Results 1 -
5 of
5
USV TRAJECTORY PLANNING FOR TIME VARYING MOTION GOALS IN AN ENVIRONMENT WITH OBSTACLES
"... Safe and efficient following of a time varying motion goal by an autonomous unmanned surface vehicle (USV) in a sea environment with obstacles is a challenge. The vehicle’s tracking capability is inherently influenced by its dynamics, the motion characteristics of the motion goal, as well as by the ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
(Show Context)
Safe and efficient following of a time varying motion goal by an autonomous unmanned surface vehicle (USV) in a sea environment with obstacles is a challenge. The vehicle’s tracking capability is inherently influenced by its dynamics, the motion characteristics of the motion goal, as well as by the configuration of obstacles in the marine environment. We have developed an approach that utilizes a lattice-based trajectory planning to generate a dynamically feasible, resolution optimal, collision-free trajectory to allow the vehicle to reliably reach the motion goal. We utilized a trajectory following controller to achieve high tracking efficiency while still preserving motion safety. The entire approach is based on the developed USV system architecture that encapsulates the necessary trajectory planning components. We demonstrated the effectiveness of the developed planner in a simulated environment with static obstacles. In addition, we have developed a physical evaluation setup.
Stackelberg-based Coverage Approach in Nonconvex Environments
"... This paper introduces StaCo: Stackelberg-based Coverage approach for nonconvex environments. This approach struc-turally differs from existing methods to cover a nonconvex environment, as it is based on a game-theoretic concept of Stackelberg games. Our key assumption is that one robot can predict ( ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
(Show Context)
This paper introduces StaCo: Stackelberg-based Coverage approach for nonconvex environments. This approach struc-turally differs from existing methods to cover a nonconvex environment, as it is based on a game-theoretic concept of Stackelberg games. Our key assumption is that one robot can predict (short-term) behavior of other robots. No direct com-munication takes place among the robots, the approach is de-centralized. However, the leading robot can direct the system into the optimal setting much more efficiently just by chang-ing its own position. This paper extends our previous work in which we have introduced the StaCo approach for coverage of a convex environment, with a simpler type of robots. We pro-vide theoretical foundations of the approach. We demonstrate its benefits by means of case studies (using the Sim.I.am soft-ware). We show situations in which the StaCo approach out-performs the standard approach, which is based on combina-tion of the Lloyd algorithm and path planning.
Generating Strategies for Multi-Agent Pursuit-Evasion Games in Partially Observable Euclidean Space
"... Abstract. We introduce a heuristic search technique for multi-agent pursuit-evasion games in partially observable Euclidean space where a team of tracker agents attempt to minimize their uncertainty about an evasive target agent. Agents ’ movement and observation capabilities are restricted by polyg ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Abstract. We introduce a heuristic search technique for multi-agent pursuit-evasion games in partially observable Euclidean space where a team of tracker agents attempt to minimize their uncertainty about an evasive target agent. Agents ’ movement and observation capabilities are restricted by polygonal obstacles, while each agents ’ knowledge of the other agents is limited to direct observation or periodic updates from team members. Our polynomial-time algorithm is able to generate strategies for games in continuous two-dimensional Euclidean space, an improvement over past algorithms that were only applicable to simple gridworld domains. We show experimentally that our algorithm is tolerant of interruptions in communication between agents, continuing to generate good strategies despite long periods of time where agents are unable to communicate directly. Experimental results also show that our technique generates effective strategies quickly, with decision times of less than a second for reasonably sized domains with six or more agents.
StaCo: Stackelberg-based coverage approach in robotic swarms
- In Proceedings of ADAPTIVE 2013
, 2013
"... ..."
(Show Context)
Methods, and Search
"... We investigate algorithms for playing multi-agent visibilitybased pursuit-evasion games. A team of pursuers attempts to maintain visibility contact with an evader who actively avoids tracking. We aim for applicability of the algorithms in real-world scenarios; hence, we impose hard constraints on th ..."
Abstract
- Add to MetaCart
(Show Context)
We investigate algorithms for playing multi-agent visibilitybased pursuit-evasion games. A team of pursuers attempts to maintain visibility contact with an evader who actively avoids tracking. We aim for applicability of the algorithms in real-world scenarios; hence, we impose hard constraints on the run-time of the algorithms and we evaluate them in a simulation model based on a real-world urban area. We compare Monte-Carlo tree search (MCTS) and iterative deepening minimax algorithms running on the informationset tree of the imperfect-information game. The experimental results demonstrate that both methods create comparable good strategies for the pursuer, while the later performs better in creating the evader’s strategy.
