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This paper develops a path planner that minimizes risk (e.g. motionexecution) while maximizing accumulated reward (e.g., quality of sensorviewpoint) motivated by visual assistance or tracking scenarios in unstructuredor confined environments. In these scenarios, the robot should maintain thebest viewpoint as it moves to the goal. However, in unstructured or confinedenvironments, some paths may increase the risk of collision; therefore there isa tradeoff between risk and reward. Conventional state-dependent risk orprobabilistic uncertainty modeling do not consider path-level risk or isdifficult to acquire. This risk-reward planner explicitly represents risk as afunction of motion plans, i.e., paths. Without manual assignment of thenegative impact to the planner caused by risk, this planner takes in apre-established viewpoint quality map and plans target location and pathleading to it simultaneously, in order to maximize overall reward along theentire path while minimizing risk. Exact and approximate algorithms arepresented, whose solution is further demonstrated on a physical tethered aerialvehicle. Other than the visual assistance problem, the proposed framework alsoprovides a new planning paradigm to address minimum-risk planning underdynamical risk and absence of substructure optimality and to balance thetrade-off between reward and risk.

Explicit-risk-aware Path Planning with Reward Maximization