We present two planners, Randward and Lamar, which are respectively built upon the Downward and LAMA planners. The technique developed in these planners is to randomize heuristic construction so that heuristic plateaus are less frequent and the bias introduced by poor, arbitrary nondeterministic choices in heuristic construction is removed. Specifically, we randomize the FF heuristic, naming th...

This paper presents a randomized motion planner for kinodynamic asteroid avoidanceproblems, in which a robot must avoid collision with moving obstacles under kinematic, dynamic constraints and reach a specified goal state. Inspired by probabilistic-roadmap (PRM) techniques, the planner samples the state time space of a robot by picking control inputs at random in order to compute a roadmap that...

It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims optimized with high accuracy efficiency. A two-step path strategy proposed. The classic problem divided into two simpler problems: reduction problems a solving weighted directed graphs. original first reduced using an RRT (Rapid...

This paper proposes a truly interactive virtual environment (VE) system for 2D assembly tasks at the microscale. It is based on the application of virtual potential fields as a control aid for performing safe and reliable path planning strategies. The planner covers a whole range of problems due to microscale effects in object assignment, obstacle detection and avoidance, path trajectory findin...

One of the most successful algorithms in the last few years for solving classical planning problems is Graphplan [3]. This algorithm can be seen as a disjunctive version of forward state space planners. The algorithm has two interleaved phases: a forward phase where a polynomial-time data structure called ”planning graph” is incrementally extended, and a backward phase where that planning graph...

Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial evaluating gradient magnitude and direction. Nevertheless, computing such field has much redundancy since the trajectory optimization procedure only covers very limited subspace of ESDF updating range. In this letter, an ESDF-free gradient-based planning framework p...

Stan is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. Stan competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, nding solutions fastest to many of the problems posed. Although the domain analysis techniques Stan exploits are an important factor in its overall pe...

This paper proposes AlphaRoute, an AlphaGo inspired algorithm for coordinating large-scale routes, built upon graph attention reinforcement learning and Monte Carlo Tree Search (MCTS). We first partition the road network into regions model coordinated route planning as a Markov game, where each partitioned region is treated player instead of driver. Then, AlphaRoute applies bilevel optimization...

Stan is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. Stan competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, nding solutions fastest to many of the problems posed. Although the domain analysis techniques Stan exploits are an important factor in its overall pe...

Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for planning. sbp-env focuses on the flexibility of tinkering with aspects framework, and had divided main planning components into two categories (i) samplers (ii) planners. The focus research been mainly improving sampling efficiency (with methods such ...