Hoang Huu Viet

BA*: an online complete coverage algorithm for cleaning robots

This paper presents a novel approach to solve the online complete coverage task of autonomous cleaning robots in unknown workspaces based on the boustrophedon motions and the A* search algorithm (BA*). In this approach, the robot performs a single boustrophedon motion to cover an unvisited region until it reaches a critical point. To continue covering the next unvisited region, the robot wisely detects backtracking points based on its accumulated knowledge, determines the best backtracking point as the starting point of the next boustrophedon motion, and applies an intelligent backtracking mechanism based on the proposed A* search with smoothed path on tiling so as to reach the starting point with the shortest collision-free path. The robot achieves complete coverage when no backtracking point is detected. Computer simulations and experiments in real workspaces prove that our proposed BA* is efficient for the complete coverage task of cleaning robots.

Simulation-Based Evaluations of Reinforcement Learning Algorithms for Autonomous Mobile Robot Path Planning

This work aims to evaluate the efficiency of the five fundamental reinforcement learning algorithms including Q-learning, Sarsa, Watkins’s Q(λ), Sarsa(λ), and Dyna-Q, and indicate which one is the most efficient of the five algorithms for the path planning problem of autonomous mobile robots. In the sense of the reinforcement learning algorithms, the Q-learning algorithm is the most popular and seems to be the most effective model-free algorithm for a learning robot. However, our experimental results show that the Dyna-Q algorithm, a method learns from the past model-learning and direct reinforcement learning is particularly efficient for this problem in a large environment of states.

Univector Field Method based Multi-Agent Navigation for Pursuit Problem

This paper presents a new approach to solve the pursuit problem based on a univector field method. In our proposed method, a set of eight agents works together instantaneously to find suitable moving directions and follow the univector field to pursue and capture a prey agent by surrounding it from eight directions in an infinite grid-world. In addition, a set of strategies is proposed to make the pursuit problem more realistic in the real world environment. This is a general approach, and it can be extended for an environment that contains static or moving obstacles. Experimental results show that our proposed algorithm is effective for the pursuit problem.

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.

B-Theta*: an Efficient Online Coverage Algorithm for Autonomous Cleaning Robots

We propose a novel approach to deal with the online complete-coverage task of cleaning robots in unknown workspaces with arbitrarily-shaped obstacles. Our approach is based on the boustrophedon motions, the boundary-following motions, and the Theta* algorithm known as B-Theta*. Under control of B-Theta*, the robot performs a single boustrophedon motion to cover an unvisited region. While performing the boustrophedon motion, if the robot encounters an obstacle with a boundary that has not yet been covered, it switches to the boundary mode to cover portions along the obstacle boundary, and then continues the boustrophedon motion until it detects an ending point. To move to an unvisited region, the robot detects backtracking points based on its accumulated knowledge, and applies an intelligent backtracking mechanism thanks to the proposed Theta* for multi-goals in order to reach the next starting point. Complete coverage is achieved when no starting point exists for a new boustrophedon motion. Computer simulations and experiments on real workspaces show that our proposed B-Theta* is efficient for the complete-coverage task of cleaning robots.

BoB: an online coverage approach for multi-robot systems

Online complete coverage is required in many applications, such as in floor cleaning, lawn mowing, mine hunting, and harvesting, and can be performed by single- or multi-robot systems. Motivated by the efficiency and robustness of multi-robot systems, this study proposes a solution to provide online complete coverage through a boustrophedon and backtracking mechanism called the BoB algorithm. This approach designs robots in the system according to a market-based approach. Without a central supervisor, the robots use only local interactions to coordinate and construct simultaneously non-overlapping regions in an incremental manner via boustrophedon motion. To achieve complete coverage, that is, the union of all covered regions in the entire accessible area of the workspace, each robot is equipped with an intelligent backtracking mechanism based on a proposed greedy A* search (GA*) to move to the closest unvisited region. The robots complete the coverage task when no more backtracking points are detected. Computer simulations show that the BoB approach is efficient in terms of the coverage rate, the length of the coverage path, and the balance of the workload distribution of robots.

Batch-Theta* for path planning to the best goal in a goal set

The development of 3D cameras and many navigation-supporting sensors has recently enabled robots to build their working maps and navigate accurately, making path planning popular not just on computer graphics, but in real environments as well. Pursuing the solutions for robot path planning, this paper presents a variant of searching method Theta* for choosing the best goal among given goals and the lowest-cost path to it, called Batch-Theta*. The novelty lies at the proposed line-of-sight checking function during the searching process and the manner that the method handles the batch of goals during one search instead of repeatedly considering a single goal or blindly doing the exhausted search. The analysis and simulations show that the proposed Batch-Theta* efficiently finds the lowest-cost path to the best goal in a given goal set under Theta*’s mechanism. Graphical Abstract

Q(λ) Based Vector Direction for Path Planning Problem of Autonomous Mobile Robots

This paper presents a novel algorithm to improve the efficiency of path planning for autonomous mobile robots. In an obstacle-free environment, the path planning of a robot is attained by following the vector direction from its current position to the goal position. In an obstacle environment, while following the vector direction, a robot has to avoid obstacles by rotating the moving direction. To accomplish the obstacle avoidance task for the mobile robot, the Q(λ) algorithm is employed to train the robot to learn suitable moving directions. Experimental results show that the proposed algorithm is soundness and completeness with a fast learning rate in the large environment of states and obstacles.

A Bidirectional Local Search for the Stable Marriage Problem

This paper proposes a bidirectional local search algorithm to find the egalitarian and the sex-equal stable matchings in the stable marriage problem. Our approach simultaneously searches forward from the man-optimal stable matching and backwards from the woman-optimal stable matching until the search frontiers meet. By employing a breakmarriage strategy to find stable neighbor matchings of the current stable matching and moving to the best neighbor matching, the forward local search finds the solutions while moving towards the woman-optimal stable matching and the backward local search finds the solutions while moving towards the man-optimal stable matching. Simulations show that our proposed algorithm is efficient for the stable marriage problem.

Offsetting obstacles of any shape for robot motion planning

We present an algorithm for offsetting the workspace obstacles of a circular robot. Our method has two major steps: It finds the raw offset curve for both lines and circular arcs, and then removes the global invalid loops to find the final offset. To generate the raw offset curve and remove global invalid loops, O(n) and O((n+k) log m ) computational times are needed respectively, where n is the number of vertices in the original polygon, k is the number of self-intersections and m is the number of segments in the raw offset curve, where m ≤ n . Any local invalid loops are removed before generating the raw offset curve by invoking a pair-wise intersection detection test (PIDT). In the PIDT, two intersecting entities are checked immediately after they are computed, and if the test is positive, portions of the intersecting segments are removed. Our method works for conventional polygons as well as the polygons that contain circular arcs. Our algorithm is simple and very fast, as each sub-process of the algorithm can be completed in linear time except the last one, which is nearly linear. Therefore, the overall complexity of the algorithm is nearly linear. By applying our simple and efficient approach, offsetting obstacles of any shape make it possible to construct a configuration space that ensures optimized motion planning.