally efficient algorithm for the generation of collision-free path for two types of motion: a) Gross motion when the robot approaches the painting area, and b) Fine motion while spraying the surface. The former is a typical collision-avoidance problem, but the latter calls for special attention as the painting mechanics has to be incorporated into path planning. The developed algorithm is applied for the internal coating of the car body whose structure is the major source of collision.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.32-38
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• 1999

An autonomous mobile vehicle for transport system must plan optimal path in work envimnrent without human supervision and obstacle collision. This is to reach a destination without getting lost. In this paper, a genetic algorithm for globaI and local path planning and collision avoidance is proposed. The genetic algorithm searches for a path in the entire and continuous free space and unifies global path planning and local path planning. The sinmulation shows the proposed method is an efficient and effective method when compared with the traditional collision avoidance algorithms.rithms.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.164-164
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• 1999

An autonomous mobile vehicle for transport system must plan optimal path in work environment without human supervision and obstacle collision. This is to reach a destination without getting lost. In this paper, a genetic algorithm for global and local path planning and collision avoidance is proposed. The genetic algorithm searches for a path in the entire and continuous free space and unifies global path planning and local path planning. The simulation shows the proposed method is an efficient and effective method when compared with the traditional collision avoidance algorithms.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.211-217
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• 2014

This paper addresses an algorithm of path planning for autonomous driving of a ground vehicle in waypoint navigation. The proposed algorithm is flexible in utilization under a large GPS positioning error and generates collision-free multiple paths while pursuing minimum traveling time. An optimal path reduces inefficient steering by minimizing lateral changes in generated waypoints along a path. Simulation results compare the proposed algorithm with the A* algorithm by manipulation of the steering wheel and traveling time, and show that the proposed algorithm realizes real-time obstacle avoidance by quick processing of path generation, and minimum time traveling by producing paths with small lateral changes while overcoming the very irregular positioning error from the GPS.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.363-370
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• 2022

Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.