A Reachability Tree-Based Algorithm for Robot Task and Motion PlanningPPT

AbstractIn recent years, there has been a growing interest in developing effi...

AbstractIn recent years, there has been a growing interest in developing efficient algorithms for task and motion planning in robotics. One of the key challenges in this field is to effectively generate feasible plans for robots to achieve complex tasks in dynamic environments. In this paper, we propose a reachability tree-based algorithm that addresses this challenge by efficiently searching the state space and generating feasible plans for robot task and motion planning.IntroductionTask and motion planning involves generating a sequence of robot actions and motions to achieve a desired task in a given environment. This is a complex problem due to the high-dimensional state space and the presence of dynamic obstacles. Traditional planning algorithms, such as A* and Dijkstra, often suffer from computational inefficiency when applied to robot task and motion planning.To overcome these challenges, researchers have proposed various algorithms based on sampling-based planners, such as Rapidly-exploring Random Trees (RRT) and Probabilistic RoadMaps (PRM). These algorithms are able to generate feasible plans by randomly sampling the state space and connecting the sampled states to form a graph representation of the environment. However, these algorithms still struggle with computational efficiency, especially in high-dimensional state spaces.The Reachability Tree-Based AlgorithmOur algorithm, the reachability tree-based algorithm, aims to overcome the computational inefficiency of existing planners by intelligently searching the state space and efficiently generating feasible plans. The key idea behind our algorithm is to construct a reachability tree that expands from an initial state to the goal state, while considering the dynamic constraints of the environment.The reachability tree is constructed by iteratively exploring the state space and expanding the tree towards the goal state. At each iteration, the algorithm generates a set of random samples from the state space and evaluates their reachability towards the goal state. The samples with high reachability are selected to expand the reachability tree, while the samples with low reachability are pruned. This sampling and evaluation process is repeated until a feasible plan connecting the initial state to the goal state is generated.To efficiently evaluate the reachability of each sample, our algorithm uses a combination of geometric and motion planning techniques. The geometric planner checks for collision-free paths between the sample and the goal state, while the motion planner determines the feasibility of executing the required motions to reach the goal state from the sample.Experimental ResultsWe conducted experiments to evaluate the performance of our reachability tree-based algorithm compared to other state-of-the-art algorithms. The results show that our algorithm achieves significantly better computational efficiency while maintaining a high success rate in generating feasible plans. It outperforms existing algorithms in terms of planning time and plan optimality in various simulated and real-world scenarios.ConclusionIn this paper, we proposed a reachability tree-based algorithm for robot task and motion planning. Our algorithm overcomes the computational inefficiency of existing planners by intelligently searching the state space and efficiently generating feasible plans. Experimental results demonstrate the superiority of our algorithm in terms of computational efficiency and plan optimality. Moving forward, we believe that our algorithm has the potential to significantly advance the field of robot task and motion planning, enabling robots to efficiently and effectively accomplish complex tasks in dynamic environments.